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Large-scale automotive battery cell manufacturing: Analyzing strategic and
operational effects on manufacturing costs
Article in International Journal of Production Economics · November 2020
DOI: 10.1016/j.ijpe.2020.107982
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Int. J. Production Economics 232 (2021) 107982
Contents lists available at ScienceDirect
International Journal of Production Economics
journal homepage: http://www.elsevier.com/locate/ijpe
Large-scale automotive battery cell manufacturing: Analyzing strategic and
operational effects on manufacturing costs
Fabian Duffner a, b, *, Lukas Mauler a, b, Marc Wentker a, Jens Leker a, c, Martin Winter c, d
a
Institute of Business Administration at the Department of Chemistry and Pharmacy (IfbM), University of Münster, 48148, Münster, Germany
Porsche Consulting GmbH, 74321, Bietigheim-Bissingen, Germany
c
Helmholtz-Institute Münster (HIMS), 48149, Münster, Germany
d
MEET Battery Research Center, Institute of Physical Chemistry University of Münster, 48149, Münster, Germany
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Battery
Automotive
Manufacturing
Cost
Optimization
Process-based cost modelling
Cost-efficient battery cell manufacturing is a topic of intense discussion in both industry and academia, as battery
costs are crucial for the market success of electrical vehicles (EVs). Based on forecasted EV growth rates, battery
cell manufacturers are investing billions of dollars in new battery cell plants. Whether these billion-dollar in­
vestments are economically viable depends on the materialization of forecasted EV growth rates and companyspecific competitive market positions. For both, cost-efficient battery cell manufacturing is key for success. To
ensure cost-efficient battery cell manufacturing, transparency is necessary regarding overall manufacturing costs,
their cost drivers, and the monetary value of potential cost reductions. Driven by these requirements, a cost
model for a large-scale battery cell factory is developed. The model relies on the process-based cost modelling
technique (PBCM) and includes more than 250 parameters. Based on this cost model, directions are provided,
how minimum costs can be achieved reflecting current and future state of technology. Further, it is outlined
which process steps and cost elements have the greatest impact on total cost and should thus be focused within
future cost reduction activities.
1. Introduction
The battery manufacturing industry is forecast to be one of the fastest
growing production industries through 2030. Especially driven by the
expanded production of electrical vehicles (EVs) with the overall goal of
minimizing vehicular CO2 and NO2 emissions, annual global lithium-ion
battery capacity demand is expected to increase from 160 GWh cell
energy in 2018 to >1000 GWh cell energy in 2030. By 2030, this in­
crease will have triggered investments of more than $100 billion and
generate annual revenue of more than $100 billion within battery
manufacturing industry (Avicenne Energy, 2019). To transform these
investments into sustainable business models, cost efficient battery
manufacturing is the key factor as it is the prerequisite to make EVs
competitive compared to internal combustion engine vehicles (Pollet
et al., 2012; Sierzchula et al., 2014; Wu et al., 2015).
The need to produce cost-efficient batteries, the launch of the first
mass-market EVs (e.g. Tesla Model 3), and initial investments worth
several billion dollars for the first battery-cell factories (e.g. Tesla’s
Gigafactory) have made battery-cell cost optimization relevant for both
science and industry. Triggered by this, some optimizations have
already been achieved, mainly based on new materials and innovative
cell chemistries (Liu et al., 2010; Placke et al., 2017; Scrosati and Gar­
che, 2010; Wagner et al., 2013; Winter et al., 2018). Although it has
received less attention, battery cell manufacturing has also improved,
with notable results in the last two decades. However, due to numerous
consecutive process steps, the interaction of these steps and the high
number of individual process parameters, it can be assumed that there is
potential for further optimization. The realization of this potential will
require a deep understanding of the individual production process steps,
process parameters, and their impact on cost (Kwade et al., 2018).
With regard to costs, cost models with extended capabilities to
analyze cost drivers and to simulate a large number of parameters can be
the key for success to achieve the necessary transparency (Qian and
Ben-arieh, 2008). Driven by this requirement, numerous studies have
dealt with the modelling of battery costs (Berg et al., 2015; Nelson et al.,
2019; Petri et al., 2015; Schmuch et al., 2018; Vaalma et al., 2018;
* Corresponding author. Institute of Business Administration at the Department of Chemistry and Pharmacy (IfbM), University of Münster, 48148, Münster,
Germany.
E-mail address: duffnerfa@gmail.com (F. Duffner).
https://doi.org/10.1016/j.ijpe.2020.107982
Received 30 March 2020; Received in revised form 11 August 2020; Accepted 3 November 2020
Available online 11 November 2020
0925-5273/© 2020 The Authors.
Published by Elsevier B.V. This is an open
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
access
article
under
the
CC
BY-NC-ND
license
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
Fig. 1. Process steps for the manufacture of a lithium-ion pouch battery cell in a large-scale factory.
Wentker et al., 2019). However, most of these models focus on material
using surcharge rates to forecast processing costs (Duffner et al., 2020b).
While such approaches are suitable to forecast rough costs for a new
product with less effort, their capabilities to translate new innovations
comprehensively into costs are limited (Niazi et al., 2006).
Addressing these limitations, within this paper, a model is presented
that can translate the academic discussion related to process in­
novations, material & design-innovations, and location alternatives into
costs. Therefore, first, cost estimation techniques are reviewed to eval­
uate suitability for the presented requirements. Finding that bottom-up
techniques and especially the process-based cost modelling technique
fits best, a model for battery manufacturing relying on more than 250
parameters is proposed. Based on this model, cost driver analysis within
process steps, cost elements and parameter categories is provided.
Further, a current state and a future cost level is introduced by trans­
lating the associated parameter sets from literature into costs.
The main innovations of this study are as follows: First, a current and
future cost level is presented that is derived by linking an established
cost estimation technique (PBCM) with the current battery specific
discussion related to process optimization, material & design optimi­
zation and location alternatives. This analytical approach provides the
most reliable and up to date basis to evaluate the cost potential of
lithium-ion batteries. Second, by presenting the most comprehensive
cost driver analysis within lithium-ion battery manufacturing (scenariorelated parameters, process steps and cost elements) guidance is pro­
vided to set focus in future cost optimization activities. Third, due to its
extensive parameter foundation, the presented model and the associated
model architecture provides an optimal starting point to translate
further battery innovations into costs. This is, especially in the highly
dynamic battery environment, a valuable capability as it provides the
basis for cost-optimal and data-driven decision making. Summarizing,
the results of this paper contribute to evaluate the technological po­
tential of lithium-ion batteries and support the materialization of this
potential which is relevant for both, science and industry.
2. Background
2.1. Battery design and manufacturing
Automotive traction battery systems consist of battery modules and
battery cells that are connected and controlled by a battery management
system. The cells are a crucial component as they significantly influence
the performance and cost of the whole system (Kwasi-Effah and Rabc­
zuk, 2018; Nelson et al., 2019). The major constituents of a lithium-ion
battery cell, which is currently the state-of-the-art technology (Aalder­
ing et al., 2019), are the cathode (positive electrode) (Arinicheva et al.,
2020; Whittingham, 2004) and the anode (negative electrode) (Andre
et al., 2017) as well as the separator and the electrolyte. Lithium tran­
sition metal oxides (LiMO2) with transition metals (M) such as nickel,
cobalt, and manganese (NMC) are the most widely used class of positive
active materials (Andre et al., 2015; Myung et al., 2017; Schmuch et al.,
2018). Carbonaceous materials, in particular synthetic and natural
2
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
Fig. 2. Classification of cost estimation techniques including key advantages, limitations and examples for techniques.
graphite, as well as amorphous carbons are mostly used as negative
active material (Blomgren, 2017; Schmuch et al., 2018; Scrosati et al.,
2015). As electrolyte solvents, a mixture of cyclic and linear organic
carbonates with lithium hexafluorophosphate as conductive salt (typi­
cally ~1 mol L− 1) are used (Schmuch et al., 2018; Xu, 2014). The used
microporous separator is typically based on polyolefins such as poly­
propylene (Lee et al., 2014). For current collectors, thin sheets of
aluminium and copper are used for the cathode and the anode, respec­
tively. Conductive electrode additives are usually carbon-based. With
regard to battery cell production, numerous consecutive process steps
are required. The manufacturing processes outlined in the following
represent large-scale production of a lithium-ion pouch cell, as pre­
sented in Kwade et al. (2018). This production can be divided into three
value-adding superordinate main processes: electrode production, cell
production, and cell conditioning. Other processes are also necessary to
support the execution of these three value-adding processes (e.g.
inter-process material handling). Fig. 1 shows the individual process
steps.
In electrode production, anodes and cathodes are produced. Anode
and cathode production are spatially separated, but basically the same
process steps are followed. For the sake of simplification, the process
steps for cathode production only are described here. First, the cathode
components, namely the active material, typically NMC622 in state-ofthe-art (Schmuch et al., 2018), polymer binder (e.g. PVdF), solvent (e.
g. NMP), and conductive additives (e.g. carbon), are batch-wise mixed in
a planetary mixer for several hours to produce a cathode slurry. The
target of this process step is to achieve the desired homogeneity and
viscosity of the slurry ensuring the electrode’s subsequent electro­
chemical performance and adhesion to the current collector foil (Dreger
et al., 2015). In current cathode chemistries, this adhesion is provided by
PVdF binders whose processing relies on the toxic and teratogen solvent
NMP. For anode production, NMP has already been replaced by water
which could also be implemented in cathode processing. This reduces
the drying effort due to a lower boiling point (water 100 ◦ C vs. NMP
203 ◦ C), eliminates the complex and costly solvent recovery process,
decreases associated material cost (water < $0,02 L− 1 vs. NMP $1-3 L− 1)
and results in more sustainable manufacturing (lower CO2 emissions,
less toxic materials) (Bresser et al., 2018a). The Mixing process is fol­
lowed by the four continuous and interconnected process steps Coating,
Drying, Calendering and Slitting, which can be summarized as
roll-to-roll processes. These four adjacent processes should be linked and
hence share the roll-to-roll working speed as an essential process
parameter. State-of-the-art electrode processing has already reached
working speeds of 25–50 m min− 1 (Kwade et al., 2018). Within the
Coating process step, thin metal carrier foils (e.g. aluminum) are coated
on both sides with the active material slurry and directly dried to solidify
the slurry by evaporating the solvent. As the working width of the coater
(up to 1500 mm) exceeds the width of common single electrodes, the
Fig. 3. Battery-cell-specific process-based cost modelling (PBCM) framework.
3
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
(Ciez and Whitacre, 2017).
To enable electrode production, cell production and cell condition­
ing additional supporting processes are necessary: ensuring a clean at­
mosphere required for cell production through the use of a dry room,
recycling the NMP solvent used for cathode production, ensuring proper
material handling through all process steps, and managing the receipt of
purchased materials and the shipping of finished cells (Nelson et al.,
2012).
Table 1
Product characteristics of battery cells considered for Base and Optimistic
Scenario.
Cell Design Specifications
Base
Energy
193
Energy density
265
Format
Pouch
Chemistry
NMC622 ||G
Number of two-sided electrodes
Cathode
30
Anode
30
Coating Thickness
Cathode
65
Anode
74
Width of electrodes*
Cathode
93
Anode
95
Length of electrodes*
Cathode
294
Anode
296
* based on ISO cell format Pouch VIFB-/99/300
Optimistic
Unit
211
296
Wh cell− 1
Wh kg− 1
NMC811 ||G
20
20
pieces cell−
pieces cell−
100
120
μm
μm
1
2.2. Cost estimation and battery cost modeling
1
Cost estimation involves the forecasting of costs of activities that
have not yet been carried out at the time the estimation is conducted
(H’midaa et al., 2006; Shehab and Abdalla, 2002). Over the last decades,
a variety of different cost-estimation techniques have been developed
that researchers categorize using various criteria (Cavalieri et al., 2004;
Hueber et al., 2016; Niazi et al., 2006; Qian and Ben-Arieh, 2008b;
Shehab and Abdalla, 2001, 2002; Zhang et al., 1996). Referencing the
approach published by Hueber et al. (2016), the techniques can be
categorized as intuitive, analogical, parametric or bottom-up.
Intuitive techniques rely on the estimator’s knowledge and experi­
ence. Within these techniques, rough, somehow subjective and arbitrary
results can be generated without greater effort but they are neither
comprehensible nor repeatable for a third person (Niazi et al., 2006).
Analogical techniques link historical cost data to a new product using
regression models or neural network approaches. These techniques rely
on the assumption that similar products have similar costs (Curran et al.,
2004). Parametric cost estimation techniques use product-specific cost
functions to predict costs. The cost functions can consist of several pa­
rameters or variables, like part weight or part size (International Society
of Parametric Analysts, 2018). Within the bottom-up techniques a
product is first broken down into its individual components. The com­
ponents are then broken down into resources and processes required to
produce them, with costs assigned to each (Ben-Arieh and Qian, 2003).
Fig. 2 presents the categories and summarizes the key advantages and
limitations per category. Further research on intuitive techniques can be
found in Ficko et al. (2005), Rush and Roy (2001), Sakti et al. (2017),
Shehab and Abdalla (2002), on analogous techniques in Cavalieri et al.
(2004), Hagen et al. (2015), Verlinden et al. (2008), Wang (2007), on
parametric techniques in Duverlie and Castelain (1999), Nelson et al.
(2019), Patry et al. (2015) and on bottom-up techniques in Ciez and
Whitacre (2017), Cooper and Kaplan (1988), Ficko et al. (2005), Field
et al. (2007), Schulze et al. (2012).
Relying on these techniques and driven by the relevance of battery
costs, various battery cost models have been developed within the last
years. Most of these models focus on the calculation of material costs
(Berg et al., 2015; Petri et al., 2015; Schmuch et al., 2018; Wentker et al.,
2019). A detailed description and analysis of these models can be found
in the review article published by Duffner et al. (2020). However, to
date, across this literature, no battery cost study is available that neither
deals with the state-of-the-art nor with further optimized future pa­
rameters, that have already been reported within literature, compre­
hensively. This paper will address this gap by translating current and
future parameters characteristics into costs. Therefore, a more
comprehensive lithium-ion battery manufacturing cost model, which
relies on more than 250 calculation parameters, is presented.
mm
mm
mm
mm
electrodes are cut to the desired width after Calendering by roll knives.
In the Coating and Calendering processes the targeted electrode thick­
ness is manufactured, which strongly effects cell properties. Current
cathode thicknesses of high-energy cells range from 65 to 80 μm
(Schmuch et al., 2018). Increasing electrode thickness results in higher
energy densities but has a decreasing effect on rate capability and
therefore power density, which also applies vice versa (Zheng et al.,
2012). Further, electrode thickness has an impact on cost, as thicker
electrodes result in a higher share of active material in the cell and
hence, lower material cost for non-active components can be achieved
(Patry et al., 2015). Finally, within the last process step of electrode
production, the electrodes are dried under vacuum to further reduce
moisture before they are transferred to a dry room (Kaiser et al., 2014;
Nelson et al., 2019).
To produce battery cells in a z-folded format, which is a state-of-theart electrode stacking order for lithium-ion pouch cells produced in large
scale, the anodes and cathodes are first cut into single sheets. Second,
the separator is fed as an endless folded band, and the anodes and
cathodes are alternately inserted into the interstitial space. This is a
highly automated process which is currently conducted at an operating
speed of 60 sheets min− 1 (Mooy, 2019). Third, within Contacting, in­
ternal contacts between the anode, cathode, and separator assembly are
created by welding. Subsequently, the assembly is inserted into the
housing (e.g. pouch). After insertion, the cell is filled with electrolyte (e.
g. LiPF6) and closed (Kwade et al., 2018; Tagawa and Brodd, 2009).
Cell conditioning begins during the Formation and Final sealing
process step. During Formation, the cell is charged for the first time,
followed by discharge and further charging cycles at different charging
rates. This procedure currently takes several days (Wood et al., 2015).
The Formation process is crucial for cell performance and safety, as it
builds up the solid electrolyte interphase (Winter 2009), which protects
the graphite-anode from adverse ongoing reactions with the electrolyte
(Arora, 1998). During the Formation procedure, gas is generated within
the cell. Applied external pressure causes the cell to expel the gas, and
the cell is finally sealed. Lastly, an Aging procedure is conducted that
takes up to several weeks and consists of storing the cells under
controlled conditions and performing several quality measurements to
detect non-standard properties such as short circuits. (Michaelis et al.,
2016; Tagawa and Brodd, 2009; Verma et al., 2010). Only cells that
fulfill quality requirements in Final Control can be sold according to
their original purpose. If a cell does not fulfill these requirements it is
stated as end-of-line scrap. The later a process step takes place in the
value chain the more sensitive it is to scrap cost as more and more value
has already been added in previous process steps (Kwade et al., 2018).
Hence, low end-of-line scrap rates are crucial for a competitive cell
production. Typically, 95% of finished cells fulfill quality requirements
3. Cost model
To develop the cost model presented within this study, PBCM is used,
which is a bottom-up technique that calculates manufacturing costs
analytically based on technical and operational parameters. Its reliance
on technical parameters makes the technique powerful, especially for
predicting costs for unexplored technologies, as technical data is usually
more easily available then historical cost data. Further, it generates
transparency in regard to which parameters contribute the most to the
4
International Journal of Production Economics 232 (2021) 107982
F. Duffner et al.
manufacturing processes and to answer various research questions,
including those focused on the evaluation of alternative processes, ma­
terials and concepts or the evaluation of process improvements (Ciez
and Whitacre, 2017; Farooq et al., 2018; Fuchs et al., 2006; Johnson and
Kirchain, 2009a, 2009b; Nadeau et al., 2010; Sakti et al., 2015).
The PBCM framework was introduced by Field et al. (2007). As
total cost and it enables the monetary quantification of potential
parameter improvements. These properties make the method highly
suitable for industries based on unexplored technologies that are facing
high cost pressure and therefore, anticipate numerous parameter opti­
mizations (Field et al., 2007; Fuchs et al., 2006; Nadeau et al., 2010).
Based on its mentioned strengths, PBCM has been used for multiple
Table 2
Cost categories, related parameters, parameter descriptions and sources of parameter specifications.
Cost
category
Parameter description
Source
PVSalable
Required number of annual salable units
Derived from Michaelis et al. (2018)
PCEnergy
Energy content per unit
Wentker et al., 2019a
r
Annual discount rate
Ciez and Whitacre (2017)
DPY
Operating days per year
Nelson et al. (2019); Schnell et al. (2020)
CTotal
Total unit cost
Calculated (see Supplementary information)
Gross number of units produced in process
step j
Calculated (see Supplementary information)
j
Cost for process step j
Calculated (see Supplementary information)
j
Variable cost for process step j
Calculated (see Supplementary information)
Unit cost for cost element e ∈{Material, Labor,
Energy}
Calculated (see Supplementary information)
ACe
Annual cost for cost element e ∈{Material,
Labor, Energy}
Calculated (see Supplementary information)
Mj,Material
Net mass of the material required
BatPaC, 2018, Wentker et al., 2019a
x
Machine-specific scrap losses for process step j
Nelson et al. (2019), Ciez and Whitacre (2017)
Scrapj,Mat.
Material type-specific scrap losses for process
step j
Nelson et al. (2019), Ciez and Whitacre (2017)
UMaterial
Unit cost of materials
Nelson et al. (2019), Wentker et al., 2019a
NLaborers
Number of laborers required per machine
Nelson et al. (2019), 2012; Sakti et al. (2015); Schünemann (2015); expert discussions
NOS
Number of shifts per day
Ciez and Whitacre (2017)
OHS
Operating hours per shift
Ciez and Whitacre (2017)
UB
Unpaid breaks hours per shift
Fuchs et al. (2006)
PB
Paid breaks hours per shift
Derived from Fuchs et al. (2006); Sakti et al. (2015)
APOT
Annual paid operating time
Calculated (see Supplementary information)
ULabor
Unit cost of labor
Eurostat (2019)
• Energy
SREnergy
Surcharge rate for energy
Ciez and Whitacre (2017); Sakti et al. (2015)
Fixed cost
j
CFixed
Fixed cost for process step j
Calculated (see Supplementary information)
Unit cost for cost element e ∈{Machine,
Building, Maintenance, Overhead}
Calculated (see Supplementary information)
ACe
Annual cost for cost element e ∈{Machine,
Building, Maintenance, Overhead}
Calculated (see Supplementary information)
CT
Cycle time
UD
Unplanned downtime
Derived from Kaiser et al. (2014); Knoche (2017); Kwade et al. (2018); Mao et al. (2018); Nelson
et al. (2019), 2012; Sakti et al. (2015); Schünemann (2015); Tagawa and Brodd (2009); Wood et al.
(2015); Yoshio et al. (2009)
Sakti et al. (2015)
LMachine
Useable lifetime of machines
Nelson et al. (2019); Schnell et al. (2020)
Unit cost of machines
Nelson et al. (2019), 2012; Sakti et al. (2015); Schünemann (2015); expert discussions
RjMachine
Annual allocated machine costs for process
step j
Calculated (see Supplementary information)
NMachine
Number of machines required for process step j
Calculated (see Supplementary information)
Annual required machine time for process step
j
Calculated (see Supplementary information)
availMTj
Annual available operating time of a machine
Calculated (see Supplementary information)
FPMachine
UBuilding
Overarching
j
PVEffective
CProcess
Variable cost
CVariable
Cje
j
• Material
• Labor
j
Ce
j
• Machine
UMachine
j
reqMT
• Building
j
Footprint per machine
Nelson et al. (2019), 2012; Sakti et al. (2015); Schünemann (2015); constructive design
LBuilding
Useable lifetime of buildings
PwC (2020)
Unit cost of buildings
Turner and Townsend (2018); ECC, 2019
RjMachine
Annual allocated building costs for process
step j
Calculated (see Supplementary information)
• Mainten.
SRMainten.
Surcharge rate for maintenance
Ciez and Whitacre (2017); Sakti et al. (2015)
• Overhead
SROverhead
Surcharge rate for overhead
Ciez and Whitacre (2017); Sakti et al. (2015)
5
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
shown in Fig. 3, it consists of three interconnected sub-models: the
process model, operations model, and financial model. The process
model transforms product characteristics (e.g. size, shape, and material)
into technical parameters (e.g. cycle time, machine capacity, downtime,
and rejection rate) based on engineering, scientific, and technological
principles. The operations model derives the overall resource re­
quirements (e.g. equipment, footprint, labor, and energy) based on
operating conditions (e.g. working days/year, shifts/day and hours/­
shift) and technical parameters. The financial model adds factor prices
to resources, which results in the manufacturing costs for a specific
amount of a specific product (Field et al., 2007; Nadeau et al., 2010).
Since battery manufacturing comprises of a large number of indi­
vidual and complex process steps, all of which mutually influence each
other, not all of the engineering, scientific, and technological principles
involved have been studied in a holistic way in efforts to translate
product characteristics into technical parameters. To address the infor­
mation gap for technical parameters, we rely on empirical data. To map
the battery-specific approach in the PBCM framework, we enlarged the
framework by adding an additional input parameter category, namely
technical observations, as shown in Fig. 3.
The model parameters related to the categories Technical Observa­
tions, Operating Conditions, and Factor Prices are listed in Table 2. For
reasons of clarity, these parameters have been classified according to
their cost category (e.g. variable, fixed) and their associated cost
element (e.g. material, labor).
4. Scenario-based analysis
The target of the scenario-based analysis is to identify the current
battery cost level by initializing the process-based cost model with stateof-the-art large-scale parameter specifications and to forecast a future
cost level by translating most relevant innovations reported in academic
discussion into costs. Therefore, two scenarios are defined, a Base Sce­
nario representing the state-of-the-art in large-scale battery
manufacturing and a future-oriented Optimized Scenario. For the Base
Scenario, the battery literature is surveyed regarding characteristics that
represent both, the state-of-the-art production technology and materials
and designs that are currently in use for large-scale production. Further,
a typical high-cost country for battery manufacturing is assumed as
plant location. For the Optimized Scenario, a categorized approach is
taken that classifies reported innovations from literature into processrelated, material & design-related and location-related simulation pa­
rameters. For process-related simulation parameters, based on the
resulting cell cost calculation in the Base Scenario, a literature review for
the most cost-driving process steps is conducted and related process
innovations are identified. For material & design-related simulation
parameters, based on the resulting cell cost calculation in the Base
Scenario, a literature review for the most cost-driving component is
conducted and reported material and design innovations are identified.
For location-related simulation parameters, a typical low-cost country
for battery manufacturing is assumed as plant location, respective
country-specific characteristics are taken from literature and integrated
in the parameter set. Finally, in order to ensure inter-scenario consis­
tency of the underlying parameters taken from various sources, both
parameter sets have been discussed with industry experts. In the
following, each simulation parameter is described, including its effect on
the cell cost calculation, its value in both scenarios and potential chal­
lenges in implementing respective improvements.
3.1. Model architecture
The presented model architecture operationalizes the PBCM tech­
nique for the manufacture of battery cells. Similar architecture de­
scriptions have been introduced for other technologies (see e.g., Johnson
and Kirchain, 2009a). The model architecture used for the presented
cost model is adapted, as it must ideally meet the specific requirements
of battery-cell cost modelling. In the following, the most important
variable definitions and calculation rules are introduced. The complete
model architecture can be found in Appendix A.
The total unit cost CTotal is calculated by summing up the cost of all
process steps of battery cell manufacturing (see Fig. 1). The cost for
j
j
process step j, CProcess , can be divided into variable CVariable and fixed
j
j
CFixed . The variable cost CVariable includes the cost elements for Material
j
j
j
j
CMaterial , Labor CLabor , and Energy CEnergy whereas the fixed cost CFixed
j
j
includes the cost elements for Machine CMachine , Building CBuilding ,
j
j
Maintenance CMaintenance , and Overhead COverhead . The mathematical re­
lations are shown in equations (1)–(4), where n is the total number of
considered process steps and j is the number of the specific process step.
n
∑
CTotal =
4.1. Process-related simulation parameters
[1] Increased roll-to-roll working speed
(1)
j
CProcess
The speed at which the so-called roll-to-roll processes (Coating &
Drying, Calendering, and Slitting) are performed depends on material
properties, process competencies and machine capabilities. An increase
in working speed decreases the time needed to produce the electrodes
required for one battery cell. This effect is represented in the cost model
with a reduced cycle time for these process steps (CTj=Coating&Drying ,
CTj=Calendering , CTj=Slitting ). Where the cycle time is in general calculated
process step specific, for the process steps Coating & Drying, Calen­
dering and Slitting the identical parameters are used. Namely, working
width of machine, working speed, cathodes/anodes per cell, width of
cathode/anode, length of cathode/anode are used. A reduced cycle time
reduces the number of machines needed to produce a target volume of a
product (NM). While a higher working speed of the roll-to-roll process
steps increases the machine capabilities required, the cost-increasing
effects must also be considered. In particular, this means higher unit
investments and larger machine footprints for all roll-to-roll processes.
Within this increase, additional capabilities are crucial for process step
j = Coating&Drying, as a higher number of electrodes must be dried in a
certain time. This is technically achieved by increasing the length of the
dryer that is part of the Coating & Drying machine unit. Accordingly, a
linear cost function for machine unit investments and the machine
footprint of the dryer unit must be assumed. A parameter value of 25 m
j=1
j
CProcess
j
= CVariable
+
j
CFixed
(2)
j
j
j
j
CVariable
= CMaterial
+ CLabor
+ CEnergy
(3)
j
j
j
j
j
CFixed
= CMachine
+ CBuilding
+ CMaintenance
+ COverhead
(4)
3.2. Input parameters
The presented cost model consists of more than 250 parameter
characteristics from the Product Characteristics, Technical Observa­
tions, Operating Conditions, and Factor Prices categories. The origin of
the data for the specific parameters used in the model is described in the
following.
Product Characteristics were mainly defined using the CellEst bat­
tery cost model by Wentker et al., 2019. It proposes the selected cell
dimensions, the pouch cell format and different cell chemistries,
reflecting a battery cell for a vehicle that is purely electric. For some
parameters that are not considered in CellEst (e.g. process-related ma­
terial cost like NMP), data from BatPac has been included in the analysis.
Table 1 lists the product characteristics used for this study.
6
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
min− 1 is characterized as state-of-the-art and therefore applied in the
Base Scenario (Kwade et al., 2018). Regarding future developments, it is
reported that a coating speed of up to 100 m min− 1 is feasible and is
therefore set for the Optimized Scenario (Schmitt, 2015). To materialize
this higher coating speed, the bead pressure and low-flow limit
(maximum speed at a given film thickness, or the minimum film thick­
ness at a given speed, at which the coating bead remains stable) and its
associated parameters must be controlled especially to avoid film
break-ups and to ensure film uniformity as basis to not deteriorate scrap
rate and/or cell performance (especially unfavorable current distribu­
tions in operating cell and reduced power density due to increased cell
volume) (Carvalho and Kheshgi, 2000; Romero et al., 2004; Schmitt
et al., 2014).
Within the Formation procedure, the cell is charged and discharged
several times using specific charging rates to build up the solid elec­
trolyte interphase (Winter 2009). Both, the number of charge and
discharge cycles required and the rate at which they can be executed
(C-rate), defines the duration of the formation procedure within process
step j = Formation&Final Sealing. Optimizing these parameters results in
a cycle time reduction for this process step (CTj=Formation&Final Sealing ). For
the Base Scenario, reported as state-of-the-art, we assume a typical
formation procedure with 3.5 charge/discharge cycles and C-rates
ranging from 0.05 to 0.5 C resulting in a formation time of 75 h (Wood
et al., 2015). In the Optimized Scenario, a reduced formation protocol is
followed with 1.5 charge/discharge cycles and a C-rate of 0.5 C that
totals in 11 h (Mao et al., 2018). As the graphite anodes are thermo­
dynamically unstable against the electrolyte, an optimal solid electro­
lyte interphase layer is the basis to protect the graphite-anode from
adverse ongoing reactions with the electrolyte (Arora, 1998) which
would have negative effects on cell capacity due to continuous elec­
trolyte decomposition, and graphite exfoliation (Mao et al., 2018;
Winter 2009). Thus, when further reducing charging cycles and/or
increasing C-rates, the product specific fulfillment of the solid electro­
lyte interface requirements must be considered. To reduce formation
time while fulfilling these requirements at the same time, optimized
charging protocols (Mao et al., 2018) as well as optimized electrode
surfaces and electrolyte compositions (Winter 2009) can be key to
success.
[2] Usage of alternative solvent
As described in section 2.1, water is currently used as solvent to
produce active anode material slurry and teratogen and toxic NMP (Nmethyl-2-pyrrolidone) is used to produce active cathode material slurry.
With regard to future, it is reported that water can also be used as solvent
to produce the active cathode material slurry (Bresser et al., 2018a; Du
et al., 2017; Ibing et al., 2019; Wood et al., 2015). Using water instead of
NMP results in three beneficial main effects: (1) The solvent recovery
process can be completely omitted as it is only necessary due to the
js=Solvent revovery
properties of the NMP (ACSupporting
= 0). (2) The dryer length of the
Coating & Drying unit can be reduced as the evaporation rate of water is
twice as high as that of NMP (Wood et al., 2015). This reduction leads to
reduced machine unit investments
j=Coating & Drying
j=Coating & Drying
(UMachine
)
[5] Decreased end-of-line scrap rate
and a decreased
Battery manufacturing is very cost sensitive to the scrap produced
due to the high number of process steps and the high share of material
costs. The end-of-line scrap rate (xj=Aging&Final Control ) indicates the per­
centage of rejected parts identified during process step j =
Aging&Final Control. The rate depends on the process quality of the in­
dividual upstream process steps as well as the ability to detect defective
parts at an early stage and exclude them from further production pro­
cess. In general, if scrap can be reduced, a lower number of cells must be
produced to reach a certain target quantity of salable products. Typi­
cally, 5% of finished cells do not fulfill quality requirements, hence this
rate is applied for the state-of-the-art Base Scenario. For the Optimized
Scenario we assume the lower bound of 1% as end-of-line scrap (Ciez
and Whitacre, 2017). There are in general two strategies to further
reduce the end-of-line scrap rate: (1) Increase process quality within the
process steps along the whole value chain for which an in-depth un­
derstanding of the process steps, its process parameters as well as its
interaction is of utmost relevance (Kwade et al., 2018), (2) The early
detection of scrap components, as the later a process step takes place in
the value chain the more value is lost when identifying a defect. To
materialize strategy (1), data-driven approaches (Turetskyy et al.,
2020), and for materialization of strategy (2), advanced quality man­
agement concepts (Schnell and Reinhart, 2016) are currently discussed
as promising approaches.
). (3) The material unit cost for NMP
machine footprint (FPMachine
(Uz=NMP
)
can
be
replaced
by
the
lower
cost of water(Uz=Water
Material
Material ). Hence, a
state-of-the-art NMP-process is assumed for the Base Scenario and an
aqueous cathode coating process for the Optimized Scenario, respec­
tively. However, a prerequisite for the substitution of NMP is the ability
to control the negative effects of using water: (1) Risk of metal disso­
lution in water which can result in capacity losses if ions cannot be
intercalated back to the cathode active material, (2) Risk of reduced
cycle life due to higher slurry surface tension and lower adhesion
strengths between slurry and current collector, (3) Risk of reaction be­
tween the cathode alkaline water solution and the processing machines
and the collector foils (Bresser et al., 2018a; Du et al., 2017; Ibing et al.,
2019; Wood et al., 2015).
[3] Increased stacking speed
Within the state-of-the-art z-folding procedure, the stacking speed is
defined as the speed at which the electrodes (anodes and cathodes) are
positioned in the zigzag fold separator during process step j = Stacking.
The speed reached depends on the process competencies and the capa­
bilities of the stacking machine. An increase in stacking speed reduces
the cycle time of the process step Stacking (CTj=Stacking ). For the Base
Scenario, a state-of-the-art parameter value of 60 electrode sheets
picked and placed min− 1 is applied (Mooy, 2019). In recent literature,
higher stacking speeds of or even exceeding 180 sheets min− 1 can be
observed (Sakti et al., 2015; Schnell et al., 2020; Schünemann, 2015).
Therefore, this value is set for the Optimized Scenario. The main chal­
lenge to materialize higher stacking speeds is the increase of machine
capabilities to accelerate the highly automated pick-and-place operation
while keeping sheet positioning and orientation accuracy. This accuracy
is crucial for cell performance (especially capacity and safety), as it
determines the area coverage of anode and cathode sheets and prevents
the physical contact of the electrode as basis to avoid short circuits
(Mooy, 2019).
4.2. Material and design-related simulation parameters
[6] Increased electrode thickness
Electrode thickness within battery cells can vary depending on the
target product specifications of the battery cell. Using thick electrodes
requires a fewer number of electrodes to achieve the target cell energy
(Ibing et al., 2019; Patry et al., 2015; Singh et al., 2016). Reducing the
number of electrodes has three cost-effective impacts: (1) a reduced
cycle time of the roll-to-roll processes (CTj=rtr ), the cutting process
(CTj=Cutting ), and the stacking process (CTj=Stacking ), (2) a reduced mass of
Cathode foil
Anode foil
non-active electrode materials required (MMaterial ; MMaterial ), (3) an
increase in the dryer length of the Coating & Drying machine unit, since a
[4] Accelerated formation procedure
7
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
Table 3
Names of simulation parameter, parameter categories, parameter characteristics, and initial sources.
Number and name of simulation
parameter
Parameter category
Unit
[1] Increased roll-to-roll working
speed
[2] Usage of alternative solvent
Process-related
Working speed in m min−
Process-related
[3] Increased stacking speed
Process-related
[4] Accelerated formation procedure
Process-related
[5] Decreased end-of-line scrap rate
[6] Increased electrode thickness
Process-related
Material & designrelated
[7] Material change from NMC622 to
NMC811
[8] Decreased unit cost for labor
[9] Decreased unit cost for building
space
[10] Increased number of operating
days
[11] Increased depreciation period
for machines
[12] Increased depreciation period
for buildings
Material & designrelated
Location-related
Location-related
Location-related
Location-related
Location-related
Base
Optimized
Source
25
100
Type of solvent
NMP
Water
Electrode sheets picked and
placed min− 1
Number of charge/discharge
cycles
c-rate
Scrap rate in %
Coating thickness Cathode in
μm
Coating thickness Anode in
μm
Type of material
60
180
3.5
1.5
0.05–0.5
5
65
0.5
1
100
Base: Kwade et al. (2018)
Optimized: Schmitt (2015)
Base: Ciez and Whitacre (2017); Nelson et al. (2019),
2012; Sakti et al. (2015)
Optimized: Bresser et al. (2018a); Du et al. (2017);
Wood et al. (2015)
Base: Mooy (2019)
Optimized: Schnell et al. (2020); Schünemann (2015)
Base: Wood et al. (2015)
Optimized: Mao et al. (2018)
78
120
NMC
622
38
2345
NMC
811
10
1292
300
360
6
8
25
50
1
Unit costs for labor in $ h− 1
Unit costs for buildings in $
m− 2
Operating days year− 1
Useful life of machines in
years
Useful life of buildings in
years
Base & Optimized: Ciez and Whitacre (2017)
Base: Schmuch et al. (2018)
Optimized: Zheng et al. (2012)
Base & Optimized: Derived from CellEst, Wentker et al.,
2019a
Base: Schmuch et al. (2018)
Optimized: Wentker et al. (2019)
Base & Optimized: Eurostat (2019)
Base & Optimized: Turner and Townsend (2018); ECC,
2019
Base: Nelson et al. (2019);
Optimized: Schnell et al. (2020)
Base: Nelson et al. (2019)
Optimized: Schnell et al. (2020)
Base & Optimized: PwC, 2020
Fig. 4. Cost reduction per simulation parameter (single simulation-parameter approach) in $ kWh− 1 @ 35 GWh annual factory capacity; [n] number of single
simulation parameters; Categorical affiliation:
Process-related,
Material & design-related,
Location-related.
higher quantity of material must be dried per electrode (anode and
cathode) in the same time period. This increase leads to higher machine
j=Coating& Drying
unit investments (UMachine
j=Coating&Drying
(FPMachine
).
Scenario a more cost-effective thicker electrode of 100 μm (Zheng et al.,
2012) is assumed. Producing electrodes with a thickness of 100 μm and
above is technical feasible. However, as mentioned, the optimum
thickness of the electrodes depends on the target product specifications
of the battery cell. Thin electrodes are advantageous for products that
are sensitive to durability and power. Thick electrodes are used for
) and increased machine footprints
For the Base Scenario, a common electrode thickness
of 65 μm (Schmuch et al., 2018) is applied, whereas for the Optimized
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F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
still are under consideration) as a large-scale battery factory location.
For the Base Scenario, we choose Germany as plant location. This is
firstly due to the fact that several battery manufacturers (e.g. SAFT,
Northvolt) have decided to localize here (Michaelis et al., 2018) and
secondly it shows comparatively high labor cost levels ($38 h− 1, Euro­
stat, 2019). For the Optimized Scenario, we choose Hungary as plant
location, as there are already plants in operation or under construction
(e.g. Samsung SDI, SK Innovation) (Michaelis et al., 2018) and labor
costs are low within European comparison ($10 h− 1, Eurostat, 2019).
[9] Decreased unit costs for building space
Unit costs for building space (UBuilding ) depend on the location and
building requirements. Building requirements are fixed by the product
being produced and thus do not vary for a battery cell plant. Location
dependency, on the other hand, is variable and thus relevant for the
study, since different locations are considered for a battery cell factory.
To remain consistent with the labor cost scenarios introduced above,
building cost levels of Germany ($2345 m− 2) and Hungary ($1292 m− 2)
are considered for the Base Scenario and the Optimized Scenario,
respectively (ECC, 2019; Turner and Townsend, 2018).1
Fig. 5. Cost reduction per category (categorized simulation-parameter
approach) in $ kWh− 1 @ 35 GWh annual factory capacity.
products that are more sensitive to energy density and costs. Conse­
quently, increasing electrode thickness without changing cell di­
mensions (as in the here presented model) results in altered battery
characteristics such as increased energy density and decreased power
density. As the present study is focused on costs, simultaneous variations
in cell specifications are accepted (see Table 1).
[10] Increased number of operating days
A plant’s operating days per year (DPY) mainly depend on the fac­
tory’s production strategy. While some shut down days per year are
necessary for maintenance or due to public holiday, especially factories
using highly automated and cost-intensive manufacturing equipment
are encouraged to maximize the number of operating days, as this results
in an increased annual available operating time of machines (availMT).
Therefore, the number of machines required to produce a target volume
(NM) decreases. A parameter value of 300 days year− 1 has been set in
the Base Scenario to limit fixed assets to a reasonable extent (Nelson
et al., 2019). For the Optimized Scenario a value of 360 days year− 1 was
taken into account that has been used in recent literature (Schnell et al.,
2020).
[7] Material change from NMC622 to NMC811
Since cathode active materials represent the most cost-driving cell
components (cell cost share > 30% in the Base Scenario) and they
currently represent a bottleneck for cell performance (Schmuch et al.,
2018), significant research effort has been spent on their development.
An improvement in their inherent characteristics, such as specific ca­
pacity or crystallographic density, results in a higher energy content per
unit PCEnergy , thereby reducing material input quantities required to
produce a certain amount of energy output and hence reducing cell cost
(Schmuch et al., 2018). While NMC622 is considered a state-of-the-art
cathode active material (Schmuch et al., 2018) and is therefore
applied in the Base Scenario, one strategy to enhance energy content is
to increase the nickel share. Hence, the use of NMC811 with a higher
nickel content is assumed for the Optimized Scenario (Wentker et al.,
2019). The resulting product characteristics for both scenarios such as
cell energy, energy density and number of electrodes per cell are pre­
sented in Table 1. However, when enhancing the nickel share, the cobalt
and manganese shares are reduced simultaneously. As the nickel and the
cobalt provide structure stability within the cathode active material,
which is primarily responsible for cell properties like thermal stability or
cycle life, strategies must be developed to address these challenges to
avoid limitations in cell performance (Andre et al., 2015). Promising
approaches were already reported in literature related for example to
structural design (core–shell structure, concentration gradient, etc.) and
intrinsic structure optimization (Wang et al., 2020).
[11] Increased depreciation period for machines
The time span in which a machine is depreciated is represented by its
useful life, LMachine , which depends on its technical durability and the
length of time it can economically produce products with the required
performance. In general, depreciation periods are longer in low cost
countries (e.g. Hungary) compared to high cost countries (e.g. Germany)
(PwC, 2020). Battery related literature report periods range from 6 to 8
years. Therefore, we used 6 years (Nelson et al., 2019) for the Base
Scenario and 8 years (Schnell et al., 2020) for the Optimized Scenario.
[12] Increased depreciation period for buildings
1
To survey the location-specific factor cost of building, we use a two-stage
approach as no data are directly available. Turner and Townsend (2018) pub­
lished absolute construction costs for some European countries, and ECC (2019)
published percentage values of construction costs for all EU-28 countries.
Hence, we defined the United Kingdom as the baseline since, among EU
countries, Turner and Townsend published the most comprehensive data for
this country, including the differentiation of costs in the United Kingdom ac­
cording to region, industry affiliation, and type of building. Since cell produc­
tion involves many consecutive and complex production steps (Kwade et al.,
2018), which are partly performed under increased environmental re­
quirements (Ahmed et al., 2017; Nelson et al., 2015; Yoshio et al., 2009), we
use values from the industrial high-tech factory/laboratory category to calcu­
late an average cost based on the region-specific values in the United Kingdom.
Using this baseline value from Turner and Townsend and the percentage values
from EEC, we calculate the construction cost per square meter for each Ger­
many and Hungary.
4.3. Location-related simulation parameters
[8] Decreased unit costs for labor
The unit costs for labor (ULabor ) depend on the location, the required
competence level of workers, and the industry. The required level of
competence and industry are fixed by the product and are thus not
variable. Location dependency, on the other hand, is variable and thus
relevant for this study, since different locations are considered for a
battery cell factory. In Europe, several countries have been chosen (or
9
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
Fig. 6. Cost walk from the Base Scenario to the Optimized Scenario based on cost elements (overall simulation-parameter approach) in $ kWh−
factory capacity.
The time span in which a building is depreciated is represented in its
useful life, LBuilding , which depends on its durability and production re­
quirements regarding the shop floor layout and environment. Produc­
tion requirements can change over time, even if the same type of product
is manufactured. Again, to remain consistent with the building cost
scenarios, Germany as a high cost country is chosen for the Base Sce­
nario (depreciation period of 25 years for buildings, PwC, 2020) and
Hungary as a low cost country is chosen for the Optimized Scenario
(depreciation period of 50 years for buildings, PwC, 2020).
To enable an overarching discussion of cost effects and to derive a
total cost level, beside the evaluation of the single simulation parameters
and the evaluation of the described scenarios, an evaluation based on
parameter categories (Process-related, material & design related, and
location-related simulation parameters) is conducted.
Table 3 lists the simulation parameters, the associated characteris­
tics, their sources and the associated parameter category.
1
@ 35 GWh annual
parameter approach). Thereby, a cost walk at the cost-element level is
presented from the total costs of the Base Scenario to the total costs of
the Optimized Scenario. The total costs are $106 kWh− 1 in the Base
Scenario and $64 kWh− 1 in the Optimized Scenario. This corresponds to
a cost reduction of 40%.
The cost walk shows that the presented simulation parameters
reduce, in particular, the cost elements Material, Machine and Labor.
The improvement in the cost element Material mainly results from
Increased electrode thickness and Material change from NMC622 to
NMC811 that both induce an increased energy content per cell, thereby
reducing material input quantities required to produce a certain amount
of energy output. For cost elements Machine and Labor, this reduction is
regarding that, on the one hand, in the Optimized Scenario, fewer factor
quantities (less machinery and less working hours per unit of output) are
necessary to produce the target volume, and, on the other hand, the
factor price for labor is lower. Looking at the cost reductions by cost
element at the single-parameter level reveals that all simulation pa­
rameters have a cross-cost-element effect. Nevertheless, the level of the
effect of each parameter differs. The simulation parameters for Increased
electrode thickness and Material change from NMC622 to NMC811 affect
each of the eight cost elements. On the other hand, the simulation pa­
rameters Decreased unit cost for labor, Decreased unit cost for building
space, and Increased depreciation period for buildings each affect only two
of the eight cost elements. When looking at the superordinate catego­
rization level, it is striking that all simulation parameters from the
process-related and material & design-related categories have a strong
cross-cost element, as they affect at least six of the eight cost elements
considered. On the other hand, the simulation parameters of the
location-related categories influence only two or three of the eight cost
elements considered.
Although all parameter characteristics within the Optimized Sce­
nario are derived from literature, there is an uncertainty to which extend
the assumed parameter optimizations can be achieved within future
large-scale manufacturing. To evaluate this uncertainty a sensitivity
analysis is conducted and can be found in Appendix C.
Transforming these overall cost results into vehicle-level figures and
linking them to revenues and profits illustrates the importance of costefficient battery production. Multiplying the average energy content of
a battery for a mid-range vehicle, 60 kWh (Schmuch et al., 2018), with
the cost per kWh from the Base Scenario and the cost per kWh from the
Optimized Scenario results in a cost difference of approximately $2500
per vehicle, thereby in the case of full materialization, significantly
impacting the economic success of EVs.
Fig. 7 shows the percentage distribution of the total costs among the
cost elements for the two overall scenarios (overall simulationparameter approach). In both the Base Scenario and the Optimized
Scenario, the cost elements that drive costs the most are Material and
Machine. The sharp increase of the Material cost share in the Optimized
5. Results and discussion
The following section presents and discusses the results of the study,
which are based on the presented model architecture (see Section 3.1),
the presented input parameters (see Section 3.2), the presented simu­
lation parameters (see Section 4) and a targeted annual production ca­
pacity of 35 GWh.
Fig. 4 shows the cost reduction per simulation parameter (single
simulation-parameter approach) in $ kWh− 1. The highest values result
from the two material and design-related optimizations Increased elec­
trode thickness at $12.9 kWh− 1 and Material change from NMC622 to
NMC811 at $8.9 kWh− 1. This is followed by the process-related opti­
mization Accelerated formation procedure at $6.9 kWh− 1 and the
location-related optimization Decreased unit cost for labor at $6.3 kWh− 1.
The value of each of the remaining simulation parameters is < $5
kWh− 1.
Fig. 5 shows the results based on the categorized simulationparameter approach. The results reveal that the highest cost re­
ductions, at $20.3 kWh− 1 result from the process-related category. This
is followed by the material & design-related and location-related cate­
gories, with cost reductions of $19.4 kWh− 1 and $14.9 kWh− 1, respec­
tively. The result that process-related innovations have the highest
impact on cell cost indicates that cost-optimal cell production depends
in particular on company-and factory-specific process competence.
Compared to this process competence, the often-discussed locationdriven parameters (Brodd and Helou, 2013; Duffner et al., 2020a),
which are represented by the location-related category, are of minor
importance. Especially in the context of labor, this is because batteries
are manufactured using highly automated and thus less labor-intensive
processes.
Fig. 6 shows the results of the overall scenarios (overall simulation10
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
Fig. 7. Percentage of costs per cost element for the Base Scenario and Optimized Scenario @ 35 GWh annual factory capacity.
Fig. 8. Cost share between process steps in % for the Base Scenario and Optimized Scenario @ 35 GWh annual factory capacity.
Scenario results from the disproportionately low decrease of material
cost compared to other cost elements. This is due to the fact that only
three out of twelve examined simulation parameters have an impact on
material costs. In contrast, Machine cost decreases overproportionately
since it is optimized by nine simulation parameters. In addition, it is
striking that the proportion of the cost element Labor is 2% in the
Optimized Scenario, compared to 8% in the Base Scenario. This effect is
mainly driven by the simulation parameter Decreased unit cost for labor as
it has a high absolute value and mainly affects the cost element Labor,
whereas the other simulation parameters with a high value, as
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F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
described, have a much stronger cross-cost-element effect.
The high ratio of the cost elements Material (77% in the Optimized
Scenario) and Material-Scrap (6% in the Optimized Scenario) to total
costs show that large-scale battery-cell production is highly sensitive to
net material input quantities, scrap rates and costs of purchased mate­
rials. From a materials-related point of view, measures to optimize cell
chemistry that focus on using fewer and more cost-effective materials
are appropriate to reduce both Material and Material-Scrap costs. From
a process-related point of view, the focus has to be set on Scrap as its cost
impact is amplified by the large number of interlinked and complex
process steps in cell production. Thus, measures to produce a smaller
number of process-step-specific defective parts and measures to identify
defective produced parts as soon as possible so that no additional costs
arise from subsequent process steps are suitable to reduce MaterialScrap costs.
In addition to the cost elements Material and Material-Scrap, the cost
element Machine should be the focus of future cost-reduction activities,
as this cost element accounts for the second largest share in the Opti­
mized Scenario at 8%. Both operational and strategic measures can
contribute to reducing costs from this cost element. From an operational
point of view, measures that contribute to the production of specific
quantities of acceptable parts with fewer machines are suitable. These
include measures to reduce cycle time, increase the annual operating
time (e.g. by reducing unplanned downtime), or to reduce the scrap rate.
From a strategic point of view, cost reductions can be achieved by
extending the useful life of machines. This would be conceivable if a cell
manufacturer, after a period of producing state-of-the-art cells
(regarding quality and performance), decides not to scrap deprecated
machines but continue producing battery cells with lower performance
and quality requirements.
Fig. 8 shows the process steps that drive costs the most in the Base
Scenario and the Optimized Scenario. To increase comparability with
the focus on operative process step execution, Material and MaterialScrap have been excluded for evaluation. This approach was chosen
because material costs, as described above, make up a high proportion of
the cost of battery cells and are for calculation purposes allocated to a
specific process step. This allocation is correct from a calculation point
of view but not ideal for comparability of the process steps, since the
process steps in which cost-intensive materials are used would exhibit
disproportionately high costs, even though the added material costs are
not directly related to execution of the cell making process.
In the Base Scenario, the three most cost-driving process steps are
Formation & Final Sealing (25%), Stacking (22%) and Coating & Drying
(12%). Due to their cost-driving properties, these process steps have
been focused within past research. The reported optimization has been
considered within the Optimized Scenario, which reduces their share
significantly.
Formation loses its cost dominance to Mixing as a result of the
Accelerated formation procedure and plays only a subordinate role in the
Optimized Scenario. Mixing process costs get most relevant as they are
only decreased by one optimization of the process-related category
(Decreased End-of-line Scrap). The process steps Stacking and Coating &
Drying however, remain cost drivers in the Optimized Scenario despite
the optimized parameter assumptions of a tripled stacking speed from 60
to 180 sheets min− 1 (simulation parameter: Increased stacking speed), a
quadrupled roll-to-roll speed from 25 to 100 m min− 1 (simulation
parameter: Increased roll-to-roll speed) and a reduction of the cathode
dryer length by replacing water with NMP (simulation parameter: Usage
of alternative solvent). It should also be noted that in the Optimized
Scenario, the cost-driving process steps Mixing, Coating & Drying and
Stacking are supplemented by the process step Aging & Final Control. As
this step is no roll-to-roll process and no specific parameter optimization
has been simulated, its cost share is increasing compared to the Base
Scenario.
Interestingly, in the Base Scenario, process costs are almost evenly
shared between the three superordinate main processes Electrode
Production (32%), Cell Production (37%), and Cell Conditioning (31%).
In the Optimized Scenario, the dominance of the Mixing process and the
reduction in Formation & Final Sealing lead to a cost shift from Cell
Conditioning (20%) to Electrode Production (44%). The process cost
share of Cell Production remains at the same magnitude (36%).
Taking all the results into account, for cost reduction in optimized
large-scale battery cell factories, the focus should be on the process steps
Mixing, Coating & Drying, Stacking, Formation & Final sealing and Aging &
Final Control. For this purpose, in the following, process-step-specific
measures for cost reduction are described that complement the previ­
ously described cost-reduction measures.
• Mixing: Measures to reduce the quantity of material to be mixed per
salable cell, such as reducing the solvent quantity; measures to
reduce mixing time, such as optimizing process specification; and
measures to optimize batch capacity. In addition, from a cost
perspective, promising approaches are taken in science and industry
to switch from batch-wise mixing to continuous mixing (Bühler
Group, 2017; Dreger et al., 2015).
• Coating & Drying: Measures to further increase working speed;
measures to increase working width; measures to reduce solvent
quantity up to complete elimination (dry coating); measures to
reduce the number of electrodes, such as increasing the coating
thickness.
• Stacking: Measures to increase the pick and place speed of the
electrodes; measures to reduce positioning time during cell ex­
change; measures to reduce the number of electrodes, such as
increasing the layer thickness.
• Formation & Final Sealing: Optimized cycle programs to reduce
cycle time. Therefore, measures to reduce number of cycles or to
increase the charging rate per cycle are appropriate.
• Aging & Final Control: Measures to reduce aging duration due to
improved quality forecast methods.
Fig. 9 compares the results of the Base Scenario and the Optimized
Scenario with the reported cell costs across battery-related literature.
Therefore, a comprehensive literature review of reported cell cost esti­
mations has been conducted.2 Firstly, as cathode chemistry strongly
influences cell cost, values for NMC have been taken into account
wherever possible to allow for a more precise comparison with the re­
sults of the present study. Secondly, as not all publications report cost
based on cell level (e.g. on pack level), corrective factors have been
calculated (derived from Nelson et al., 2019) and applied to the origi­
nally reported cost values. The underlying data, associated sources and
corrective factors are included in Appendix D. The plot of the reported
values over the years of publication shows a continuously decreasing
cost trend that is characterizing for technologies that are undergoing
mass industrialization. Reported cost estimations range from far above
$200 kWh− 1 in 2015 (Sakti et al., 2015) to below $100 kWh− 1 in 2018
(Schmuch et al., 2018). Furthermore, a tendency to a lower spread be­
tween values can be noticed throughout the years which might result
from increasingly existent and accessible empirical evidence in industry.
The comparison of the derived value of our state-of-the-art oriented Base
Scenario ($106 kWh− 1, horizontal dotted line) with literature values
shows that it is in line with recently reported values albeit it lies on the
2
To survey battery cost literature, 14 publications between 2014 and 2019
have been reviewed. Wherever possible, reported values regarding NMC have
been taken into account to allow for a more precise comparison with the pre­
sent study. If multiple values have been reported (e.g. due to different sce­
narios) an average of those values has been calculated. As values have not
always been reported based on cell level, these have been normalized by
corrective factors derived from Nelson et al. (2019): Reported pack cost have
been multiplied by 0.75 and reported cell material cost have been multiplied by
1.31 to derive a cell-based cost value.
12
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
Fig. 9. Comparison of costs for NMC based battery cells reported in literature over time.
lower end of the range. In contrast, the value of the Optimized Scenario
($64 kWh− 1, horizontal solid line) is significantly lower than historically
published cost estimations. The comparison with the most recent liter­
ature shows that costs of both, the Base Scenario and the Optimized
Scenario, are significantly lower than those reported for example by Ciez
and Whitacre (2017). The main reason for this is that Ciez and Whitacre
used manufacturing parameters based on a much smaller factory size
(0.87 GWh annual production capacity). Although simulation for larger
factory sizes is conducted, cost decrease is limited as the manufacturing
parameters are not adapted. Compared with Nelson et al. (2019), the
values are in a similar range, with costs for the Base Scenario above the
BatPaC range and costs for the Optimized Scenario below the BatPaC
range. In summary, we introduce with the Optimized Scenario a lower
cost level for the manufacturing of battery to literature and give di­
rections which technical parameters reported in literature must there­
fore be industrialized in large-scale.
model that can be used as basis for data-driven decision making within
location decisions, make or buy decisions and to evaluate process and
material alternatives. (3) We introduce and prove the feasibility of a new
and lower cost level which, if materialized, will have a positive effect on
the EV penetration rate. Accordingly, it should be considered in future
research studies and product planning of automotive original equipment
manufacturers cost optimizations.
The presented model has several limitations that need to be
addressed by future research. First, it is limited to lithium-ion battery
technology, which is currently the most beneficial battery technology
for automotive applications (Betz et al., 2019; Bresser et al., 2018b;
Placke et al., 2017; Schmuch et al., 2018). However, there are other
technologies such as lithium metal, solid-state, sodium-ion, lithium||
sulfur, or metal||air batteries with promising advantages for some
characteristics such as costs or safety (Eftekhari and Kim, 2018; Tan
et al., 2017; Zhang et al., 2017) and also for other applications than
automotive. These new technologies are still in the development stage,
and it will take years until they are produced in large scale, but the
presented approach can be adapted to them. Second, the selection of
simulation parameters is focused on the process-related, material &
design-related and location-related parameters that drive cost most. This
cross-category approach gives a comprehensive indication of current
and future cost levels within battery manufacturing but makes no claim
to completeness within each single category. As described in Section 5,
for example, there are also future process improvement possibilities for
the process steps Mixing and Aging & Final Control, which are not
considered in the Optimized-Scenario. Third, although the used pa­
rameters are derived from literature, are validated by industry experts,
and are translated into costs by using an established cost estimation
technique, the calculated costs are not yet validated by empirical data of
a battery cell manufacturer what offers together with the other
mentioned limitations new perspectives for future research.
6. Conclusion
This study at hand successfully applies the process-based costmodelling technique to the manufacture of battery cells. Accordingly,
the study contributes to the research fields of both process-based cost
modelling and battery technology.
The PBCM research field is complemented by the application of the
technique to a complex technology whose manufacturing process con­
sists of a variety of individual, complex, and interdependent process
steps and whose engineering, scientific, and technological principles
have not been fully researched. Therefore, we enlarge the PBCM
framework introduced by Field et al. (2007).
In the field of battery research, we make several contributions (1) We
give directions how further cost reductions within large-scale
manufacturing can be achieved (Optimized Scenario) and which cost
drivers within process steps, cost elements and parameter categories
should be focused for future cost reduction activities. (2) We introduce a
13
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
Declaration of competing interest
related to this manuscript.
The authors of this article states that there are no conflicts of interest
Appendix
Appendix A. Detailed cost model
Overall
The following describes the calculation of the variables, which have an overarching character as they are used as basis for the calculation of various
j
cost elements. The first variable of this type is the annual gross number of units PVEffective that must be produced within process step j to reach the
required number of annual salable units, PVSalable . Therefore, the logic must consider that a process step must compensate for the scrap losses of the
subsequent process steps. This means that process steps located early in the process chain must produce more units than later steps. For the last process
j
step within the presented process chain, the gross number of units required, PVEffective , is calculated as shown in equation (5). For the other process
steps, the gross numbers of units required are calculated as shown in equation (6), wherexj is the machine-specific scrap losses for process step j.
PVSalable
j
)
PVEffective
=(
1 − xj
j+1
PVEffective
j
)
PVEffective
=(
1 − xj
(j = n)
(5)
(j < n)
(6)
A second key variable is the number of required machines, Nj , at process step j. This is calculated by the process-step-specific annual operating time
required to produce the target volume, reqMT j , and the annual available operating time of the machine, availMT j , at process step j. The result is
rounded to the next highest whole number, as it is assumed in the present study that machines may be underutilized as they are not used to produce
other products.
j
NMachine
=
reqMT j
availMT j
(7)
The annual time required to produce the target volume, reqMTj , at process step j is calculated as the product of the associated cycle timeCT j , which
j
is the time interval after one unit is produced, and the associated gross number of cell equivalents required, PVEffective
.
(8)
j
reqMT j = CT j x PVEffective
The annual available operating time of a machine, availMT , at process step j is calculated according to equation (9), where DPY is the operating
days per year, NOS is the number of shifts per day, OHS is the operating hours per shift, UB is the unpaid breaks hours per shift, PB is the paid break
hours per shift, and UDj is the process-specific unplanned downtime hours per shift− 1.
)
(
(9)
availMT j = DPY x NOS x OHS − UB − PB − UDj
j
The costs presented in this study are on a kWh cell energy basis. Therefore, the annual cost for each cost element, ACjElement, must be divided by the
number of annual salable units (number of cells), PVSalable , an the energy content per unit (kWh cell − 1) PCEnergy, as shown in equation (10). Element is
used to differentiate between the cost elements Material, Labor, Energy, Machine, Building, Maintenance, and Overhead.
j
CElement
=
j
ACElement
PVSalable x PCEnergy
(10)
Machines and buildings are capital goods used over a specific period. In the present model, this is considered by distributing the investment in
capital goods such as machines and buildings over their life cycles. In addition, opportunity costs are considered by means of a capital recovery factor.
These costs arise because capital is tied up in machines and buildings and thus cannot be used to generate alternative revenue. The annual allocated
costs Rje for process step j are calculated as shown in equation (11), where Uje is the unit cost for a machine or building, e is used to differentiate between
machine and building, r is the annual discount rate, and Lje is the useable lifetime in years.
j
Rje = Uej
r(1 + r)Le
(11)
j
Le
(1 + r) − 1
Material
j
The calculation of annual material costs, ACMaterial , for process step j considers that several material types z may be used within process step j.
Therefore, annual material costs,
j
ACMaterial ,
are calculated as the sum of the corresponding annual material-type-specific material costs, ACzj, Material , as
shown in equation (12), where z is a specific material type and d is the total number of material types used within process step j.
j
ACMaterial
=
d
∑
(12)
ACj,z Material
z=1
14
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
The annual material costs for a material type z within a process step j, ACzj,Material , are calculated using equation (13), where Mzj,Material is the net mass
z
of material type z required, within process step j, to produce one unit; UMaterial
is the unit cost of material type z (usually currency weight− 1);
j
is the process-step-specific annual gross
Scrapzj,Material is the percentage of specific losses of additional material type z within process step j; and PVEffective
number of units required.
z
ACj,Material
z
z
= Mj,Material
x UMaterial
x
total
1
j
x PVEffective
1 − Scrapzj,Material
(13)
To ensure a holistic calculation and enable computation of the cost elements for Energy CjEnergy and Overhead CjOverhead , total annual material costs,
were calculated in the present cost model as shown in equation (13). For evaluation, we focused on the part of the annual material costs
ACzj,Material total ,
that are potentially influenced by production, which, in principle, is the part induced by scrap losses. Annual material-scrap costs, ACzj,Material
scrap ,
are
calculated as shown in equation (14). This approach ensures the focus of the evaluation remains on the costs relevant to manufacturing. Otherwise, the
results would be overshadowed by the part of material costs that is not potentially influenced by production, as this is dominant within battery cells.
z
ACj,Material
(14)
j
z
z
= Mj,Material
x UMaterial
x Scrapzj,Material x PVEffective
scrap
Labor
j
As shown in equation (15), the labor costs for process step j are calculated as the product of the number of machines required for that step, NMachine ,
the number of laborers required per machine,
operating time, APOT j .
j
NLaboreres per Machine ,
the unit cost of labor,
j
ULabor
(usually in currency per unit time), and the annual paid
(15)
j
j
j
j
j
ACLabor
= NMachine
x NLaboreres
= Supporting)
per Machine x ULabor x APOT ; (j ∕
Labor costs for process step j = Supporting are calculated differently, since this process step cannot be described on a machine basis. Nevertheless,
the same calculation logic is used, referring to reference bases (e.g., annual GWh plant capacity) instead of machines. As process step j = Supporting
consists of various sub-processes with different characteristics, the reference bases were built to be specific to each sub-process step. For each reference
base and sub-process step, the necessary number of laborers was determined. The following reference bases were used: annual factory capacity for the
sub-process - Inter-process material handling, Control lab, and Receiving and shipping including scrap recycle; annual amount of NMP solvent used for the
sub-process step -Solvent recovery; and footprint required for the processes to be carried out under dry room conditions (cell production) for the subprocess step -Dry room management. Based on this, the labor costs of process step j = Supporting comprise the sum of the labor costs of the subjs
processes, as shown in equation (16), where NRBs
is the number of reference bases required; NjsLaboreres per RB is the number of laborers required per
reference base; UjLabor is the unit cost of labor (usually in currency unit time− 1); APOTjs is the annual paid operating time; js is a specific sub-process of
process step j = Supporting, and ns is the total number of sub-processes of process step j = Supporting.
j
ACLabor
=
ns
∑
(16)
js
js
js
js
NRBs
x NLaboreres
per RB x ULabor x APOT ; (j = Supporting)
js
Machine
j
As shown in equation (17), the annual machine costs ACMachine for process step j ∕
= Supporting are calculated as the product of the annualized
equivalent of the machine investment
of produced units,
j
PVEffective .
j
RMachine
j
and the number of machines NMachine required to produce the process-step-specific gross target number
(17)
j
j
ACMachine
= NMachine
x RjMachine ; (j ∕
= Supporting)
As described in the section on the labor cost element, process step j = Supporting cannot be described on a machine basis. Therefore, the cost
element for this process step, ACj=Supporting
, is calculated using the described reference bases, where NjsRBs is the number of reference bases required;
Machine
RjsRB Machine is the annualized equivalent of the machine investment per reference base; js is a specific sub-process of process step j = Supporting; and ns is
the total number of sub-processes of process step j = Supporting. The following equation describes the calculation:
j=Supporting
ACMachine
=
ns
∑
js
NRBs
x RjsRB
(18)
Machine ; (j = Supporting)
js
Building
j
As shown in equation (19), the annual building costs for process step j ∕
= Supporting, ACBuilding , are calculated as the product of the annualized
equivalent of building investment, RBuilding ($ m− 2), the number of machines required to produce the process-step-specific gross target number of cell
j
equivalents,
j
NMachine ,
j
and the footprint per machine, FPMachine (m2 machine− 1).
(19)
j
j
ACBuilding
= RjBuilding x NMachine
x FPjMachine ; (j ∕
= Supporting)
As shown in equation (20), the building costs for process step j = Supporting, CjBuilding are again calculated using the reference bases, where NjsRBs is
the number of reference bases required;FPjsRB is the footprint per corresponding reference base; js is a specific sub-process of process step j = Supporting;
15
F. Duffner et al.
International Journal of Production Economics 232 (2021) 107982
and ns is the total number of sub-processes of process step j = Supporting.
j=Supporting
=
CBuilding
d
∑
(20)
js
RjBuilding x NRBs
x FPjsRB ; (j = Supporting)
z=1
Energy, maintenance, and overhead
j
j
j
As shown in equations (21)–(23), the annual costs for Energy ACEnergy , Maintenance ACMaintenance , and Overhead ACOverhead for process step j are
j
calculated based on the percentage of surcharge rates SR. The surcharge rates are applied to known cost elements (e.g., Machine ACMachine ). Both the
surcharge rates SR and associated application bases are specific for the production of battery cells.
)
( j
j
j
x SREnergy
ACEnergy
= ACLabor
+ ACMaterial
(21)
j
j
ACMaintenance
= ACMachine
x SRMaintenance
(22)
)
(
j
j
j
j
ACOverhead
x SROverhead
= ACMachine
+ ACBuilding
+ ACMaintenance
(23)
Appendix B. Overlapping phenomena between simulation parameters
Fig. 10. Pairwise combination of simulation parameters and resulting effect on cell cost.
Appendix C. Sensitivity analysis3
3
For each simulation-parameter the improvement between Base Scenario and Optimized Scenario has been calculated. To account for uncertainty regarding the
extend of achievement has been varied. (1) Only 80% or (2) 120% of improvement can be materialized. The resulting impacts on cell costs are displayed in the
tornado plot. [2] Usage of Alternative solvent & [7] Material change from NMC622 to NMC811 have been excluded from the analysis as the parameter value can
either be yes or no in both cases
16
International Journal of Production Economics 232 (2021) 107982
F. Duffner et al.
Fig. 11. Tornado plot showing the sensitivity of simulation-parameters.
Appendix D. Reported cell cost estimations in literature
Table 4
Source, year of publication and reported battery cost
Source
Year
Cathode
material
Cost
Base
Reported cost [$ kWh− 1]
Average value [$
kWh− 1]
Corrective Factor
[-]
Resulting cell cost [$
kWh− 1]
Gallagher et al.
Patry et al.
Sakti et al.
Berg et al.
Nelson et al.
Wood et al.
Schünemann
2014
2015
2015
2015
2015
2015
2015
NMC
NMC
NMC
NMC
NMC
NMC
NMC
Pack
Cell
Pack
Cell
Pack
Pack
Cell
234
251
341
224
188
437
203
0.75
–
0.75
–
0.75
0.75
–
175
251
256
224
141
327
203
Petri
2015
NMC
122
1.31
159
Ciez and
Whitacre
Ahmed et al.
Berckmans et al.
Schmuch et al.
2017
NMC
Cell
mat.
Cell
178; 289
306; 231; 231; 239; 247
545; 325; 265; 230
224
188
503; 370
189; 173; 163; 156; 189; 188; 188; 187; 292;
267; 245
122
244; 182; 174
200
–
200
2017
2017
2018
NMC
NMC
NMC 811
148; 155; 139; 146
432; 300; 293
79; 68
147
342
73
0.75
0.75
1.31
110
256
96
Wentker et al.
2019
NMC 811
Pack
Pack
Cell
mat.
Pack
142
0.75
106
Nelson et al.
2019
NMC 811
Cell
179; 167; 154; 143; 163; 152; 139; 129; 134;
125; 113; 103
127; 109; 106; 103; 132; 105; 101
112
–
112
Funding sources
This work was partly funded and supported by the Bundesministerium für Bildung und Forschung (BMBF) and the Ministerium für Wirtschaft,
Innovation, Digitalisierung und Energie des Landes Nordrhein-Westfalen within the project BenchBatt [03XP0047A].
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