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Energy Procedia 00 (2018) 000–000
ScienceDirect
ScienceDirect
www.elsevier.com/locate/procedia
Energy
(2019) 000–000
168–173
EnergyProcedia
Procedia159
00 (2017)
www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids,
REM 2018, 29–30 September 2018, Rhodes, Greece
The 15th International
Symposium
on District
Heating
and Cooling
Lithium-ion
battery
modeling
based
on Big
Data
Shuangqi
Li, Jianwei Li,
He*,heat
Hanxiao
Wang
Assessing
the feasibility
ofHongwen
using the
demand-outdoor
National
Engineering Laboratory
for Electric Vehicles,
of Mechanical Engineering,
Beijing
Institute
of Technology, Beijing
100081,
temperature
function
for aSchool
long-term
district
heat
demand
forecast
China
I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
a
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
Abstract
b
Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France
Département
Systèmes
et Environnement
- IMT Atlantique,
4 rue Alfred
Kastler,
Nantes,
France to monitor
Battery is the bottleneck technologyÉnergétiques
of electric vehicles.
The complex
chemical reactions
inside
the44300
battery
are difficult
c
directly. The establishment of a precise mathematical model for the battery is of great significance in ensuring the secure and stable
operation of the battery management system. First of all, a data cleaning method based on machine learning is put forward, which
is applicable to the characteristics of big data from batteries in electric vehicles. Secondly, this paper establishes a lithium-ion
Abstract
battery model based on deep learning algorithm and the error of model based on different algorithms is compared. The data of
electric buses are used for validating the effectiveness of the model. The result shows that the data cleaning method achieves good
District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the
results, in the case of the terminal voltage missing, the mean absolute percentage error of filling is within 4%, and the battery
greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat
modeling method in this paper is able to simulate the battery characteristics accurately, and the mean absolute percentage error of
sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease,
the terminal voltage estimation is within 2.5%.
prolonging the investment return period.
The
main
scope of this
paper isby
to assess the
feasibility of using the heat demand – outdoor temperature function for heat demand
©
2019
The
Published
Ltd.
Copyright
©Authors.
2018 Elsevier
Ltd. AllElsevier
rights reserved.
forecast.
The district
of Alvalade,
located
in Lisbon (Portugal),
was used as a case study. The district is consisted of 665
This
is an open
access article
under the
CC-BY-NC-ND
license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum,
buildings and
that peer-review
vary in bothunder
construction
period of
andthe
typology.
Three
weatherofscenarios
(low,Energy
medium,
high) and and
threeForum,
district
Selection
responsibility
scientific
committee
the Applied
Symposium
Renewable
Energy Integration
with
REM
2018.
Renewable
Integration
withMini/Microgrids,
Mini/Microgrids,
REM
2018.
renovation Energy
scenarios
were developed
(shallow, intermediate,
deep). To estimate the error, obtained heat demand values were
compared with results from a dynamic heat demand model, previously developed and validated by the authors.
Keywords: electric vehicle; lithium-ion power battery; modeling; battery management; bigdata; deeplearning;
The results showed that when only weather change is considered, the margin of error could be acceptable for some applications
(the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation
scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered).
1. Introduction
The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the
decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and
The battery
and its
management
system
thefunction
most significant
components
of the electric
vehicles
(EVs), on
with
renovation
scenarios
considered).
On the
otherare
hand,
intercept increased
for 7.8-12.7%
per decade
(depending
the
great
impact
on the The
efficiency
and safetycould
of thebeEVs.
research
the battery
and its
mainly
coupled
scenarios).
values suggested
used The
to modify
the of
function
parameters
formanagement
the scenariossystem
considered,
and
focus
on the
theaccuracy
parameter
identification,
State of Charge (SoC) estimation and fault detection based on the equivalent
improve
of heat
demand estimations.
© 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and
Cooling.
* Corresponding author. Tel.: +86-010-68914842; fax: +86-010-68914842.
E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy
Integration with Mini/Microgrids, REM 2018.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
1876-6102 © 2019 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
This is an open access article under the CC-BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy
Integration with Mini/Microgrids, REM 2018.
10.1016/j.egypro.2018.12.046
2
Shuangqi Li et al. / Energy Procedia 159 (2019) 168–173
Shuangqi Li / Energy Procedia 00 (2018) 000–000
169
circuit model and electrochemical model [1]. However, these models cannot adapt to changes of battery life and
environment, which have effect on the normal operation of the battery management system [2]. Therefore, in order to
improve the safety and stability of the battery management system, it is extremely important to put forward a battery
model that is able to work stably in whole life cycle and multi-variable environment.
In previous work, the neural network algorithm has been widely used in battery modeling. A black box model for
the battery based on neural network algorithm is established by Kang L W et al. [3], who designed a model that can
accurately represent the highly nonlinear mapping between the input and output of the battery, but a
tremendous amount of data is needed for improving the accuracy and adaptability of the model. However, with the
increase of data volume, the training process of neural network becomes complicated, and there is a serious overfitting problem in the case of poor data quality.
For improving the accuracy of the battery model, Haq I N et al [4] used the Support Vector Regression (SVR)
algorithm to model the battery, and achieved effective results in experimental stage. However, as the amount of
training data increasing, the training time of the model increases explosively, making it difficult to adapt to the big
data environment.
Based on the aforementioned discussion, the contributions of this paper are in 3-fold: (1) A data cleaning method
based on machine learning algorithm is proposed to improve the quality of the data; (2) A lithium-ion battery model
based on deep learning algorithm is established ; (3) The error of model based on different algorithms is compared.
2. Methodology
2.1. Short description of SVR
The SVR is an algorithm for solving the regression problem which adheres to the principle of minimizing structural
risks [5]. SVR makes use of a kernel function to map data into high-dimensional space, where a line can be used to fit
the data points. The loss function of SVR allows the model to have an acceptable error for the training data, meanwhile,
SVR uses slack variables in the training process, which will prevent the model from being affected by the dead pixel
data. Since SVR algorithm can effectively prevent the model from over-fitting, it obtain a better effect than Back
Propagation neural network when processing the data which is polluted by noise or bad points [6].
2.2. Short description of SDAE
In contrast to neural network algorithm, the highly nonlinear mapping between input and output can be simulated
by deep learning algorithm effectively. At present, deep learning algorithms are widely used in electric load
forecasting [7, 8], traffic speed prediction [9], energy management system [10], etc.
Stacked Denoising Autoencoder (SDAE) is a semi-supervised learning model. The input data is reconstructed from
the bottom up, and the deep structure features of the data are continuously extracted while the effective information
in the data is retained, and the re-expressed data is preferable for further processing [11]. Automatic encoder (AE) is
the basic unit of SDAE, which is a single hidden layer neural network.
At present, automatic encoders are mostly used to reduce the dimension of input data [12]. It can, however, also be
used to find structural features to enrich that information of the input data when the sparse constraints are added to the
hiding layer, which means that most of the neuron outputs are 0. The relative entropy is used as sparsely restriction,
and it can be depicted as below:
1− ρ
ρ
ρˆ ) ρ log + (1 − ρ ) log
KL ( ρ ||=
(1)
ρˆ
1 − ρˆ
Where ρ represents the sparse target value and ρ̂ represents the average activation rate of neurons, when ρ = ρˆ
the value of the relative entropy function is 0.
The Denoising Autoencoder (DAE) trains the automatic encoder with the contaminated data. Therefore, the model
can not only filter out the noise in the data, but also extract the structure features of the data.
Shuangqi Li et al. / Energy Procedia 159 (2019) 168–173
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170
3
Extreme Learning Machine (ELM) is a kind of machine learning algorithm to solve the problem of regression, the
core of the ELM algorithm is the linear regression layer, in which the regression problem is converted to the problem
of finding least-squares solution for linear system, the weight matrix can be obtained directly by the formula below:
βˆ = X + T
(2)
Where X and T respectively represent the input and the output data, β̂ is weight of the network.
SDAE-ELM is one of the most commonly models in deep learning, training the SDAE layer-by-layer with he nonlabeled data , and then using the label data to train the ELM directly.
2.3. Model development
Battery data is characterized by large volume, low value density and error, etc. Therefore, the data cleaning is an
important process before modeling the battery. But the operating conditions of EVs are complex and changeable and
the battery is a highly nonlinear system, the data preprocessing method based on statistical methods can hardly work
effectively, so a data cleaning method for the big data of electric vehicles based on machine learning algorithm is
proposed in this section.
Fig. 1. The process of data cleaning.
The SVR algorithm is used to fill in the missing data (The current or the terminal voltage), a complete battery state
is formed and the data is sent back to the database for further mining. The process of data cleaning in showed in Figure
1.
Fig. 2. Black-box model for the battery.
The chemical reaction in Lithium-ion battery is extremely complex and it is difficult to be monitored directly, so a
black-box model is established for the battery in this paper. The modeling process is shown in Figure 2.The current,
temperature, SoC are used as inputs, and the terminal voltage of the battery is used as output in this paper, the purpose
of the neural network is to approximate the function:
4
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Li et al.Procedia
/ Energy Procedia
(2019) 168–173
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Li / Energy
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000–000
U = f ( SOC , I , T )
171
(3)
The method for model training is shown in sections 2-2.In the process of the training, the residual A and residual
B are recorded. When the residual A is less than the critical value or reaches the maximum iterations, the parameters
of the network are assigned to the black box model. Residual B is used to determine whether the model is over-fitting.
3. Result and discussion
3.1. Basis data and the result of data cleaning
This paper collects and stores a large amount of operational data including the Terminal voltage, the SoC, the
temperature and the current information for the battery of electrical buses. But limited by the calculating ability of the
simulation environment, only a small part of the data is selected to test the models and algorithms proposed in this
paper.
Fig 3. The data used in this paper.
Shuangqi Li et al. / Energy Procedia 159 (2019) 168–173
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5
As shown in Figure 3, after eliminating the charging data in the database, this paper selects 15000 sets of data for
model training and testing, among which 1-10000 are used as training data and 10001-15000 are used as test data for
the Black-box Model of the battery.
In the experiment of missing data filling, the loss ratio of the data are 1/5, and the missing variables are terminal
voltage. The SVR algorithm is used to fill in the terminal voltage in this paper.
Owing to SVR’s higher robustness to the noise and the error in the data, it performs well on the experimental data,
the maximum relative error is within 4%.
3.2. Result for the battery modeling
The battery is modeled based on the method mentioned in Section 2-5, and the test-data are used to verify the
accuracy of the model, and the error of the model established by different algorithms is shown in Table 1, the accuracy
of the black-box model is shown in figure 4.
Table 1. The error of different algorithms.
Model
MAPE(%)
Std
BP neural network
4.15
2.3782
ELM
3.68
2.6414
SVR
3.17
1.7283
SDAE+ELM
2.42
1.5452
Fig. 4. The output of the black-box model for battery.
The BP neural network and the ELM algorithm are difficult to fit the complex functions, but compared with BP
neural network, ELM algorithm is faster, so it can be used with other algorithms to form a complex model. The SDAEELM model is able to extract the deep structure features of the data, so its accuracy and stability have been significantly
improved.
The error of the model can be further used to mine the fault information of the power battery and its management
system, and provide a data foundation for fault diagnosis.
4. Conclusions and outlook
This paper presents a lithium-ion battery model based on deep learning algorithm, which can adapt to the big data
environment. Based on machine learning algorithm, the data cleaning method achieves good results, in the case of
the terminal voltage missing, the mean absolute percentage error of filling is within 4%, which has a greater effect on
promoting the overall quality of the data set. The battery modeling method based on deep-learning algorithm is able
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to extract the deep structure features of the data effectively, and the results show that the deep-learning algorithm is
able to reduce the error of the model and achieve a high-precision simulation for the dynamic characteristics of the
battery effectively, and mean absolute percentage error of the model is within 2.5%.
Future work can be conducted by the collection and storage of the big data, the benefit of improving the quality
of data are much greater than that of the algorithms, and high-quality data is the basis for precise modeling.
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