EV Battery State of Charge: Neural network based estimation
A. Affanni, A. Bellini, C. Concari, G. Franceschini, E. Lorenzani, C. Tassoni
Dipartimento di Ingegneria dell’lnformazione
University of Parma
Parco Area delle Scienze, 181iA
43100 Parma, ITALY
A b m a n Different Electric Vehicles (EV) types have been
recently developed with the aim of solving pollution problems
caused by the emission of gasolinepowered engines.
Environmental Considerations promote the adoption 01EV for
urban transportation. As it is well known one of the weakest
points ofelectric vehicle is the battery system. Vehicle autonomy
and therefore accurate detection of battery state of charge are
among the main drawbacks that prevent he spread of electric
vehicles in the consumer market.
This paper deals with the analysis of battery state of charge:
performances of B few sizes of batteries are analyzed and their
state of charge is estimated with a Neural Network (NN)bared
system. The obtained results have been used to design a ion.
lithium battery pack suitable lor electric vehicles. The proposed
System presents high capability of energy recovering in braking
conditions, together with charge equalization, over and under
voltage protection. Moreover a Neural Network based
estimation of battery state of charge has been implemented in
order to optimize autonomy instead of perfarmanas or viceversa depending on journey.
simple and effective implementation. Experiments were made
on different discharge profile modeled on the typical urban
EV duty cycle.
Then a battery pack has been designed and realized. It
performs battery equalization and protection in order to
performenergy recovering during vehicle braking operation.
Moreover battery State of charge is continuously monitored
allowing different control strategy depending on joumey or
dnver requirements. These battery packs will he s e d to
supply a high performance electric scooter [SI.
~
1.
NTRODUCTION
Different Electric Vehicles (EV) types have been recently
developed with the aim of solving polluCon problems caused
by the emission of gasolinepowered engines [l]. As far as
overland transports are concemed large range requirements
lead to hybrid vehicles solution while environmental
considerations promote the- adoption of EV for urban
transportation [2]. As it is well known one of the weakest
points of electric vehicle is the battery system. Vehicle
autonomy and therefore accurate detection of battery State of
Charge (SoC) are among the main drawbacks that prevent the
spreadofelechic vehicles in the consumer market [3], [4].
The aim of the paper is to obtain a simple and reliable
method to estimate-the battery state of charge using only the
electrical quantities usually available. State of charge
information allows tuning control strategy according with
journey characteristics. This allows for example the
optimization of battery lifespan instead of useless
performances in case of urban journey. The proposed method
includes a neural network based estimator in order to comply
with the non linear behavior of the battery and to provide a
11.
BAITERIES MODELIZATlON FOR EV
The experiments are referred to Lithium batteries whose
main features are briefly reviewed in the following.
Lithium batteries can be divided into: LPB (Lithium
polymer) batteries, and Ion Lithium batteries. The formee
feature the best energy density values. Specifically they
provide 170 W K g , however they are very expensive. The
authors chose Ion lithium cells nevertheless the lower energy
density and the stronger self discharge, because of their cost
g
and their weight. Specifically they provide 140 M
(Cobalt), 120 Wh/Kg (Manganese).
During the experiments the following sizes of Lithiumlon
battery cells were used: 3.7 V, 1.5 Ah and 3.7 V, I O Ah. The
battery voltages are constrained during discharging operation
to a minimum level of 2V in order to prevent capacity loss.
As already stated, batteries are one among the weakest part .
of an electric vehicle. Limited autonomy and recharge
facilities are some of the main drawbacks. Therefore
exploiting the battery up to its end is a key element, and an
accurate howledge of current State of Charge is needed.
Accurate estimation of SoC is a complex task. To accomplish
it a few approaches are possible: circuit nodels, empirical
models, statistical or A.I. based models [61, [71, [SI,[9].
Circuit models require a very heavy job for each type of
battery, and require noElinear elements. Classical methods
-for SoC estimation include: measurements of the extracted
charge, measurements of the battery intemal impedance or
resistance, measurements of the battery no-load voltage.
Though none of them provides acceptable results, a hybrid
method, including measurements of intemal impedance or
resistance &), extracted charge
and no-load voltage
a)
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Vim), can achieve a better estimation of Soc.
Fig. 1 refers to the 1.5Ah battery cell, it shows the R,
profile as a function of time: the profile is almost constant
during battery discharge increasing suddenly close to the end
of discharge. Therefore this profile, alone, is not sufficient to
evaluate the SOC.
Fig. 2 refers to the lOAh battery cell, it shows a discharge
profile as a function of time at constant discharge current
can be computed using this
equal to the battery capacity.
profile howeverthis quantity is not sufficient to state SOC.
The knowledge of the amount of energy that can be
extracted from battery is a key element in order m optimize
system performances. This information can be obtained
starting from SoC only if battery discharge status is fully
defined. A battery can be defined completely discharged
when it cannot provide the power needed by the system it
supplies.
Therefore an accurate estimation of S o C should be made
only fully discharging the battery. This procedure is
impractical because of the large amount of time required.
The authors’ assumption is that SoC decreases linearly
with time ifdischarge occurs with a periodical current profile.
This simple approximation was then verified a posteriori by
laboratory tests.
Starting from this assumption the Authors propose a NN
based procedure based that allows the estimation SoC
mapped as a functionf of fem, Qe audRi .
a
..
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0
50 I K b I M O 2b.l 2.503 30% 3504wo45W
r a [SI
Figure 2: lOAh battery cell discharge profile.
As usual the neural network estimation is more accurate if
the training set is large and fully representative of different
operating conditions. Therefore the behavior o f f was
analyzed in several different conditions, performing several
different discharge cycles.
Specifically the test set-up must allow discharging the
battery cell with a given current to measure cell voltage and
current during discharge. Fig. 3 shows a current profile
chosen accordingly to the typical E V urban duty cycle for a
single cell.
The test set-up for the experiments was made by a
Programmable current Generator HP6684A, IOOA 60V; a
Data-logger HP 3 4 9 7 0 4 a host PC with IEEE-488 interface
and suitable LabView software. Two GUls were designed in
LabView which allow to specify the desired current profile
during battery discharge, s e t u p the measurements, store
collected data on the host PC. Starting from measured data
(battery current and voltage during discharge) a MATLAB
script is used to produce the training set.
Qe is computed with the composite trapezoidal
approximation from current sampling.
Ri is computed measuring the voltage drop A V
corresponding to a current variationdl: R( =&
!.!
Time [sec]
AI
Figure 1: 1.5Ah battery cell Rprofile.
111. QATE OF CHARGE ESTrmATlON
The aim is to find a function f so that SoC=f(lem,aRi ).
As stated before fcan be obtained from an analytical study of
physical and chemical behavior of batteries, or it can be
mapped from empirical data. The latter approach tums out to
be preferable, since it is simpler and often more efficient. In
this paper an empirical estimation o f f was made, using a
neural network, which maps the input vector: /em,
R, into
the state of charge (SoC) [9], [IO].
a
Finally feemwas estimated directly as the voltage with noload in time intervals where the current is zero.
The SoC values suitable for NN training are obtained in
accordance with the previous hypothesis on cell discharge
behavior.
N. EXPERIMENTAL RESULTS
The collected training data were used to train a two-layer
neural network with tansig activation function. The leaming
procedure uses the BFGS algorithm (Broyden, Fletcher,
GoldfarbeShanno)[lO],[ll].
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Figure 3: Typical discharge current profile as a function of
time for a single cell
The trained NN was then used as a real time estimator of
battery state of charge. To validate the estimation results
different tests were performed changing the discharging
conditions. Specifically the current profile has been varied
during every validation test in order to prove the NN
performances in operationsclose to'the real ones.
Each test was stopped after a consistent number of cycles,
the banery was fully discharged and the measure of the
extracted charge was compared with NN output. The two
different IonLithium battery cells detailed in section I1 were
used.
As an example Fig. 4 shows the estimated normalized SoC
compared with the current state of charge during the
discharge of the smallest Ion-Lithium battery. The curves are
almost completely overlapped, therefore in Fig. 5 an enlarged
version of the same discharge process is reported.
Figure 5 Neural network estimation (light grey) compared
with current SOC.
Several discharge cycles were repeated with the described
test set-up, obtaining a good agreement between neural
network estimation and measured results. Specifically tab. 1
reports the results in terms of Mean Square errors and
maximum errors for different tests performed with different
discharge current profiles.
Test
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2
3
4
5
MeansquareError
2.9R71r-4
5.0878e-3
2.9377e-3
4.2381e-3
3.2647e-3
I . Maximumemr
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0.030
0.024
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Figure 4 Neural network estimation compared with current
SOC
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among the ten cells to a DSP by means of its output
impedance.
The block diagram in fig. 6 shows the structure of the
board.
2.4"
3.2v
3.6"
Cdl "oh$* I y O h , ]
1"
4.4v
4.8V
Figure 7 Optocouplers input voltage as a function of
voltage across the cell.
Figure 6 Block diagram of the battery pack control board.
Normal operating voltage range of each cell is [3V,
4.15V1. When the voltage exceeds this range undervoltage
and overvoltage protection circuits start to operate by sending
the actual cell voltage level to the control by the optocoupler.
Specifically in overvoltage operation range [4.15V,
4.25V], overvoltage circuits produce a linear voltage
depending on the chargestate of the relevant cell.
With a dual behavior in undervoltage operation range
[2.7V, 3V], undervoltage circuits produce a linear voltage
depending on the chargestate of the relevant cell.
When two or more cells are operating in a non-optimal
voltage, the control board acts as an e x a and sends the
output of the sensing circuit which shows the worst case of
chargestate.
The temperature sensor guarantees that the battery pack
temperature does not overcome 7O0C, in order to prevent the
damage of the cells.
The outputs of the above mentioned protection circuits are
sent through a proper interface (implemented with an a m y of
optocouplers) to the motion control DSP allowing tuning
control strategy according with journey characteristics.
The chargPequalirers allow the maximum energy storage
in the battery pack shunting the current of the cell at the
threshold of overvoltage during the charge.
It is remarkable to observe that the circuits which sense the
overvoltage and undervoltage state do not suffer from
commommode voltage; so that each sensor observes the
voltage across the relevant cell independently of the position
of the cell inside the pack.
Extensive simulations were performed to validate the
behavior of the protection circuits. Fig. 7 shows the
simulation of the input voltage of the optocouplers as a
function of the voltage across a cell. Simulations were
performed with a DC input sweep between 2.4 V and 4.8V
superimposed by a ripple which models the disturbance
caused by the current requested by the motor.
2.w
The input of optocoupler is at high level if each cell is in
the optimal voltage range [3V, 4.15VI and falls with linearity
if the voltage across a.cell exceeds this range. The circuit
does not suffer from the disturbance introduced in the
simulation, as the output is function of the average input.
The slopes of,overvoltage and undervoltage transitions are
different, because of the different priorities. In fact when a
cell is in overvoltage condition the DSP must stop the charge
at constant current starting the charge at constant voltage. On
the other side when a cell is in undervoltage condition the
DSP must reduce the current requested by the user providing
less acceleration in order to,guarantee the best autonomy of
the vehicle allowing a larger margin ofbattery-safety.
The choice of the components of the control circuit was
driven by the needs of low-power consumption of the board
(900mW). Because of the large number of devices, they have
been placed onto two faces into a four layers PCB.
The conformation and the size of the board are related to
the size of the battery pack composed by ten cells, in fact it
underlies the board in order to reduce the pack volume.
The size of the board is 201mm x 54mm (7.91 x 2.12
inches) and a global view is reported on fig 8.
Figure 8 Control board layout
The pads which provide the voltage of each cell have been
placed in order to reach the cells connectors by screwed bars
to make easier the assembly.
The structure of the battery pack is shown in fig 9.
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.
I
I--
The proposed control board was embedded in a prototype
electric scwter in order to increase its reliability and
autonomy.
201 mn
Figure 9 Battery pack lay-out
CONCLUSIONS
This paper presents a simple approach for protection and
estimation of State of Charge goC) of batteries tailored for
electric vehicles applications. The proposed estimation is
based only on the measurement of electrical quantities like
/em, g, Ri usually available without introducing further
invasive sensors. The data input set for training a NN is built
relying on experimental measurements of the aforementioned
electrical quantities. The NN is a two layer one with wellstated activation function and leaming algorithm. The real
time measurements of battery charge has been performed in
order to validate NN based estimation results. It can be noted
that this approach does not require any physical or chemical
knowledge of battery behavior. Experiments show that the
method is reliable and accurate.
Electronic circuits for charge equalization, undervoltage
and overvoltage monitoring, temperature sensing were
designed and implemented o n a dedicated PCB. Their outputs
drive motor control strategy according with joumey
characteristics. The implemented charge equalization strategy
allows to obtain the maximum energy storage also during
vehicle braking operation.
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