This presentation explains the resent methodologies utilized to estimate the Battery State of Charge with the help of a Data-Driven and Filter-Based approach for enhancing the performance parameters of a Battery Management System.

1. Battery State of Charge (SOC) Estimation Techniques
using the Data Driven and Filter Based Approach to
Enhance the Performance Parameters of a Battery
Management System (BMS).
Prepared by –
Jeet Patel, Bhaumik Joshi, Siddhi Vinayak Pandey
Department of Electrical Engineering,
Adani Institute of Infrastructure Engineering
Ahmedabad, Gujarat, India-382421.

2. Contents
 Introduction
 Why and How BMS?
 What is SOC and SOH and why are they important?
 SOC estimation using Data Driven approach
 SOC estimation using Filter based approach
 Conclusion
 References

3. Function of Battery Management System
A BMS has the following priorities:
 Protects safety of the operators from the host applications.
 Detects unsafe operating conditions and responds Protects cells of battery
from damage in abuse/failure cases Prolongs life of battery (normal
operating cases)
 Maintains battery in a state in which it can fulfill its functional design
requirements Informs the host-application control computer
 how to make the best use of the pack right now (e.g., power limits),
control charger, etc.

4. State of Health
 State of health determines the health of the battery i.e. how long the battery lasts.
 There is no device to directly measure state of health of a battery.
 It is measured using the values on voltage, current, energy density, power density,
temperature etc.
 SOH is used for learning how much abuse of a battery is done by the user.
 SOH predicts how much charge-discharge cycles are left in the battery before it
becomes futile.

5. State of Charge
 State of charge determines the amount of charge present in the battery at a point
of time.
 As like the SOH; SOC cannot be measured directly using a single device. There are
special techniques used to measure SOC which will be discussed later.
 SOC is most important parameter of a battery. It is basically used to measure how
much power is left in the battery, which is used to estimate that how long the
battery will last before it is completely discharged.
 In other words how much time it can deliver the power to a given load.

6. SOC Estimation Using Data Driven Approach
 As of today, with huge computation power and lots of data available
about the battery, such as battery voltage, current value, temperature,
cycle number, etc.
 Few Algorithms which will be covered in the next few slides are:
 SVM (Support Vector Machine)
 Neural Networks
 Recurrent Neural Networks
 Reinforcement Learning

7. SVM (Support Vector Machine)
 This algorithm tries to find the ideal boundary in the Nth
dimensional, which easily separates your dataset as shown in
below figure.
 Kernel are used to take data as input and transform it into the
required form. For example Linear, Polynomial etc.

8. Neural Networks
 This is a complex algorithm, derived from the most complex thing
in the universe, “The Brain”.
 It takes N-Dimensional input and tries to find the pattern in the
given input, output.
 The network shown in the figure has 3 layers Input layer, Hidden
layer, and Output layer. As shown in below figure -

9. Recurrent Neural Network
 An efficient method for predicting the time-series data.
 It considers the relevant data taken from the past or earlier states for
estimation; which has been shown in below figure-

10. Reinforcement Learning
 If there is less amount of data. This algorithm can come in handy.
 In this algorithm, predictions are made in real-time and after some
iterations, the accurate predictions are made.
 This algorithm decides suitable actions to be taken to maximize the
output.

11. SOC estimation using Filter Based
approach
 Authentic Time Observer based series
 Real time estimation of SOC and SOH
 Kalman Filter (KF) for linear estimation
 Extended Kalman Filter (EKF) and Double Extended Kalman Filter
(DEKF) for Nonlinear Systems.
 Unscented Kalman Filter (UKF) / Sigma Point Kalman Filter (SPKF) for
highly nonlinear systems.

12. SOC estimation using Kalman Filter
 Prediction of (k) based on (k-1….n).
 Based on current state (k) of V,I and T; the %SOC is calculated [d].
 Combination with Coulomb Counting technique, Kalman filter (KF)
estimates the accurate overvoltage and current in real time domain [e]
[f].
 Estimation of overvoltage and current is done by combining the
Coulomb counting with linear KF.
 It also estimates the SOC (better than the individual Coulomb counting
based estimations).

13. SOC estimation using Kalman Filter
 As mentioned in below graph; the %SOC utilizing the Kalman Filter based
approach has been estimated [g] –

14. SOC estimation using Extended Kalman
Filter
 The Auto Regressive Exogenous (ARX) battery model with EKF is
utilized for accurate % SOC estimation of a Lithium-ion based
battery [h].
 Temperature incrementation causes the enhancement in the
nonlinearity within a battery model [h].
 The EKF decreases the extreme terminal voltage error from 0.0397
to 0.0165 V. While the mean error value drops from 0.0014 to
0.0007 V [h].

15. SOC estimation using Extended Kalman
Filter
 The graphical representation of ARX battery model with EKF is mentioned in below
figure [h] -

16. SOC estimation using Unscented Kalman
Filter
 Sigma point is more efficient compare to mean and approximation method for the
estimation of %SOC.
 The UKF filter for SOC estimation was designed using the posterior mean and
Taylor series expansion method [i].
 When UKF implemented for battery cell model, the robustness for faster
convergence tracking ability of SOC is found very accurate[j].
 When the LS-UPF (Least Squared Unscented Particle Filter) is compared with GA-
UPF (Genetic Algorithm and Unscented Particle Filter); performs the better %SOC
estimation utilizing the UKF technique.

17. SOC estimation using Unscented Kalman
Filter
 SOC estimation through UKF/SPKF for faster convergence tracking ability is explained in
below attached figure [j] –

18. References
 [a] Packtpub SVM. [Online]https://subscription.packtpub.com/book/big_data_and_
business_intelligence/9781789345070/3/ch03lvl1sec30/svm-for-churn-prediction
 [b] A.A. Hussain, Kalman Filters versus Neural Networks in Battery State‐of‐Charge
Estimation: A Comparative Study, International Journal of Modern Nonlinear Theory and
Application. 3 (5) (2014) 199–209, https://dx.doi.org.104236/ijmnta.2014.35022 .
 [c] Dlology SOC. [Online] https://www.dlology.com/blog/how-to-use-return_stateor-
return_sequences-in-keras/.
 [d] G.L. Plett, Extended Kalman filtering for battery management systems of LiPB-based
HEV battery packs: Part 1. Background, Journal of Power Sources. 134(2) (2004) 252–261,
http://dx.doi.org/10.1016/j.jpowsr.2004.02.031 .
 [e] J. Zhang, J. Lee, A review on prognostics and health monitoring of Li- ion battery,
Journal of power sources. 196(15) (2011) 6007–6014,
http://dx.doi.org/10.1016/j.jpowsour.2011.03.101

19. References
 [f] W. He, N. Williard, C. Chen, M. Pecht, State of charge estimation for electric vehicle
batteries using unscented kalman filter, Microelectron
 [g] S.V. Pandey, J. Patel, H. Dhiman, Battery State of Charge Modeling for Solar PV Array
using Polynomial Regression, Systems and Controls, arXiv
https://arxiv.org/abs/2008.09038
 [h]Youtube SOC. [Online] https://www.youtube.com/watch?v=Gc4wjOEAoiA
 [i] TOWARDSDATASCIENCE ekf, https://towardsdatascience.com/the-unscented-
kalman-filteranything-ekf-can-do-i-can-do-it-better-ce7c773cf88d/ ekf, last accessed
2020/03/26.
 [j] H. He, H. Qin, Y. Shui, K. Oleksandr, Lithium ion battery SOC estimation with UKF and
RTOS µ COS – II Platform, Energy Procedia. 61 (2014) 468–471,
http://dx.doi.org/10.1016/j.egypro.2014.11.1150 .