Research Article
Lithium-ion battery ageing prediction in real time using Genetic Algorithm in MATLAB
@INPROCEEDINGS{10.4108/eai.7-12-2021.2314581, author={Suresh Kumar R and Hariharan T G}, title={Lithium-ion battery ageing prediction in real time using Genetic Algorithm in MATLAB}, proceedings={Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India}, publisher={EAI}, proceedings_a={ICCAP}, year={2021}, month={12}, keywords={soh hevs evs ffnn soc opal-rt}, doi={10.4108/eai.7-12-2021.2314581} }- Suresh Kumar R
Hariharan T G
Year: 2021
Lithium-ion battery ageing prediction in real time using Genetic Algorithm in MATLAB
ICCAP
EAI
DOI: 10.4108/eai.7-12-2021.2314581
Abstract
Highly accurate State-of-Health(SoH) assessment in lithium-ion based cells is exceptionally difficult due to the nonlinear exhibits of batteries and the complex application environment in hybrid electric vehicles (HEVs) and electric vehicles (EVs), primarily variations in temperature conditions.TheStateofCharge(SOC)conditions were calculated using the extended Kalman filter algorithmin this paper using an analogous circuit model with experimental data. A two-layer feedforward neural network (FFNN) with sigmoid function and Levenberg-Marquardt training algorithm choice was used to optimize the estimated performance. For a constant temperature of 35°C, plot findings were cross-correlated with various SOC conditions using electrochemical impedance spectroscopy (EIS). The developedEKFestimationmodelisevaluatedcurrentprofiles to compute the change in voltage for estimating the battery's SOH. The developed EKF estimation model was analyzed current profiles to compute the change in voltage for estimating the battery's SOH. A hardware-in-loop (HIL) test bench using the OPAL-RT tool is designed for the real-time and heuristic of the developed EKF estimation model to evaluate current profiles to compute the change in voltage estimation of the battery's SOH.
