Intelligent state of charge estimation of lithium-ion batteries based on L-M optimized back-propagation neural network

The state of charge (SOC) of lithium-ion batteries (LIBs) is a critical parameter of the battery management system (BMS), which represents the remaining capacity of LIBs. Precise SOC estimation is vitally important to ensure the safe and reliable operation of electric vehicles (EVs). In this paper, a series of intelligent SOC estimation methods using Levenberg-Marquard (L-M) algorithm based back-propagation neural network (BPNN) are proposed and compared with the extended Kalman filter (EKF) method. Firstly, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to optimize the three-layer BPNN based on L-M training (LMBP) and the multi-hidden-layer BPNN based on L-M training (LMMBP), which improve the estimated accuracy and convergence speed. Besides, it is verified that the LMMBP has better estimation performance than LMBP, the average absolute error (AAE) and the root mean square error (RMSE) of LMMBP can be reduced to 0.4% and 0.5% respectively under the United Kingdom Bus Cycle (UKBC). Finally, four typical driving cycles are used to carry out comparative analysis experiments, combining with the robustness evaluation results including measurement noises test, untrained driving cycles test, different batteries and piecewise training tests, it is validated that the intelligent SOC estimation methods proposed in this paper have high accuracy and strong robustness.

Automated summary

This paper proposes a series of intelligent SOC estimation methods using BPNN based on the L-M algorithm, and compares them with the EKF method. By optimizing BPNN with GA and PSO algorithms, the estimated accuracy and convergence speed are improved. The intelligent SOC estimation methods proposed in this paper demonstrate high accuracy and strong robustness through experimental validation.