An Immune Genetic Extended Kalman Particle Filter approach on state of charge estimation for lithium-ion battery

In this paper, based on the lithium-ion battery parameter identification by Immune Genetic Algorithm, An Extended Kalman Particle Filter approach is proposed to estimate the state of charge. Immune Genetic Algorithm was designed to identify the second-order equivalent circuit model parameters of lithium-ion battery. Combining Extended Kalman Filter with Particle Filter, Extended Kalman Particle Filter is designed to estimate the lithium-ion battery state of charge. This method is especially for the nonlinear and time variant lithium-ion battery system, and it can improve the calculation accuracy and stability of State of Charge estimation. An Immune Genetic Extended Kalman Particle Filter approach is validated by some experimental scenarios on the test bench. Experimental results show that Immune Genetic Extended Kalman Particle Filter has better adaptability, robustness and accuracy than Extended Kalman Filter under both UDDS and ECE conditions. Both theoretical and experimental results illustrate that Extended Kalman Particle Filter is a good candidate to estimate the lithium-ion battery state of charge. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a method using Extended Kalman Particle Filter to estimate the state of charge of lithium-ion battery by identifying parameters through Immune Genetic Algorithm, showing good adaptability and accuracy in experimental scenarios.