Whale Optimization Algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery

Jian Ouyang, Hao Lin and Ye Hong ()
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Jian Ouyang: Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Hao Lin: School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Ye Hong: Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China

Energies, 2024, vol. 17, issue 17, 1-22

Abstract: The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance.

Keywords: LMFP battery; SOC estimation; BP neural network; whale optimization algorithm; chaotic mapping improving (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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