Electric vehicle parameter identification and state of charge estimation of Li-ion batteries: Hybrid WSO-HDLNN method

This manuscript proposes a hybrid method for measuring the battery's dynamic electrical response as it is compressed by an external-force. The proposed hybrid technique is the wrapper of the War Strategy Optimization algorithm and Hierarchical Deep Learning Neural Network, commonly called as WSO-HDLNN technique. The main aim of the proposed method is to lessen the battery-voltage error. The War Strategy Optimization method detects the parameters of the battery method. The Hierarchical Deep Learning Neural Network is used to predict the dynamic-electrical-response of the battery when it deforms during external-force. By using the proposed method, the estimated voltage and measured voltage error are reduced, and identifies the parameter effectively. Finally, the proposed method is done in the MATLAB platform and it is compared with different existing approaches. The error of the proposed method is 4 mV, the Jellyfish Search Optimizer method error is 6 mV, the Heap-based Optimizer method error is 12 mV, and the Grey Wolf Optimizer method error is 14 mV. The proposed method time is 0.7 s The proposed method shows better results in all methods, like Jellyfish Search Optimizer, Heap-based Optimizer, and Grey Wolf Optimizer, The proposed method provides less computation time and error than the existing one is proved from the simulation outcome.(c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This manuscript proposes a hybrid method, WSO-HDLNN, for measuring the battery's dynamic electrical response as it is compressed by an external force. The proposed method aims to reduce the battery-voltage error by combining the War Strategy Optimization algorithm and Hierarchical Deep Learning Neural Network. The results show that the proposed method outperforms existing approaches in terms of computation time and error.