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Multi-temperature capable enhanced bidirectional bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation.

Zhou, Yifei; Wang, Shunli; Feng, Renjun; Xie, Yanxin; Fernandez, Carlos

Authors

Yifei Zhou

Shunli Wang

Renjun Feng

Yanxin Xie



Abstract

In this study, we propose a novel hybrid modeling framework for State of Charge (SOC) estimation across a broad temperature spectrum. First, we build a hybrid model to optimize stacked layers of stacked bidirectional long short term memory networks by introducing dropout mechanisms. At the same time, we also optimize the traditional multi-layer perceptron model to ResMLP, which is improved by introducing residual linkage, and then integrate the two optimization models. Finally, the synergistic effect and attention mechanism of genetic algorithm and particle swarm optimization are used to optimize its parameters. We then rigorously tested the model on nine datasets, including HPPC, DST and BBDST, at different temperatures of 5°C, 15°C and 35°C. Using MAE, RMSE and MAXE benchmarks, our research results show that the proposed hybrid model outperforms the benchmark algorithm, achieving significantly enhanced performance and higher accuracy, and the maximum SOC estimation error is kept below 4.53%. In addition, experimental evaluation at different temperatures shows the robustness and adaptability of the proposed algorithm.

Citation

ZHOU, Y., WANG, S., FENG, R., XIE, Y. and FERNANDEZ, C. [2024]. Multi-temperature capable enhanced bidirectional bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation. Energy [online], In Press. Available from: https://doi.org/10.1016/j.energy.2024.133596

Journal Article Type Article
Acceptance Date Oct 22, 2024
Online Publication Date Oct 24, 2024
Deposit Date Oct 25, 2024
Publicly Available Date Oct 25, 2025
Journal Energy
Print ISSN 0360-5442
Electronic ISSN 1873-6785
Publisher Elsevier
Peer Reviewed Peer Reviewed
Article Number 133596
DOI https://doi.org/10.1016/j.energy.2024.133596
Keywords State of charge; Lithium-ion battery; BiLSTM; Multiple perceptrons; Particle swarm optimization algorithm; Genetic algorithm
Public URL https://rgu-repository.worktribe.com/output/2542496