Yifei Zhou
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
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 |
Files
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