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Article

Temperature-Influenced SOC Estimation of LiFePO4 Batteries in Hybrid Electric Tractors Based on SAO-LSTM Model

by
Yiwei Wu
1,2,
Xiaohui Liu
1,
Jingyun Zhang
1,2,
Mengnan Liu
2,3,
Lin Wang
2,3,
Xiaoxiao Du
4 and
Xianghai Yan
1,2,*
1
College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
2
State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471003, China
3
Luoyang Tractor Research Institute Co., Ltd., Luoyang 471003, China
4
First Tractor Company Limited, Luoyang 471003, China
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2025, 16(5), 283; https://doi.org/10.3390/wevj16050283
Submission received: 8 April 2025 / Revised: 9 May 2025 / Accepted: 15 May 2025 / Published: 19 May 2025
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Abstract

LiFePO4 batteries are widely used in hybrid electric tractors due to their high energy density, stable working voltage, low self-discharge rate, long cycle life, absence of memory effect, environmental friendliness, and flexible sizing. Accurate State of Charge (SOC) estimation is crucial for Battery Management Systems (BMSs). This study utilizes a LiFePO4 battery dataset from the University of Maryland to improve SOC estimation accuracy. The forgetting factor recursive least squares method was employed for parameter identification, and a temperature-dependent second-order RC equivalent circuit model was developed in MATLAB R2024a/Simulink. The proposed SAO-LSTM model demonstrated superior SOC estimation performance compared to traditional ampere-hour integration, achieving a 98.23% error reduction. Evaluation results showed 0.39% and 0.31% decreases in root mean square error and mean absolute error, respectively, confirming the model’s robustness and high estimation accuracy for LiFePO4 batteries in hybrid electric tractors.
Keywords: LiFePO4 battery; state of charge; snow ablation optimizer; long short-term memory network; temperature; hybrid electric tractor LiFePO4 battery; state of charge; snow ablation optimizer; long short-term memory network; temperature; hybrid electric tractor

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MDPI and ACS Style

Wu, Y.; Liu, X.; Zhang, J.; Liu, M.; Wang, L.; Du, X.; Yan, X. Temperature-Influenced SOC Estimation of LiFePO4 Batteries in Hybrid Electric Tractors Based on SAO-LSTM Model. World Electr. Veh. J. 2025, 16, 283. https://doi.org/10.3390/wevj16050283

AMA Style

Wu Y, Liu X, Zhang J, Liu M, Wang L, Du X, Yan X. Temperature-Influenced SOC Estimation of LiFePO4 Batteries in Hybrid Electric Tractors Based on SAO-LSTM Model. World Electric Vehicle Journal. 2025; 16(5):283. https://doi.org/10.3390/wevj16050283

Chicago/Turabian Style

Wu, Yiwei, Xiaohui Liu, Jingyun Zhang, Mengnan Liu, Lin Wang, Xiaoxiao Du, and Xianghai Yan. 2025. "Temperature-Influenced SOC Estimation of LiFePO4 Batteries in Hybrid Electric Tractors Based on SAO-LSTM Model" World Electric Vehicle Journal 16, no. 5: 283. https://doi.org/10.3390/wevj16050283

APA Style

Wu, Y., Liu, X., Zhang, J., Liu, M., Wang, L., Du, X., & Yan, X. (2025). Temperature-Influenced SOC Estimation of LiFePO4 Batteries in Hybrid Electric Tractors Based on SAO-LSTM Model. World Electric Vehicle Journal, 16(5), 283. https://doi.org/10.3390/wevj16050283

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