Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM

Cao Mingxing; Gao Guozhang

doi:doi:10.11648/j.sd.20251306.18

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Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM

Cao Mingxing^*, Gao Guozhang

Published in Science Discovery (Volume 13, Issue 6)

Received: 28 October 2025 Accepted: 11 December 2025 Published: 24 December 2025

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Abstract

State of Charge (SOC) estimation is one of the core contents of the Battery Management System (BMS), and it is crucial for evaluating the safety of using lithium-ion batteries as the power source for ships. Based on the existing problems in SOC estimation methods, this paper proposes a method for SOC estimation that uses the Long Short-Term Memory (LSTM) neural network as the core algorithm, optimizes parameters through the Harris Hawk Optimization Algorithm (HHO), and builds a co-simulation model. Firstly, using four batteries, B0005, B0006, B0007, and B0018, from the National Aeronautics and Space Administration (NASA) as the basic data, the random forest algorithm and Pearson correlation coefficient analysis are used to identify the linear and nonlinear correlations with the target variable respectively, eliminate redundant or irrelevant variables, and screen out features with high correlation with SOC to improve estimation accuracy. Then, the obtained features are input into the LSTM neural network model, and the Harris optimization algorithm is used to optimize the model parameter combination. Finally, digital simulation experiments are conducted. The results show that the HHO-LSTM prediction model improves the estimation accuracy of SOC. This method provides a new idea for SOC estimation and has certain effectiveness and feasibility.

Published in	Science Discovery (Volume 13, Issue 6)
DOI	10.11648/j.sd.20251306.18
Page(s)	149-158
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

State of Charge Estimation, Harris Hawk Optimization Algorithm, Long Short-Term Memory Neural Network, Pearson Correlation Coefficient Analysis

References

[1]	I Dongliang, MAO Qian, et al. Scient metric research and critic analysis of battery state of-charge estimation [J]. Journal of Energy Storage, 2023, 58:106283
[2]	叶丽华, 薛定邦, 彭鼎涵, 等.基于分数阶模型与IEKF的锂离子电池 SOC估计研究 [J]. 农业装备与车辆工程, 2023, 61 (6): 7–10
[3]	周韦润, 姜文刚.基于遗传算法优化扩展卡尔曼滤波的锂电池SOC估计 [J]. 重庆理工大学学报 (自然科学版), 2019, 33 (9): 33 - 39.
[4]	王位.基于平均电极反应动力学的锂离子动力电池模型降阶与SOC估计研究 [D]. 江苏大学2017.
[5]	杜嘉诚. 基于滑模观测器的锂离子电池荷电状态与健康状态估计算法研究 [D].哈尔滨工业大学, 2018.
[6]	骆秀江, 张兵, 黄细霞, 等.基于SVM的锂电池SOC估算 [J].电源技术, 2016, 40(02): 287-290.
[7]	徐帅, 刘雨辰, 周飞.基于 RNN 的锂离子电池SOC估算研究进展 [J]. 电源技术, 2021, 45 (2): 263 - 269.
[8]	Zhang Z, Xu M, Ma L, et al. A state - of - charge estimation method based on bidirectional LSTM networks for lithium - ion batteries [C]. IEEE International Conference, 2020.
[9]	Yang, Yalong, Chen, Siyuan, Chen, Tao, et al. State of Health Assessment of Lithium-ion Batteries Based on Deep Gaussian Process Regression Considering Heterogeneous Features [J]. Journal of Energy Storage, 2023, 61.
[10]	Wei, Yupeng, Wu, Dazhong. Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms [J]. Reliability Engineering & System Safety, 2023, 230.
[11]	李超然, 肖飞, 樊亚翔, 等.基于深度学习的锂离子电池SOC和SOH联合估算 [J]. 中国电机工程学报, 2021, 41 (2): 681 – 691.
[12]	郑青根, 杨祥国, 刘冬, 等.改进灰狼优化最小二乘法支持向量机的锂电池剩余寿命预测 [J]. 重庆大学学报, 2023, 46 (11): 78-89.
[13]	Fan Y, Xiao F, Li C, et al. A novel deep learning framework for state of health estimation of lithium - ion battery [J]. Journal of Energy Storage, 2020, 32: 101741.
[14]	Liu S, Dong X, Yu X, et al. A method for state of charge and state of health estimation of lithium - ion battery based on adaptive unscented Kalman filter [J]. Energy Reports, 2022, 8:426 - 436.
[15]	常宇佳. 基于ICA与SVR的锂离子电池健康状态估计研究 [D]. 哈尔滨理工大学, 2021.
[16]	Hong S, Kang M, Kim J, et al. Sequential application of denoising autoencoder and long - short recurrent convolutional network for noise - robust remaining - useful - life prediction framework of lithium - ion batteries [J]. Computers & Industrial Engineering, 2023, 179:109231.
[17]	Qiyan Yan.SOCPrediction of Power Battery Based on SVM [C].2020:2425-2429.
[18]	Li H, Yao Y, Hou J, et al. GPR - Bi - LSTM power battery health state estimation and remaining life prediction based on ICEEMDAN algorithm [J]. 2022.
[19]	Gao M, Bao Z, Zhu C, et al. HFCM - LSTM: A novel hybrid framework for state - of - health estimation of lithium - ion battery [J]. Energy Reports, 2023, 9:2577 - 2590.
[20]	Ren Z, Du C. A review of machine learning state - of - charge and state - of - health estimation algorithms for lithium - ion batteries [J]. Energy Reports, 2023, 9: 2993 - 3021.
[21]	Hossain Lipu M S, Ansari S, Miah M S, et al. Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: methods, implementations, issues and prospects [J]. Journal of Energy Storage, 2022, 55:105752.
[22]	Chai, Xuqing, Li, Shihao, Liang, Fengwei. A novel batterySOCestimation method based on random search optimized LSTM neural network [J]. ENERGY, 2024, 306. https://doi.org/10.1016/j.energy.2024.132583
[23]	Hong, Jichao, Wang, Zhenpo, Chen, Wen, et al. Online joint-prediction of multi-forward-step batterySOCusing LSTM neural networks and multiple linear regression for real-world electric vehicles [J]. JOURNAL OF ENERGY STORAGE, 2020, 30. https://doi.org/10.1016/j.est. 2020.101459
[24]	王晓峰, 王军霞, 彭庆媛, 等.融合多策略的哈里斯鹰优化算法求解Steiner树问题 [J/OL].郑州大学学报(理学版), 1-8 [2025-10-05]. https://doi.org/10.13705/j.issn.1671-6841.2024174
[25]	SHERSTINSKY A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network [J]. Physica D: Nonlinear Phenomena, 2020, 404: 132306.
[26]	于天剑, 曾笑颜, 冯恩来, 等.基于LSTM-Transformer多通道特征融合的锂电池SOC-SOH联合估计 [J/OL].铁道科学与工程学报, 1-13, 2025-10-06.

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APA Style

Mingxing, C., Guozhang, G. (2025). Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM. Science Discovery, 13(6), 149-158. https://doi.org/10.11648/j.sd.20251306.18

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ACS Style

Mingxing, C.; Guozhang, G. Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM. Sci. Discov. 2025, 13(6), 149-158. doi: 10.11648/j.sd.20251306.18

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AMA Style

Mingxing C, Guozhang G. Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM. Sci Discov. 2025;13(6):149-158. doi: 10.11648/j.sd.20251306.18

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@article{10.11648/j.sd.20251306.18,
  author = {Cao Mingxing and Gao Guozhang},
  title = {Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM
},
  journal = {Science Discovery},
  volume = {13},
  number = {6},
  pages = {149-158},
  doi = {10.11648/j.sd.20251306.18},
  url = {https://doi.org/10.11648/j.sd.20251306.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20251306.18},
  abstract = {State of Charge (SOC) estimation is one of the core contents of the Battery Management System (BMS), and it is crucial for evaluating the safety of using lithium-ion batteries as the power source for ships. Based on the existing problems in SOC estimation methods, this paper proposes a method for SOC estimation that uses the Long Short-Term Memory (LSTM) neural network as the core algorithm, optimizes parameters through the Harris Hawk Optimization Algorithm (HHO), and builds a co-simulation model. Firstly, using four batteries, B0005, B0006, B0007, and B0018, from the National Aeronautics and Space Administration (NASA) as the basic data, the random forest algorithm and Pearson correlation coefficient analysis are used to identify the linear and nonlinear correlations with the target variable respectively, eliminate redundant or irrelevant variables, and screen out features with high correlation with SOC to improve estimation accuracy. Then, the obtained features are input into the LSTM neural network model, and the Harris optimization algorithm is used to optimize the model parameter combination. Finally, digital simulation experiments are conducted. The results show that the HHO-LSTM prediction model improves the estimation accuracy of SOC. This method provides a new idea for SOC estimation and has certain effectiveness and feasibility.
},
 year = {2025}
}

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TY  - JOUR
T1  - Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM

AU  - Cao Mingxing
AU  - Gao Guozhang
Y1  - 2025/12/24
PY  - 2025
N1  - https://doi.org/10.11648/j.sd.20251306.18
DO  - 10.11648/j.sd.20251306.18
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 149
EP  - 158
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20251306.18
AB  - State of Charge (SOC) estimation is one of the core contents of the Battery Management System (BMS), and it is crucial for evaluating the safety of using lithium-ion batteries as the power source for ships. Based on the existing problems in SOC estimation methods, this paper proposes a method for SOC estimation that uses the Long Short-Term Memory (LSTM) neural network as the core algorithm, optimizes parameters through the Harris Hawk Optimization Algorithm (HHO), and builds a co-simulation model. Firstly, using four batteries, B0005, B0006, B0007, and B0018, from the National Aeronautics and Space Administration (NASA) as the basic data, the random forest algorithm and Pearson correlation coefficient analysis are used to identify the linear and nonlinear correlations with the target variable respectively, eliminate redundant or irrelevant variables, and screen out features with high correlation with SOC to improve estimation accuracy. Then, the obtained features are input into the LSTM neural network model, and the Harris optimization algorithm is used to optimize the model parameter combination. Finally, digital simulation experiments are conducted. The results show that the HHO-LSTM prediction model improves the estimation accuracy of SOC. This method provides a new idea for SOC estimation and has certain effectiveness and feasibility.

VL  - 13
IS  - 6
ER  -

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Author Information

Cao Mingxing

School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China

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Gao Guozhang

School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China

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Plain Text BibTeX RIS

APA Style

Mingxing, C., Guozhang, G. (2025). Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM. Science Discovery, 13(6), 149-158. https://doi.org/10.11648/j.sd.20251306.18

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ACS Style

Mingxing, C.; Guozhang, G. Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM. Sci. Discov. 2025, 13(6), 149-158. doi: 10.11648/j.sd.20251306.18

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AMA Style

Mingxing C, Guozhang G. Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM. Sci Discov. 2025;13(6):149-158. doi: 10.11648/j.sd.20251306.18

Copy | Download

@article{10.11648/j.sd.20251306.18,
  author = {Cao Mingxing and Gao Guozhang},
  title = {Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM
},
  journal = {Science Discovery},
  volume = {13},
  number = {6},
  pages = {149-158},
  doi = {10.11648/j.sd.20251306.18},
  url = {https://doi.org/10.11648/j.sd.20251306.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20251306.18},
  abstract = {State of Charge (SOC) estimation is one of the core contents of the Battery Management System (BMS), and it is crucial for evaluating the safety of using lithium-ion batteries as the power source for ships. Based on the existing problems in SOC estimation methods, this paper proposes a method for SOC estimation that uses the Long Short-Term Memory (LSTM) neural network as the core algorithm, optimizes parameters through the Harris Hawk Optimization Algorithm (HHO), and builds a co-simulation model. Firstly, using four batteries, B0005, B0006, B0007, and B0018, from the National Aeronautics and Space Administration (NASA) as the basic data, the random forest algorithm and Pearson correlation coefficient analysis are used to identify the linear and nonlinear correlations with the target variable respectively, eliminate redundant or irrelevant variables, and screen out features with high correlation with SOC to improve estimation accuracy. Then, the obtained features are input into the LSTM neural network model, and the Harris optimization algorithm is used to optimize the model parameter combination. Finally, digital simulation experiments are conducted. The results show that the HHO-LSTM prediction model improves the estimation accuracy of SOC. This method provides a new idea for SOC estimation and has certain effectiveness and feasibility.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Lithium-ion Battery State of Charge (SOC) Estimation Based on HHO-LSTM

AU  - Cao Mingxing
AU  - Gao Guozhang
Y1  - 2025/12/24
PY  - 2025
N1  - https://doi.org/10.11648/j.sd.20251306.18
DO  - 10.11648/j.sd.20251306.18
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 149
EP  - 158
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20251306.18
AB  - State of Charge (SOC) estimation is one of the core contents of the Battery Management System (BMS), and it is crucial for evaluating the safety of using lithium-ion batteries as the power source for ships. Based on the existing problems in SOC estimation methods, this paper proposes a method for SOC estimation that uses the Long Short-Term Memory (LSTM) neural network as the core algorithm, optimizes parameters through the Harris Hawk Optimization Algorithm (HHO), and builds a co-simulation model. Firstly, using four batteries, B0005, B0006, B0007, and B0018, from the National Aeronautics and Space Administration (NASA) as the basic data, the random forest algorithm and Pearson correlation coefficient analysis are used to identify the linear and nonlinear correlations with the target variable respectively, eliminate redundant or irrelevant variables, and screen out features with high correlation with SOC to improve estimation accuracy. Then, the obtained features are input into the LSTM neural network model, and the Harris optimization algorithm is used to optimize the model parameter combination. Finally, digital simulation experiments are conducted. The results show that the HHO-LSTM prediction model improves the estimation accuracy of SOC. This method provides a new idea for SOC estimation and has certain effectiveness and feasibility.

VL  - 13
IS  - 6
ER  -

Copy | Download