基于动量反向传播神经网络的锂离子电池建模与荷电状态估计

doi:10.19595/j.cnki.1000-6753.tces.241977

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1970 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Boosting the adoption of new energy electric vehicles and energy storage technologies is pivotal in expediting the standardization of the “dual carbon” objectives. Lithium-ion batteries, serving as critical components in electric vehicles and energy storage systems, are indispensable for energy storage and buffering purposes. The precision of a battery model is vital for enhancing state estimation accuracy. Based on momentum back propagation neural networks, this paper introduces a fractional-order modeling and state- of-charge estimation technique for lithium-ion batteries, aiming to improve the interpretability of data-driven modeling.
Firstly, a BPM neural network (BPMNN)-based battery model is constructed, which incorporates the physical information of a first-order fractional-order model. The weights of the BPMNN-based model represent the model parameters, including resistance, capacitance, and fractional order. With the hierarchical feature extraction capability of neural networks, the novel battery model enables the online optimization of fractional order. Secondly, the OCV-SOC relationship is linearized using a Taylor expansion, and the linearized OCV-SOC relationship is employed to reconstruct the BPMNN-based battery model. Thirdly, a momentum gradient descent algorithm is utilized. Historical data is introduced using momentum, which improves the stability of the estimation. Moreover, the convergence of the proposed method is analyzed. The loss function of the BPMNN-based battery model converges to a positive value with a suitable learning rate. Finally, the proposed method is verified with a 2.55 A·h LiFePO₄ battery. The influence of temperature is compared with a UDDS profile at -5℃, 25℃, and 45℃. The result shows that the external temperature influences the ohmic resistance, model accuracy, and fractional order. However, it seems to have no relationship with the SOC estimation. The fractional order descends faster as the temperature decreases. Compared to the EKF-based and UKF-based methods, the proposed method improves the model accuracy by up to 64%, and the SOC estimation accuracy is improved by up to 36.5%. Compared to the GRU-RNN-based SOC estimation method, the BPMNN-based method yields a better result in SOC estimation because the combination of physical information enables online optimization of the BPMNN-based battery model. As the memory space increases, the computational burden also grows, and the battery model becomes more accurate. The model achieves a balance between accuracy and computational burden when the memory space includes 50~70 steps.
The following conclusions can be drawn. (1) The BPMNN-based online estimation method converges with a suitable learning rate, significantly improving the accuracy of the battery model and SOC estimation. The BPMNN-based method estimates SOC more accurately than the GRU-RNN method. (2) The external temperature influences the variation of ohmic resistance and model accuracy, but the SOC estimation is not affected. The fractional order descends more rapidly as the temperature decreases. (3) The memory space influences the computational burden and model accuracy.

Key words： Lithium-ion battery fractional order model parameter identification SOC estimation back propagation neural network with momentum

Received: 05 November 2024

PACS:

TM911

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Tian Jiaqiang
	Zhang Zepei
	Pan Tianhong
	Huang Darong
	Liu Xinghua

Cite this article:

Tian Jiaqiang,Zhang Zepei,Pan Tianhong等. Modeling and State of Charge Estimation for Lithium-Ion Batteries Using Backpropagation Neural Network with Momentum[J]. Transactions of China Electrotechnical Society, 2025, 40(24): 8156-8170.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.241977 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I24/8156

[1] 张程, 陆万林, 张东清, 等. 基于调节因子改进模糊熵融合加权法的锂电池状态联合估计[J/OL]. 电工技术学报, 1-13[2025-05-22]. https://doi.org/10.19595/j.cnki.1000-6753.tces.242101.
Zhang Cheng, Lu Wanlin, Zhang Dongqing, et al.Joint estimation of lithium battery states based on improved fuzzy entropy fusion weighting method with regulation factors[J/OL]. Transactions of China Electrotechnical Society, 1-13[2025-05-22].https://doi.org/10.19595/j.cnki.1000-6753.tces.242101.
[2] Tian Jiaqiang, Fan Yuan, Pan Tianhong, et al.A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems[J]. Renewable and Sustainable Energy Reviews, 2024, 189: 113978.
[3] 李弈, 张金龙, 漆汉宏, 等. 基于变分深度嵌入-带有梯度惩罚的生成对抗网络的锂离子电池老化特性建模[J]. 电工技术学报, 2024, 39(13): 4226-4239.
Li Yi, Zhang Jinlong, Qi Hanhong, et al.Ageing performance modeling of li-ion batteries based on variational deep embedding-wasserstein GAN with gradient penalty[J]. Transactions of China Electro- technical Society, 2024, 39(13): 4226-4239.
[4] Li Weihan, Demir I, Cao Decheng, et al.Data-driven systematic parameter identification of an electro- chemical model for lithium-ion batteries with artificial intelligence[J]. Energy Storage Materials, 2022, 44: 557-570.
[5] Tian Jiaqiang, Wang Yujie, Chen Zonghai.An improved single particle model for lithium-ion batteries based on main stress factor compensation[J]. Journal of Cleaner Production, 2021, 278: 123456.
[6] Appiah W A, Busk J, Vegge T, et al.Sensitivity analysis methodology for battery degradation models[J]. Electrochimica Acta, 2023, 439: 141430.
[7] 李建林, 李雅欣, 陈光, 等. 退役动力电池健康状态特征提取及评估方法综述[J]. 中国电机工程学报, 2022, 42(4): 1332-1347.
Li Jianlin, Li Yaxin, Chen Guang, et al.Research on feature extraction and SOH evaluation methods for retired power battery[J]. Proceedings of the CSEE, 2022, 42(4): 1332-1347.
[8] 刘旖琦, 雷万钧, 刘茜, 等. 基于双自适应扩展粒子滤波器的锂离子电池状态联合估计[J]. 电工技术学报, 2024, 39(2): 607-616.
Liu Yiqi, Lei Wanjun, Liu Qian, et al.Joint state estimation of lithium-ion battery based on dual adaptive extended particle filter[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 607-616.
[9] 赵靖英, 胡劲, 张雪辉,等. 基于锂电池模型和分数阶理论的SOC-SOH联合估计[J]. 电工技术学报, 2023, 38(17): 4551-4563.
Zhao Jingying, Hu Jin, Zhang Xuehui, et la. Joint estimation of the SOC-SOH based on lithium battery model and fractional order theory[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4551-4563.
[10] Wang Yujie, Zhao Guanghui.A comparative study of fractional-order models for lithium-ion batteries using Runge Kutta optimizer and electrochemical impe- dance spectroscopy[J]. Control Engineering Practice, 2023, 133: 105451.
[11] 杜焕伦, 李哲帆, 石琼林, 等. 基于BP神经网络与DP等效电路的锂离子电池热电耦合模型构建[J/OL]. 电源学报, 1-12[2025-05-22]. http://kns.cnki.net/kcms/detail/12.1420.tm.20231219.1348.014.html.
Du Huanlun, Li Zhefan, Shi Qionglin, et al. Con- struction of a thermoelectric coupling model for Lithium-ion batteries[J/OL]. Journal of Power Supply, 2024: 1-12[2025-05-22]. http://kns.cnki.net/kcms/ detail/12.1420.tm.20231219.1348.014.html.
[12] 蔡黎, 何德伍, 代妮娜, 等. 锂离子电池测试仪的设计与实现[J]. 电池, 2019, 49(1): 83-85.
Cai Li, He Dewu, Dai Nina, et al.The design and implementation of a Li-ion battery testing instru- ment[J]. Battery Bimonthly, 2019, 49(1): 83-85.
[13] Wu Longxing, Lü Zhiqiang, Huang Zebo, et al.Physics-based battery SOC estimation methods: recent advances and future perspectives[J]. Journal of Energy Chemistry, 2024, 89: 27-40.
[14] Chai Xuqing, Li Shihao, Liang Fengwei.A novel battery SOC estimation method based on random search optimized LSTM neural network[J]. Energy, 2024, 306: 132583.
[15] 林鹏, 刘涛, 金鹏, 等. 基于多新息辨识算法的锂离子电池等效电路模型参数辨识[J]. 储能科学与技术, 2023, 12(10): 3155-3169.
Lin Peng, Liu Tao, Jin Peng, et al.Identification of lithium-ion battery equivalent circuit model para- meters based on the multi-innovation identification algorithm[J]. Energy Storage Science and Technology, 2023, 12(10): 3155-3169.
[16] 刘芳, 邵晨, 苏卫星, 等. 基于全新等效电路模型的电池关键状态在线联合估计器[J]. 控制与决策, 2023, 38(6): 1620-1628.
Liu Fang, Shao Chen, Su Weixing, et al.Online joint estimator of battery key states based on a new equivalent circuit model[J]. Control and Decision, 2023, 38(6): 1620-1628.
[17] Tian Jiaqiang, Zhang Qingping, Liu Xinghua, et al.Route planning-based active equalization of recon- figurable battery packs in electric vehicles[J]. IEEE Transactions on Intelligent Vehicles, 2024(99): 1-14.
[18] 程泽, 杨磊, 孙幸勉. 基于自适应平方根无迹卡尔曼滤波算法的锂离子电池SOC和SOH估计[J]. 中国电机工程学报, 2018, 38(8): 2384-2393, 2548.
Cheng Ze, Yang Lei, Sun Xingmian.State of charge and state of health estimation of Li-ion batteries based on adaptive square-root unscented Kalman filters[J]. Proceedings of the CSEE, 2018, 38(8): 2384-2393, 2548.
[19] Hu Xiaosong, Yuan Hao, Zou Changfu, et al.Co- estimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculus[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10319-10329.
[20] Yang Fangfang, Li Weihua, Li Chuan, et al.State- of-charge estimation of lithium-ion batteries based on gated recurrent neural network[J]. Energy, 2019, 175: 66-75.
[21] Li Chaoran, Zhu Sichen, Zhang Liuli, et al.State of charge estimation of lithium-ion battery based on state of temperature estimation using weight clustered-convolutional neural network-long short- term memory[J]. Green Energy and Intelligent Transportation, 2025, 4(1): 100226.