Fractional Order Model-based Estimation for State of Charge in Lithium-ion Battery

doi:10.3901/JME.2024.08.224

Abstract

Abstract: How to accurately estimate the state of charge is one of the key technologies for safe application in lithium-ion batteries. However, the current approaches are not fully fit for lithium-ion battery systems, and there needs improvement in accuracy, stability and practicality. In order to describe the dynamic characteristics of lithium-ion battery systems and improve the accuracy and stability, an adaptive extended Kalman particle filter is proposed based on fractional order battery model to estimate the state of charge of lithium-ion. In the process of parameter identification of fractional order battery model with immune genetic algorithm, the "memory vault" is used to reduce the calculation amount of the algorithm, and the "affinity degree" is introduced to solve the problem of local convergence of the algorithm. The proposed control algorithm was downloaded into the battery management system controller by means of model-based development and compared and verified by ECE and UDDS condition tests. The terminal voltage error of the second-order fractional battery model is not more than 13.96 mV, and the average error is 2.4-4.2 mV, which indicates that the fractional battery model is more sensitive to the change of current, and can better represent the performance of the battery voltage change, and can effectively ensure the calculation accuracy of the battery SOC. Compared with the EKF, the accuracy of SOC estimation is improved by more than 50%, and the convergence time is greatly reduced, which indicates that the introduction of adaptive Kalman filter in particle filter for correction can filter out noise and enhance the accuracy and robustness.

Key words: state of charge, fractional order battery model, adaptive Kalman particle filter, immune genetic algorithm

CLC Number:

TU375

SHI Qin, JIANG Zhengxin, LIU Yiwen, WEI Yujiang, HU Xiaosong, HE Lin. Fractional Order Model-based Estimation for State of Charge in Lithium-ion Battery[J]. Journal of Mechanical Engineering, 2024, 60(8): 224-232,244.

References

[1] POMERANTSEVA E ，BONACCORSO F ，FENG Xinliang，et al. Energy storage：The future enabled by nanomaterials[J]. Science，2019，366(6468)：eaan8285.
[2] JIANG Zhengxin，SHI Qin，HE Lin，et al. An immune genetic extended Kalman particle Filter approach on state of charge estimation for lithium-ion battery[J]. Energy，2021，230：120805.
[3] 张亚军，王贺武，冯旭宁，等. 动力锂离子电池热失控燃烧特性研究进展[J]. 机械工程学报，2019，55(20)：17-27. ZHANG Yajun，WANG Hewu，FENG Xuning，et al. Research progress on thermal runaway combustion characteristics of power lithiumion batteries[J]. Journal of Mechanical Engineering，2019，55(20)：17-27.
[4] ZHANG Hailong，PENG Jiankun，TAN Huachun，et al. Tackling SOC long-term dynamic for energy management of hybrid electric buses via adaptive policy optimization[J]. Applied Energy，2020，269：115031.
[5] HU Xiaosong，JIANG Haifu，FENG Fei，et al. An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management[J]. Applied Energy，2020，257：114019.
[6] WANG Houlian，ZHOU Gongbo，XUE Rui，et al. A driving-behavior based SOC prediction method for light urban vehicles powered by supercapacitors[J]. IEEE Transactions on Intelligent Transportation Systems，2020，21(5)：2090-2099.
[7] MAO Binbin，CHEN Haodong，JIANG Lin，et al. Refined study on lithium ion battery combustion in open space and a combustion chamber[J]. Process Safety and Environmental Protection，2020，139：133-146.
[8] 向兆军，胡凤玲，罗明华，等. 基于电池组模型和聚类算法的锂离子电池组SOC不一致估计[J]. 机械工程学报，2020，56(18)：154-163. XIANG Zhaojun，HU Fengling，LUO Minghua，et al. Estimation of SOC inconsistencies in lithium-ion battery packs based on battery pack modeling and clustering algorithm[J]. Journal of Mechanical Engineering，2020，56(18)：154-163.
[9] XIONG Rui，LI Linlin，YU Quanqing，et al. A set membership theory based parameter and state of charge co-estimation method for all-climate batteries- sciencedirect[J]. Journal of Cleaner Production，2020，249：119380.
[10] ZHANG Cheng，ALLAFI W ，DINH Q ，et al. Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique[J]. Energy，2018，142：678-688.
[11] SUN Yong，MA Zilin，TANG Gongyou，et al. Estimation method of state-of-charge for lithium-ion battery used in hybrid electric vehicles based on variable structure extended Kalman filter[J]. Chinese Journal of Mechanical Engineering，2016，29(4)：717-726.
[12] WANG Shunli，FERNANDEZ C，CAO Wen ，et al. An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect[J]. Journal of Power Sources，2019，428：67-75.
[13] XIA Bizhong，GUO Shengkun，WANG Wei，et al. A state of charge estimation method based on adaptive extended Kalman-particle filtering for lithium-ion batteries[J]. Energies，2018，11(10)：2755.
[14] 刘兴涛，李坤，武骥，等. 基于EKF-SVM算法的动力电池SOC估计[J]. 汽车工程，2020，42(11)：1522-1528，1544. LIU Xingtao，LI Kun，WU Ji，et al. State of charge estimation for traction battery based on EKF-SVM algorithm[J]. Automotive Engineering，2020，42(11) ：1522-1528，1544.
[15] QIU Xianghui，WU Weixiong，WANG Shuangfeng. Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method[J]. Journal of Power Sources，2020，450：227700.
[16] ZHANG Kai ，MA Jian ，ZHAO Xuan ，et al. State of charge estimation for lithium battery based on adaptively weighting cubature particle filter[J]. IEEE Access，2019，7：166657-166666.
[17] XIONG Rui，ZHANG Yongzhi，HE Hongwen，et al. A double-scale，particle-filtering，energy state prediction algorithm for lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics，2017，65(2)：1526-1538.
[18] YANG Fangfang，ZHANG Shaohui，LI Weihua，et al. State-of-charge estimation of lithium-ion batteries using LSTM and UKF[J]. Energy，2020，201：117664.
[19] 鲍伟，葛建军. 基于稀疏采样数据的电动公交车电池SOC预测方法研究[J]. 汽车工程，2020，42(3)：367-374. BAO Wei，GE Jianjun. Study on battery SOC prediction method for electric bus based on sparsely sampled data[J]. Automotive Engineering，2020，42(3)：367-374.
[20] FENG Fei，TENG Sangli，LIU Kailong，et al. Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical- thermal- neural- network model[J]. Journal of Power Sources，2020，455：227935.
[21] DENG Zhongwei，HU Xiaosong，LIN Xianke，et al. Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression[J]. Energy，2020，205：118000.
[22] 周航，刘晓龙，张梦迪，等. 基于简单循环单元的储能锂离子电池SOC和SOH联合估计方法[J]. 电气工程学报，2023，18(3)：332-340. ZHOU Hang，LIU Xiaolong，ZHANG Mengdi，et al. Joint SOC and SOH estimation method for energy storage lithium-ion batteries based on simple recurrent unit[J]. Journal of Electrical Engineering，2023，18(3)：332-340.
[23] 张宇鑫，武建华，郑林峰，等. 基于数字孪生的锂离子电池管理系统设计分析[J]. 电气工程学报，2022，17(4)：103-112. ZHANG Yuxin，WU Jianhua，ZHENG Linfeng，et al. Design and analysis of lithium-ion battery management system based on digital twin[J]. Journal of Electrical Engineering，2022，17(4)：103-112.
[24] HU Minghui，LI Yunxiao，LI Shuxian，et al. Lithium-ion battery modeling and parameter identification based on fractional theory[J]. Energy，2018，165：153-163.
[25] HE Lin，HU Minkang，WEI Yujiang，et al. State of charge estimation by finite difference extended Kalman filter with HPPC parameters identification[J]. Science China(Technological Sciences)，2020，63(3)：410-421.