摘要准确、实时地估计电池的荷电状态(state of charge,SOC)和健康状态(state of health,SOH)是现代电池管理系统的关键任务。通过自适应H2/H∞滤波器可对锂电池的SOC和SOH进行联合估计。该方法基于锂电池的二阶RC等效电路模型,采用AFFRLS法在线辨识锂电池的模型参数,并利用H2/H∞滤波器估计锂电池的SOC,AFFRLS辨识与H2/H∞滤波交替进行,得到一种自适应H2/H∞滤波器。SOH依据AFFRLS辨识的电池内阻进行估计,实现了锂电池SOC与SOH的联合估计。实验结果表明:自适应H2/H∞滤波算法的估计精度高且鲁棒性强,电池的SOC和SOH的平均估计误差始终保持在±0.19%以内,相比于EKF和H∞滤波算法有更高的估计精度与稳定性。
Abstract：Accurate and real-time estimation of a batterys state of charge (SOC) and state of health (SOH) is a key task of modern battery management systems.The SOC and SOH of lithium batteries can be estimated jointly by an adaptive H2/H∞ filter. This method is based on the second-order RC equivalent circuit model of lithium battery, and AFFRLS method is used to identify the model parameters of lithium battery online.Using H2/H∞ filter to estimate SOC of lithium battery, AFFRLS identification and H2/H∞ filter are alternated to obtain an adaptive H2/H∞ filter.SOH is estimated according to the internal resistance identified by AFFRLS, and the joint estimation of SOC and SOH of lithium battery is realized.The experimental results show that the adaptive H2/H∞ filtering algorithm has high estimation accuracy and strong robustness, and the average estimation error of SOC and SOH of the battery is always within 0.19%, which has higher estimation accuracy and stability than EKF and H∞ filtering algorithm.
Ma Z, Gao F, Gu X, et al. Multilayer SOH equalization scheme for MMC battery energy storage system［J］. IEEE Transactions on Power Electronics, 2020, 35(12): 13514-13527.
［2］
Hannan M A, How D N T, Lipu M S H, et al. SOC estimation of li-ion batteries with learning rate-optimized deep fully convolutional network［J］. IEEE Transactions on Power Electronics, 2020, 36(7): 7349-7353.
［3］
Xie J, Ma J, Bai K. Enhanced coulomb counting method for state-of-charge estimation of lithium-ion batteries based on peukerts law and coulombic efficiency［J］. Journal of Power Electronics, 2018, 18(3): 910-922.
［4］
Klintberg A, Zou C, Fridholm B, et al. Kalman filter for adaptive learning of two-dimensional look-up tables applied to OCV-curves for aged battery cells［J］. Control Engineering Practice, 2019, 84: 230-237.
［5］
Snihir I, Rey W, Verbitskiy E, et al. Battery open-circuit voltage estimation by a method of statistical analysis［J］. Journal of Power Sources, 2006, 159(2): 1484-1487.
［7］
Beelen H, Bergveld H J, Donkers M C F. Joint estimation of battery parameters and state of charge using an extended Kalman filter: a single-parameter tuning approach［J］. IEEE Transactions on Control Systems Technology, 2020, 29(3): 1087-1101.
Wu Z Q, Wang G Y, Xie Z K, et al. Joint estimation of lithium battery capacity and SOC based on WOA-UKF algorithm ［J］. Acta Metrologica Sinica, 2022, 43(5): 649-656.
Barillas J K, Li J, Günther C, et al. A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems［J］. Applied Energy, 2015, 155: 455-462.
［12］
Liu Y, Ma R, Pang S, et al. A nonlinear observer SOC estimation method based on electrochemical model for lithium-ion battery［J］. IEEE Transactions on Industry Applications, 2020, 57(1): 1094-1104.
［13］
Zhao L, Liu Z, Ji G. Lithium-ion battery state of charge estimation with model parameters adaptation using H∞ extended Kalman filter［J］. Control Engineering Practice, 2018, 81: 114-128.
［15］
Chen Z, Zhao H, Shu X, et al. Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H∞ filter［J］. Energy, 2021, 228: 120630.
Song Y, Liu D, Liao H, et al. A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries［J］. Applied Energy, 2020, 261: 114408.
Wang P, Peng X Y, Cheng Z, et al. Multi-time scale State Estimation Method for Lithiumion Batteries Based on Data-driven Model Fusion ［J］. Automotive Engineering, 2022, 44(3): 362-371.
［20］
Xiao D, Fang G, Liu S, et al. Reduced-coupling coestimation of SOC and SOH for lithium-ion batteries based on convex optimization［J］. IEEE Transactions on Power Electronics, 2020, 35(11): 12332-12346.
［21］
Qiu X, Wu W, Wang S. 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.
Yin X H, Song Y C, Liu W, et al. Joint Estimation of lithium-ion battery State Based on Multi-time scale ［J］. Chinese Journal of Scientific Instrument, 2018, 39(8): 118-126.
［6］
Truchot C, Dubarry M, Liaw B Y. State-of-charge estimation and uncertainty for lithium-ion battery strings［J］. Applied Energy, 2014, 119: 218-227.
［14］
Chen C, Xiong R, Shen W. A lithium-ion battery-in-the-loop approach to test and validate multiscale dual H∞ filters for state-of-charge and capacity estimation［J］. IEEE Transactions on power Electronics, 2017, 33(1): 332-342.
Wu Z Q, Hu X Y, Ma B Y, et al. SOC estimation of lithium battery based on RFF and GWO-PF ［J］. Acta Metrologica Sinica, 2022, 43(9): 1200-1207.
［11］
Feng Y, Xue C, Han Q L, et al. Robust estimation for state-of-charge and state-of-health of lithium-ion batteries using integral-type terminal sliding-mode observers［J］. IEEE Transactions on Industrial Electronics, 2019, 67(5): 4013-4023.
Hu X, Feng F, Liu K, et al. State estimation for advanced battery management: Key challenges and future trends［J］. Renewable and Sustainable Energy Reviews, 2019, 114: 109334.
Li C R, Xiao F, Fan Y X, et al. Joint SOC and SOH Estimation of Lithium ionized Battery Based on Deep Learning ［J］. Proceedings of the CSEE, 2021, 41(2): 681-692.
2023, Vol. 44 
