-
摘要:
锂离子电池的荷电状态(SOC)和电池容量估计是电池管理系统的核心。由于SOC和容量在估计过程中参数相互影响,提出一种适用于三元锂离子电池SOC及容量的多尺度联合估计方法。采用戴维宁等效电路模型,建立数学模型及状态空间方程。针对不同温度下电池特性不同的问题,在不同温度下开展了模型参数辨识,建立了参数随SOC及温度的变化关系。基于双扩展卡尔曼滤波(DEKF)算法建立了电池状态多尺度联合估计模型,对电池的SOC、极化电压在微观时间尺度上进行估计,对电池的容量在宏观时间尺度上进行估计,并对SOC估计中的容量进行更新,保证了电池长期估计的精度。在宽温度范围内进行验证,所建立的三元锂离子电池多尺度联合估计方法具有较高的精度。
Abstract:The State of Charge (SOC) and battery capacity estimation of lithium-ion battery are the core of battery management system. Because the parameters of SOC and capacity affect each other in the estimation process, a multi-scale joint estimation method for SOC and capacity of ternary lithium-ion battery is proposed. In this paper, we use the equivalent circuit model of Thevenin to establish the mathematical model and state space equation. In order to solve the problem of different characteristics of battery at different temperatures, model parameters identification was carried out at different temperatures, and the two-dimensional pulse spectrograms of parameters related to SOC and temperature were established. Based on the Dual Extended Kalman Filter (DEKF), a multi-scale joint estimation model of battery state is established. The SOC and polarization voltage of the battery are estimated on the micro-time scale, the capacity of the battery is estimated on the macro-time scale, and the capacity of the SOC estimation is updated to ensure the accuracy of battery long-term estimation. Finally, the proposed multi-scale joint estimation algorithm for ternary lithium-ion battery is validated in a wide temperature range, and the result shows that it has high accuracy.
-
Key words:
- lithium-ion battery /
- State of Charge (SOC) /
- capacity /
- joint estimation /
- multi-time scale
-
表 1 国轩三元锂离子电池主要技术指标
Table 1. Main technical indicators of Guoxuanternary lithium-ion battery
参数 数值 1C设计容量/Ah 57 放电截止电压/V 3.0 充电截止电压/V 4.2 放电温度范围/℃ -30~50 充电温度范围/℃ 0~50 最大持续充电倍率 2C 最大持续放电倍率 5C 额定电压/V 3.6 10s最大充电倍率 7.5C 10s最大放电倍率 10C -
[1] RIVERA-BARRERA J P, MUÑOZ-GALEANO N, SARMIENTO-MALDONADO H O.SoC estimation for lithium-ion batteries:Review and future challenges[J].Electronics, 2017, 6(4):102. doi: 10.3390/electronics6040102 [2] ANDWARI A M, PESIRIDIS A, RAJOO S, et al.A review of battery electric vehicle technology and readiness levels[J].Renewable and Sustainable Energy Reviews, 2017, 78:414-430. doi: 10.1016/j.rser.2017.03.138 [3] 杨世春, 麻翠娟.基于PNGV改进模型的SOC估计算法[J].汽车工程, 2015, 37(5):582-586. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qcgc201505018YANG S C, MA C J.SOC estimation algorithm based on improved PNGV model[J].Automotive Engneering, 2015, 37(5):582-586(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qcgc201505018 [4] LI Z, HUANG J, LIAW B Y, et al.On state-of-charge determination for lithium-ion batteries[J].Journal of Power Sources, 2017, 348:281-301. doi: 10.1016/j.jpowsour.2017.03.001 [5] 徐颖, 沈英.基于改进卡尔曼滤波的电池SOC估算[J].北京航空航天大学学报, 2014, 40(6):855-860. doi: 10.13700/j.bh.1001-5965.2016.0796XU Y, SHEN Y.Improved battery state-of-charge estimation based on Kalman filter[J].Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(6):855-860(in Chinese). doi: 10.13700/j.bh.1001-5965.2016.0796 [6] HANNAN M A, LIPU M S H, HUSSAIN A, et al.A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications:Challenges and recommendations[J].Renewable and Sustainable Energy Reviews, 2017, 78:834-854. doi: 10.1016/j.rser.2017.05.001 [7] 印学浩, 宋宇晨, 刘旺, 等.基于多时间尺度的锂离子电池状态联合估计[J].仪器仪表学报, 2018, 39(8):118-126. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201808013YIN X H, SONG Y C, LIU W, et al.Multi-scale state joint estimation for lithium-ion battery[J].Chinese Journal of Scientific Instrument, 2018, 39(8):118-126(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yqyb201808013 [8] HU C, YOUN B D, CHUNG J.A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation[J].Applied Energy, 2012, 92:694-704. doi: 10.1016/j.apenergy.2011.08.002 [9] PLETT G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs-Part 1.Background[J].Journal of Power Sources, 2004, 134(2):252-261. doi: 10.1016/j.jpowsour.2004.02.031 [10] HE H W, ZHANG Y Z, XIONG R, et al.A novel Gaussian model based battery state estimation approach:State-of-energy[J].Applied Energy, 2015, 151:41-48. doi: 10.1016/j.apenergy.2015.04.062 [11] SUN F, XIONG R, HE H.Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions[J].Journal of Power Sources, 2014, 259:166-176. doi: 10.1016/j.jpowsour.2014.02.095 [12] LIU C, LIU W, WANG L, et al.A new method of modeling and state of charge estimation of the battery[J].Journal of Power Sources, 2016, 320:1-12. doi: 10.1016/j.jpowsour.2016.03.112 [13] SCHWUNK S, ARMBRUSTER N, STRAUB S, et al.Particle filter for state of charge and state of health estimation for lithium-iron phosphate batteries[J].Journal of Power Sources, 2013, 239:705-710. doi: 10.1016/j.jpowsour.2012.10.058 [14] ROSCHER M A, ASSFALG J, BOHLEN O S.Detection of utilizable capacity deterioration in battery systems[J].IEEE Transactions on Vehicular Technology, 2011, 60(1):98-103. doi: 10.1109/TVT.2010.2090370 [15] PLETT G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs-Part 3.State and parameter estimation[J].Journal of Power Sources, 2004, 134(2):277-292. doi: 10.1016/j.jpowsour.2004.02.033 [16] YANG S C, DENG C, ZHANG Y L, et al.State of charge estimation for lithium-ion battery with a temperature-compensated model[J].Energies, 2017, 10(10):1560. doi: 10.3390/en10101560 [17] SHEN P, OUYANG M G, LU L G, et al.The co-estimation of state of charge, state of health and state of function for lithium-ion batteries in electric vehicles[J].IEEE Transactions on Vehicular Technology, 2017, 67(1):92-103. http://ieeexplore.ieee.org/document/8036285/ [18] 刘新天, 孙张驰, 何耀, 等.基于环境变量建模的锂电池SOC估计方法[J].东南大学学报(自然科学版), 2017, 47(2):306-312. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dndxxb201702018LIU X T, SUN Z C, HE Y, et al.SOC estimation method based on lithium-ion cell model considering environmental factors[J].Journal of Southeast University (Natural Science Edition), 2017, 47(2):306-312(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dndxxb201702018 [19] YANG J F, XIA B, SHANG Y L, et al.Adaptive state-of-charge estimation based on a split battery model for electric vehicle applications[J].IEEE Transactions on Vehicular Technology, 2017, 66(12):10889-10898. doi: 10.1109/TVT.2017.2728806 [20] XIONG R, CAO J Y, YU Q Q, et al.Critical review on the battery state of charge estimation methods for electric vehicles[J].IEEE Access, 2018, 6:1832-1843. doi: 10.1109/ACCESS.2017.2780258