时变温度环境下锂离子电池自适应SOC估计方法
|
摘要
目的针对低温环境下锂离子电池特性显著变化问题,为大规模锂离子电池组在极地科考船混合动力系统上的应用提供理论依据,方法对10 Ah高功率三元镍钴锰酸锂电池低温特性展开实验研究,结合实验数据,利用基于遗忘因子的递推最小二乘算法(FFRLS)分别与两种改进的卡尔曼滤波算法(AEKF、UKF)组成的串联观测器在线估计电池荷电状态(SOC)。结果在25～-30℃时变温度环境的改进DST工况下,FFRLS-AEKF算法的SOC估计精度略高于FFRLS-UKF算法,其最大估计误差为3.04%,均方根误差为0.69%。结论相比EKF与RLS-EKF算法,更好的模型参数与噪声信息的自适应性使FFRLS-AEKF算方法有更高的SOC估计精度与收敛性。
Objective Considering the significant change of battery characteristics at low temperature, to provide a theoretical basis for the application of large scale lithium ion battery packs in the hybrid power system of polar scientific expedition ships. Methods The low temperature characteristics of 10 Ah high power NCM lithium-ion battery were experimentally investigated. Combined with the experimental data, the state of charge(SOC) was estimated on line with a series observer, which was composed of recursive least squares algorithm with forgetting factor(FFRLS) and two improved kalman filtering algorithms(AEKF, UKF) respectively. Results The SOC estimated accuracy of FFRLS-AEKF algorithm was slightly higher than that of FFRLS-UKF algorithm under the improved DST condition of time-varying temperature environment within the temperature range of 25 ～-30 ℃, with the maximum estimated error of 3.04% and the root mean square error of 0.69%. Conclusion Compared with EKF and RLS-EKF algorithms, the better adaptability of model parameters and noise information makes FFRLS-AEKF algorithm have higher SOC estimated accuracy and convergence.
Objective Considering the significant change of battery characteristics at low temperature, to provide a theoretical basis for the application of large scale lithium ion battery packs in the hybrid power system of polar scientific expedition ships. Methods The low temperature characteristics of 10 Ah high power NCM lithium-ion battery were experimentally investigated. Combined with the experimental data, the state of charge(SOC) was estimated on line with a series observer, which was composed of recursive least squares algorithm with forgetting factor(FFRLS) and two improved kalman filtering algorithms(AEKF, UKF) respectively. Results The SOC estimated accuracy of FFRLS-AEKF algorithm was slightly higher than that of FFRLS-UKF algorithm under the improved DST condition of time-varying temperature environment within the temperature range of 25 ～-30 ℃, with the maximum estimated error of 3.04% and the root mean square error of 0.69%. Conclusion Compared with EKF and RLS-EKF algorithms, the better adaptability of model parameters and noise information makes FFRLS-AEKF algorithm have higher SOC estimated accuracy and convergence.
引文
[1]WANG X,SHANG J,LUO Z,et al.Reviews of Power Systems and Environmental Energy Conversion for Unmanned Underwater Vehicles[J].Renewable&Sustainable Energy Reviews,2012,16(4):1958-1970.
[2]赵世玺,郭双桃,赵建伟,等.锂离子电池低温特性研究进展[J].硅酸盐学报,2016,44(1):19-28.
[3]JI Y,ZHANG Y,WANG C Y.Li-Ion Cell Operation at Low Temperatures[J].Journal of The Electrochemical Society,2013,160(4):A636-A649.
[4]雷治国,张承宁,李军求,等.电动车用锂离子电池低温性能研究[J].汽车工程,2013,35(10):927-933.
[5]WANG C Y,ZHANG G,GE S,et al.Lithium-ion Battery Structure That Self-heats at Low Temperatures[J].Nature,2016,529(7587):515.
[6]HE H,ZHANG X,XIONG R,et al.Online Model-based Estimation of State-of-charge and Open-circuit Voltage of Lithium-ion Batteries in Electric Vehicles[J].Energy,2012,39(1):310-318.
[7]CHEN C,XIONG R,SHEN W.A Lithium-ion Battery-in-the-loop Approach to Test and Validate Multi-scale Dual H Infinity Filters for State of Charge and Capacity Estimation[J].IEEE Transactions on Power Electronics,2018(99):1.
[8]丁玲.锂离子动力电池正极材料发展综述[J].电源技术,2015,39(08):1780-1782.
[9]PETZL M,KASPER M,DANZER M A.Lithium Plating in a Commercial Lithium-ion Battery-A Low-temperature Aging Study[J].Journal of Power Sources,2015,275:799-807.
[10]UNKEHAEUSER T,SMALLWOOD D,Freedom CARBattery Test Manual for Power-assist Hybrid Electric Vehicles[J].U S Department of Energy,2003,4(1):3-6.
[11]陈全世,林成涛.电动汽车用电池性能模型研究综述[J].汽车技术,2005(3):1-5.
[12]JULIER S J,UHLMANN J K.Unscented Filtering and Nonlinear Estimation[J].Proceedings of the IEEE,2004,92(3):401-422.
[13]XIONG R,HE H,SUN F,et al.Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach[J].IEEE Transactions on Vehicular Technology,2013,62(1):108-117.
[2]赵世玺,郭双桃,赵建伟,等.锂离子电池低温特性研究进展[J].硅酸盐学报,2016,44(1):19-28.
[3]JI Y,ZHANG Y,WANG C Y.Li-Ion Cell Operation at Low Temperatures[J].Journal of The Electrochemical Society,2013,160(4):A636-A649.
[4]雷治国,张承宁,李军求,等.电动车用锂离子电池低温性能研究[J].汽车工程,2013,35(10):927-933.
[5]WANG C Y,ZHANG G,GE S,et al.Lithium-ion Battery Structure That Self-heats at Low Temperatures[J].Nature,2016,529(7587):515.
[6]HE H,ZHANG X,XIONG R,et al.Online Model-based Estimation of State-of-charge and Open-circuit Voltage of Lithium-ion Batteries in Electric Vehicles[J].Energy,2012,39(1):310-318.
[7]CHEN C,XIONG R,SHEN W.A Lithium-ion Battery-in-the-loop Approach to Test and Validate Multi-scale Dual H Infinity Filters for State of Charge and Capacity Estimation[J].IEEE Transactions on Power Electronics,2018(99):1.
[8]丁玲.锂离子动力电池正极材料发展综述[J].电源技术,2015,39(08):1780-1782.
[9]PETZL M,KASPER M,DANZER M A.Lithium Plating in a Commercial Lithium-ion Battery-A Low-temperature Aging Study[J].Journal of Power Sources,2015,275:799-807.
[10]UNKEHAEUSER T,SMALLWOOD D,Freedom CARBattery Test Manual for Power-assist Hybrid Electric Vehicles[J].U S Department of Energy,2003,4(1):3-6.
[11]陈全世,林成涛.电动汽车用电池性能模型研究综述[J].汽车技术,2005(3):1-5.
[12]JULIER S J,UHLMANN J K.Unscented Filtering and Nonlinear Estimation[J].Proceedings of the IEEE,2004,92(3):401-422.
[13]XIONG R,HE H,SUN F,et al.Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach[J].IEEE Transactions on Vehicular Technology,2013,62(1):108-117.