基于Drift-Ah积分法的CKF估算锂电池SOC
|
摘要
锂电池荷电状态(SOC)是反映电池使用情况的重要参数之一.在锂电池实际工作过程中,电流传感器测量时的漂移电流会对SOC估计精度造成很大影响.对此,提出一种加入漂移电流的Drift-Ah积分法,建立SOC的噪声组合模型,并采用容积卡尔曼滤波算法(CKF)实现锂电池的SOC估计.最后,对锂电池进行模拟工况实验,仿真结果表明,所提出的估计算法可以有效抑制漂移电流的干扰,精度高且复杂度低.
State of charge(SOC) is one of the important parameters that reflects battery usage. In the actual work process of lithium battery, the drift current measured by current sensor will have great influence on the accuracy of SOC estimation. For this problem, the Drift-Ah integral method is proposed with drift current, the noise combination model is established, and the SOC estimation of lithium battery is achieved by using the cubature Kalman filter(CKF). Finally, the simulation experiment of lithium battery is carried out. Simulation results show that the proposed method can effectively suppress disturbance of drift current, with the advantages of high filtering accuracy and low complexity.
State of charge(SOC) is one of the important parameters that reflects battery usage. In the actual work process of lithium battery, the drift current measured by current sensor will have great influence on the accuracy of SOC estimation. For this problem, the Drift-Ah integral method is proposed with drift current, the noise combination model is established, and the SOC estimation of lithium battery is achieved by using the cubature Kalman filter(CKF). Finally, the simulation experiment of lithium battery is carried out. Simulation results show that the proposed method can effectively suppress disturbance of drift current, with the advantages of high filtering accuracy and low complexity.
引文
[1]刘胜永,于跃,罗文广,等.基于自适应无迹卡尔曼滤波的锂电池SOC估计[J].控制工程, 2017, 24(8):1611-1616.(Liu S Y, Yu Y, Luo W G, et al. Estimation of state of charge for lithium battery based on adaptive unscented Kalman filter[J]. Control Engineering of China, 2017,24(8):1611-1616.)
[2]刘红锐,张昭怀.锂离子电池组充放电均衡器及均衡策略[J].电工技术学报, 2015, 30(8):186-192.(Liu H R, Zhang Z H. The equalizer of charging and discharging and the banlancing strategies for lithium-ion battery pack[J]. Trans of China Electrotechnical Society,2015, 30(8):186-192.)
[3] Nejad S, Gladwin D T, Stone D A. A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states[J]. J of Power Sources, 2016, 316:183-196.
[4] Yan Qiyan, Wang Yanning. Predicting for power battery SOC based on neural network[C]. The 36th Chinese Control Conf. Dalian, 2017:4140-4143.
[5]袁学庆,张阳,赵林,等.基于EKF的锂电池SOC估算与试验研究[J].电源技术, 2015, 139(12):2587-2589.(Yuan X Q, Zhang Y, Zhao L, et al. Li-ion battery SOC estimation and test research based on EKF[J]. Power Technology, 2015, 139(12):2587-2589.)
[6]杨春生,牛红涛,隋良红,等.基于贝叶斯正则化算法BP神经网络钒电池SOC预测[J].现代电子技术,2016, 39(8):158-161.(Yang C S, Niu H T, Sui L H, et al. Application of BP neural network improved by Bayesian regularization algorithm in VRB SOC prediction[J]. Modern Electronics Technique, 2016, 39(8):158-161.)
[7]刘征宇,杨俊斌,张庆,等.基于QPSO-BP神经网络的锂电池SOC预测[J].电子测量与仪器学报, 2013,27(3):224-228.(Liu Z Y, Yang J B, Zhang Q, et al. Estimation for SOC of lithium battery based on QPSO-BP neural network[J]. J of Electronic Measurement and Instrument, 2013, 27(3):224-228.)
[8]王世元,黄锦旺,谢智刚,等.非线性卡尔曼滤波器原理及应用[M].北京:电子工业出版社, 2015:22-25,64-66.(Wang S Y, Huang J W, Xie Z G, et al. Principles of nonlinear Kalman filters and their applications[M].Beijing:Publishing House of Electronics Industry, 2015:22-25, 64-66.)
[9] Tian Y, Xia B, Sun W, et al. A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter[J]. J of Power Sources, 2014,270:619-626.
[10]孔祥创,赵万忠,王春燕.基于BP-EKF算法的锂电池SOC联合估计[J].汽车工程, 2017, 39(6):648-652.(Kong X C, Zhao W Z, Wang C Y. Co-estimation of lithium battery SOC based on BP-EKF algorithm[J].Automotive Engineering, 2017, 39(6):648-652.)
[11] Sheng Hanmin, Xiao Jian, Wang Peng. Lithium iron phosphate battery electric vehicle state-of-charge estimation based on evolutionary gaussian mixture regression[J]. IEEE Trans on Industrial Electronics, 2017,64(1):544-551.)
[12] Liu Cong-zhi, Zhu Qiao, Li Liang, et al. A state of charge estimation method based on H∞observer for switched systems of litjium-ion nickel-manganese-cobalt batteries[J]. IEEE Trans on Industrial Electronics, 2017,64(10):8128-8137.
[13] He Ting, Li Donghai, Wu Zhenlong, et al. A modified luenberger observer for SOC estimation of lithium-ion battery[C]. The 36th Chinese Control Conf. Dalian, 2017:927-928.
[14]何晓兰.开环霍尔直流电流传感器及其校正方法设计[D].北京:中国科学院大学工程科学学院, 2015.(He X L. Open loop hall effect DC current sensor design and calibration[D]. Beijing:College of Engineering Sciences, University of Chinese Academy of Sciences,2015.)
[15]郑岳久,许霜霜,张振东.三元锂电池荷电状态估计的传感器误差影响[J].汽车安全与节能学报, 2017, 8(2):198-204.(Zheng Y J, Xu S S, Zhang Z D. Effect of sensor errors on stat e of charge estimation for ternary lithiumion cells[J].J of Automotive Safety and Energy, 2017, 8(2):198-204.)
[16]刘新天,刘兴涛,何耀,等.基于Vmin-EKF的动力锂电池组SOC估计[J].控制与决策, 2010, 25(3):445-448.(Liu X T, Liu X T, He Y, et al. Based Vmin-EKF SOC estimation for power li-ion battery pack[J]. Control and Decision, 2010, 25(3):445-448.)
[17] Li J H, Barillas J K, Guenther C, et al. A comparative study of state of charge estimation algorithms for LiFePO4, batteries used in electric vehicles[J]. J of Power Sources, 2013, 230(10):244-250.
[18] Plett G L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery pack[J]. J of Power Sources, 2004, 134(2):277-292.
[19]王小旭,潘泉,黄鹤,等.非线性系统确定采样型滤波算法综述[J].控制与决策, 2012, 27(6):801-812.(Wang X X, Pan Q, Huang H, et al. Overview of deterministic sampling filtering algorithms for nonlinear system[J]. Control and Decision, 2012, 27(6):801-812.)
[20] Arasaratnam I, Haykin S. Cubature Kalman filtering[J].IEEE Trans on Automatic Control, 2009, 54(6):1254-1269.
[21]丁国强.非线性系统建模与滤波方法[M].北京:北京航空航天大学出版社, 2017:150-153.(Ding G Q. Modeling and filtering methods for nonlinear system[M]. Beijing:Beihang University Press, 2017:150-153.)
[22] Xiong K, Zhang H Y, Chan C W. Performance evaluation of UKF-based nonlinear filtering[J]. Automatica, 2006,42(2):261-270.
[23] Xiong K, Zhang H Y, Chan C W. Author’s reply to“Comments on ‘Performance evaluation of UKF-based nonlinear filtering’”[J]. Automatica, 2007, 43(3):569-570.
[24] QC/T 743-2006,电动汽车用锂离子蓄电池[S]. QC/T743-2006, Lithium-ion batteries for electric vehicles[S].
[25] Li W L, Liang L, iu W J, et al. State of charge estimation of lithium-ion batteries using a discrete-time nonlinear observer[J]. IEEE Trans on Industrial Electronics, 2017,64(11):8557-8565.
[26] Chaoui H, Ibe-Ekeocha C C. State of charge and state of health estimation for lithium batteries using recurrent neural networks[J]. IEEE Trans on Vehicular Technology,2017, 66(10):8773-8783.
[27] Guo Yifeng, Zhao Zeshuang, Huang Limin. SoC estimation of lithium battery based on improved BP neural metwork[C]. The 8th Int Conf on Applied Engery.Beijiang, 2017:4153-4158.
[28] Wang Baojin, Liu Zhiyuan, Shengbo Eben Li, et al.State-of-charge estimation for lithium-ion batteries based on a nonlinear fractional model[J]. IEEE Trans on Control System Technology, 2017, 25(1):3-11.
[2]刘红锐,张昭怀.锂离子电池组充放电均衡器及均衡策略[J].电工技术学报, 2015, 30(8):186-192.(Liu H R, Zhang Z H. The equalizer of charging and discharging and the banlancing strategies for lithium-ion battery pack[J]. Trans of China Electrotechnical Society,2015, 30(8):186-192.)
[3] Nejad S, Gladwin D T, Stone D A. A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states[J]. J of Power Sources, 2016, 316:183-196.
[4] Yan Qiyan, Wang Yanning. Predicting for power battery SOC based on neural network[C]. The 36th Chinese Control Conf. Dalian, 2017:4140-4143.
[5]袁学庆,张阳,赵林,等.基于EKF的锂电池SOC估算与试验研究[J].电源技术, 2015, 139(12):2587-2589.(Yuan X Q, Zhang Y, Zhao L, et al. Li-ion battery SOC estimation and test research based on EKF[J]. Power Technology, 2015, 139(12):2587-2589.)
[6]杨春生,牛红涛,隋良红,等.基于贝叶斯正则化算法BP神经网络钒电池SOC预测[J].现代电子技术,2016, 39(8):158-161.(Yang C S, Niu H T, Sui L H, et al. Application of BP neural network improved by Bayesian regularization algorithm in VRB SOC prediction[J]. Modern Electronics Technique, 2016, 39(8):158-161.)
[7]刘征宇,杨俊斌,张庆,等.基于QPSO-BP神经网络的锂电池SOC预测[J].电子测量与仪器学报, 2013,27(3):224-228.(Liu Z Y, Yang J B, Zhang Q, et al. Estimation for SOC of lithium battery based on QPSO-BP neural network[J]. J of Electronic Measurement and Instrument, 2013, 27(3):224-228.)
[8]王世元,黄锦旺,谢智刚,等.非线性卡尔曼滤波器原理及应用[M].北京:电子工业出版社, 2015:22-25,64-66.(Wang S Y, Huang J W, Xie Z G, et al. Principles of nonlinear Kalman filters and their applications[M].Beijing:Publishing House of Electronics Industry, 2015:22-25, 64-66.)
[9] Tian Y, Xia B, Sun W, et al. A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter[J]. J of Power Sources, 2014,270:619-626.
[10]孔祥创,赵万忠,王春燕.基于BP-EKF算法的锂电池SOC联合估计[J].汽车工程, 2017, 39(6):648-652.(Kong X C, Zhao W Z, Wang C Y. Co-estimation of lithium battery SOC based on BP-EKF algorithm[J].Automotive Engineering, 2017, 39(6):648-652.)
[11] Sheng Hanmin, Xiao Jian, Wang Peng. Lithium iron phosphate battery electric vehicle state-of-charge estimation based on evolutionary gaussian mixture regression[J]. IEEE Trans on Industrial Electronics, 2017,64(1):544-551.)
[12] Liu Cong-zhi, Zhu Qiao, Li Liang, et al. A state of charge estimation method based on H∞observer for switched systems of litjium-ion nickel-manganese-cobalt batteries[J]. IEEE Trans on Industrial Electronics, 2017,64(10):8128-8137.
[13] He Ting, Li Donghai, Wu Zhenlong, et al. A modified luenberger observer for SOC estimation of lithium-ion battery[C]. The 36th Chinese Control Conf. Dalian, 2017:927-928.
[14]何晓兰.开环霍尔直流电流传感器及其校正方法设计[D].北京:中国科学院大学工程科学学院, 2015.(He X L. Open loop hall effect DC current sensor design and calibration[D]. Beijing:College of Engineering Sciences, University of Chinese Academy of Sciences,2015.)
[15]郑岳久,许霜霜,张振东.三元锂电池荷电状态估计的传感器误差影响[J].汽车安全与节能学报, 2017, 8(2):198-204.(Zheng Y J, Xu S S, Zhang Z D. Effect of sensor errors on stat e of charge estimation for ternary lithiumion cells[J].J of Automotive Safety and Energy, 2017, 8(2):198-204.)
[16]刘新天,刘兴涛,何耀,等.基于Vmin-EKF的动力锂电池组SOC估计[J].控制与决策, 2010, 25(3):445-448.(Liu X T, Liu X T, He Y, et al. Based Vmin-EKF SOC estimation for power li-ion battery pack[J]. Control and Decision, 2010, 25(3):445-448.)
[17] Li J H, Barillas J K, Guenther C, et al. A comparative study of state of charge estimation algorithms for LiFePO4, batteries used in electric vehicles[J]. J of Power Sources, 2013, 230(10):244-250.
[18] Plett G L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery pack[J]. J of Power Sources, 2004, 134(2):277-292.
[19]王小旭,潘泉,黄鹤,等.非线性系统确定采样型滤波算法综述[J].控制与决策, 2012, 27(6):801-812.(Wang X X, Pan Q, Huang H, et al. Overview of deterministic sampling filtering algorithms for nonlinear system[J]. Control and Decision, 2012, 27(6):801-812.)
[20] Arasaratnam I, Haykin S. Cubature Kalman filtering[J].IEEE Trans on Automatic Control, 2009, 54(6):1254-1269.
[21]丁国强.非线性系统建模与滤波方法[M].北京:北京航空航天大学出版社, 2017:150-153.(Ding G Q. Modeling and filtering methods for nonlinear system[M]. Beijing:Beihang University Press, 2017:150-153.)
[22] Xiong K, Zhang H Y, Chan C W. Performance evaluation of UKF-based nonlinear filtering[J]. Automatica, 2006,42(2):261-270.
[23] Xiong K, Zhang H Y, Chan C W. Author’s reply to“Comments on ‘Performance evaluation of UKF-based nonlinear filtering’”[J]. Automatica, 2007, 43(3):569-570.
[24] QC/T 743-2006,电动汽车用锂离子蓄电池[S]. QC/T743-2006, Lithium-ion batteries for electric vehicles[S].
[25] Li W L, Liang L, iu W J, et al. State of charge estimation of lithium-ion batteries using a discrete-time nonlinear observer[J]. IEEE Trans on Industrial Electronics, 2017,64(11):8557-8565.
[26] Chaoui H, Ibe-Ekeocha C C. State of charge and state of health estimation for lithium batteries using recurrent neural networks[J]. IEEE Trans on Vehicular Technology,2017, 66(10):8773-8783.
[27] Guo Yifeng, Zhao Zeshuang, Huang Limin. SoC estimation of lithium battery based on improved BP neural metwork[C]. The 8th Int Conf on Applied Engery.Beijiang, 2017:4153-4158.
[28] Wang Baojin, Liu Zhiyuan, Shengbo Eben Li, et al.State-of-charge estimation for lithium-ion batteries based on a nonlinear fractional model[J]. IEEE Trans on Control System Technology, 2017, 25(1):3-11.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |