Open-circuit voltage-based state of charge estimation of lithium-ion power battery by combining controlled auto-regressive and moving average modeling with feedforward-feedback compensation method

被引:70
作者
Dang, Xuanju [1 ]
Yan, Li [1 ]
Jiang, Hui [1 ]
Wu, Xiru [1 ]
Sun, Hanxu [2 ]
机构
[1] Guilin Univ Elect Technol, Sch Elect & Automat, 1 Jinji Rd, Guilin 541004, Peoples R China
[2] Beijing Univ Posts & Telecommun, Sch Automat, Beijing 100876, Peoples R China
关键词
Power battery; State of charge; Controlled auto-regressive and moving; average model; Feedforward-feedback; OF-CHARGE; ELECTRIC VEHICLES; NEURAL-NETWORK; PARAMETER; OBSERVER; FILTER; SOC;
D O I
10.1016/j.ijepes.2017.01.013
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
Being one of the important parameters describing the state of power battery, state of charge (SOC) is essential for the electric vehicle battery management system (BMS). SOC estimation method, which combines the constructed controlled auto-regressive and moving average (CARMA) model with the feedforward-feedback compensation method used for revising SOC by the deviation of terminal voltage, is presented in this paper. Fully taken into account the measurement errors of voltage and current, the CARMA model is employed to estimate the battery open-circuit voltage (OCV). With the good consistency of the OCV-SOC curve under the process of battery charge and discharge cycles within a certain temperature range, OCV is adopted to estimate SOC. BP neural network rather than the high order polynomial approximation is used to capture the strong nonlinear relationship between OCV and SOC with the high precision. It is a big challenge for OCV-based SOC estimation that the flat area of OCV-SOC curve for lithium-ion power battery enlarges the measurement errors of OCV. By analyzing the flat characteristic of Delta SOC-OCV curve, the feedforward-feedback compensation for SOC is used for improving the accuracy of OCV-based SOC estimation. Experiment results confirm the effectiveness of the proposed approach that has evidently advantages over other estimation methods. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:27 / 36
页数:10
相关论文
共 40 条
[1]
Support Vector Machines Used to Estimate the Battery State of Charge [J].
Alvarez Anton, Juan Carlos ;
Garcia Nieto, Paulino Jose ;
Blanco Viejo, Cecilio ;
Vilan Vilan, Jose Antonio .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2013, 28 (12) :5919-5926
[2]
Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries [J].
Andre, Dave ;
Appel, Christian ;
Soczka-Guth, Thomas ;
Sauer, Dirk Uwe .
JOURNAL OF POWER SOURCES, 2013, 224 :20-27
[3]
Variable Forgetting Factor Recursive Least Square Control Algorithm for DSTATCOM [J].
Badoni, Manoj ;
Singh, Alka ;
Singh, Bhim .
IEEE TRANSACTIONS ON POWER DELIVERY, 2015, 30 (05) :2353-2361
[4]
Bao K, 2012, COMPUT ENG SCI, V12
[5]
Fuzzy modelling for the state-of-charge estimation of lead-acid batteries [J].
Burgos, Claudio ;
Saez, Doris ;
Orchard, Marcos E. ;
Cardenas, Roberto .
JOURNAL OF POWER SOURCES, 2015, 274 :355-366
[6]
Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA [J].
Chang, Wen-Yeau .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2013, 53 :603-611
[7]
Chaoui H, P IEEE INT C IND TEC, P39
[8]
Chen X, 2015, IEEE T VEH TECHNOL, V99
[9]
A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles [J].
Chen, Xiaopeng ;
Shen, Weixiang ;
Cao, Zhenwei ;
Kapoor, Ajay .
JOURNAL OF POWER SOURCES, 2014, 246 :667-678
[10]
Chen ZH, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), P2156, DOI 10.1109/IJCNN.2011.6033495