Voltage-Based State of Charge Correction at Charge-End

被引:2
作者
Abdollahi, Ali [1 ]
Li, Jianwei [1 ]
Li, Xiaojun [1 ]
Jones, Trevor [1 ]
Habeebullah, Asif [1 ]
机构
[1] Gotion Inc, Dept Applicat Software Design Got, Fremont, CA 94538 USA
来源
2021 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC) | 2021年
关键词
battery management systems; state of charge; SOC estimation; battery charging; EXTENDED KALMAN FILTER; LITHIUM-ION BATTERIES; COULOMB COUNTING METHOD; TEMPERATURE RISE; SOC ESTIMATION; NETWORKS; VEHICLES; SYSTEMS; MODEL; TIME;
D O I
10.1109/VPPC53923.2021.9699170
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A voltage-based method is proposed to correct battery pack state of charge (SOC) estimation at the charge-end. Two main characteristics make the charge-end time span a good opportunity to correct SOC estimation: first, it is easy to detect when the battery is at the last stage of charging because the charging profile is known to the BMS designer and also during the charge-end time span the amount of current is low, and the terminal voltage of the battery cells are high; second, as the battery reaches the charge-end stage, we know that the true SOC is approaching to 100%. This paper presents a method to utilize these important features to correct the SOC estimation error. Using a voltage threshold method, the algorithm detects when the battery is close to the charge-end to activate the charge-end SOC correction strategy. Once activated, the strategy corrects the SOC using the maximum cell voltage to guarantee that SOC is 100% when charging is complete. The amount of correction is a function of maximum cell voltage and the charge current C-rate.
引用
收藏
页数:6
相关论文
共 25 条
[1]   Optimal charging for general equivalent electrical battery model, and battery life management [J].
Abdollahi, A. ;
Han, X. ;
Raghunathan, N. ;
Pattipati, B. ;
Balasingam, B. ;
Pattipati, K. R. ;
Bar-Shalom, Y. ;
Card, B. .
JOURNAL OF ENERGY STORAGE, 2017, 9 :47-58
[2]   Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model [J].
Abdollahi, A. ;
Han, X. ;
Awari, G. V. ;
Raghunathan, N. ;
Balasingam, B. ;
Pattipati, K. R. ;
Bar-Shalom, Y. .
JOURNAL OF POWER SOURCES, 2016, 303 :388-398
[3]   State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach [J].
Chemali, Ephrem ;
Kollmeyer, Phillip J. ;
Preindl, Matthias ;
Emadi, Ali .
JOURNAL OF POWER SOURCES, 2018, 400 :242-255
[4]   Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries [J].
Chemali, Ephrem ;
Kollmeyer, Phillip J. ;
Preindl, Matthias ;
Ahmed, Ryan ;
Emadi, Ali .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2018, 65 (08) :6730-6739
[5]   State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering [J].
Chen, Zheng ;
Fu, Yuhong ;
Mi, Chunting Chris .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2013, 62 (03) :1020-1030
[6]   A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations [J].
Hannan, M. A. ;
Lipu, M. S. H. ;
Hussain, A. ;
Mohamed, A. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 78 :834-854
[7]   State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model [J].
He, Hongwen ;
Xiong, Rui ;
Zhang, Xiaowei ;
Sun, Fengchun ;
Fan, JinXin .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2011, 60 (04) :1461-1469
[8]   State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach [J].
How, Dickshon N. T. ;
Hannan, Mahammad A. ;
Lipu, Molla S. Hossain ;
Sahari, Khairul S. M. ;
Ker, Pin Jern ;
Muttaqi, Kashem M. .
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2020, 56 (05) :5565-5574
[9]   State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review [J].
How, Dickson N. T. ;
Hannan, M. A. ;
Lipu, M. S. Hossain ;
Ker, Pin Jern .
IEEE ACCESS, 2019, 7 :136116-136136
[10]  
Jeong YM, 2014, IEEE ENER CONV, P4313, DOI 10.1109/ECCE.2014.6953989