论文信息 - A novel intelligent SOC prediction method of lithium-ion battery packs based on the improved unscented transformation - 字舞流文

A novel intelligent SOC prediction method of lithium-ion battery packs based on the improved unscented transformation

Fei Xie | Shun-Li Wang | Yanxin Xie | Coffie-ken James | Xueqing Liang | Shunli Wang | Fei Xie | Yanxin Xie | Xu Liang | Coffie-ken James

[1] Xuning Feng,et al. Online internal short circuit detection for a large format lithium ion battery , 2016 .

[2] Peng Zhang,et al. Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles , 2016 .

[3] Jianqiu Li,et al. Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles , 2018 .

[4] Binyu Xiong,et al. Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer , 2016 .

[5] Baoming Ge,et al. State-of-Charge Balancing Control for a Battery-Energy-Stored Quasi-Z-Source Cascaded-Multilevel-Inverter-Based Photovoltaic Power System , 2018, IEEE Transactions on Industrial Electronics.

[6] Lingyan Wang,et al. A new method of modeling and state of charge estimation of the battery , 2016 .

[7] Ping Shen,et al. The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles , 2018, IEEE Transactions on Vehicular Technology.

[8] D. Stone,et al. A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states , 2016 .