The following publications are possibly variants of this publication:
- Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric VehiclesXiaopeng Chen, Weixiang Shen, Mingxiang Dai, Zhenwei Cao, Jiong Jin, Ajay Kapoor. tvt, 65(4):1936-1947, 2016. [doi]
- Lithium-Ion Batteries State of Charge Prediction of Electric Vehicles Using RNNs-CNNs Neural NetworksFen Zhao, Yinguo Li, Xinheng Wang, Ling Bai, Tailin Liu. access, 8:98168-98180, 2020. [doi]
- The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric VehiclesPing Shen, Minggao Ouyang, Languang Lu, Jianqiu Li, Xuning Feng. tvt, 67(1):92-103, 2018. [doi]
- Lithium-Ion Battery State of Charge and State of Power Estimation Based on a Partial-Adaptive Fractional-Order Model in Electric VehiclesRuohan Guo, Weixiang Shen. tie, 70(10):10123-10133, October 2023. [doi]
- A Model Fusion Method for Online State of Charge and State of Power Co-Estimation of Lithium-Ion Batteries in Electric VehiclesRuohan Guo, Weixiang Shen. tvt, 71(11):11515-11525, 2022. [doi]
- Robust and accurate state-of-charge estimation for lithium-ion batteries using generalized extended state observerYu Song, Weirong Liu, Heng Li, Yanhui Zhou, Zhiwu Huang, Fu Jiang. SMC 2017: 2146-2151 [doi]