Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning
Jiahuan Lu(Beijing Institute of Technology), Ju Li(Beijing Institute of Technology), Rui Xiong(Beijing Institute of Technology), Chia‐Wei Hsu(National Yang Ming Chiao Tung University), Jinpeng Tian(Hong Kong Polytechnic University), Chenxu Wang(Jiangsu University), Fengchun Sun(Kangwon National University), Nien‐Ti Tsou(National Yang Ming Chiao Tung University)
Cited by 239
Related Papers
Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
|IEEE Access|2017|950
A review of supercapacitor modeling, estimation, and applications: A control/management perspective
|Renewable and Sustainable Energy Reviews|2017|921
State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model
|IEEE Transactions on Vehicular Technology|2011|785
Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives
|Renewable and Sustainable Energy Reviews|2020|699