Enhancing cross-temperature state-of-charge estimation accuracy for lithium-ion batteries using multi-physics features and physical guidance
Authors:
- Yan WuYWYan Wu
- Tong WangTWTong Wang
- Keming ZhuKZKeming Zhu
- Yingying XuYXYingying Xu
- Haoyuan MaHMHaoyuan Ma
- Jiayuan LuoJLJiayuan Luo
- Xiaoyu TangXiaoyu Tang
- Yuqi HuangYuqi Huang
Abstract
No abstract availableThis publication has 54 references indexed in Scilit:
- Battery state of health estimation under dynamic operations with physics-driven deep learningApplied Energy, 2024
- Advancing fault diagnosis in next-generation smart battery with multidimensional sensorsApplied Energy, 2024
- Low temperature preheating techniques for Lithium-ion batteries: Recent advances and future challengesApplied Energy, 2022
- A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimationApplied Energy, 2022
- A novel non-experiment-based reconstruction method for the relationship between open-circuit-voltage and state-of-charge/state-of-energy of lithium-ion batteryElectrochimica Acta, 2022
- State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approachApplied Energy, 2021
- State of charge estimation for lithium-ion pouch batteries based on stress measurementEnergy, 2017
- Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical modelApplied Energy, 2016
- State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperaturesApplied Energy, 2014
- Stress development due to surface processes in graphite electrodes for Li-ion batteries: A first reportElectrochimica Acta, 2012