Physics-informed machine learning for accurate SOH estimation of lithium-ion batteries considering various temperatures and operating conditions

Accurate State of Health (SOH) estimation for lithium batteries (LIBs) is crucial for the safe operation of battery systems. However, the lack of physical properties and the varied operating conditions in real-world use further increase the difficulty of traditional SOH estimation, making it a significant challenge in current research. For this reason, this paper proposes a physics-informed machine learning (PIML) method for accurate SOH estimation of LIBs varied operating conditions. Considering the fully charged relaxation voltage data obtained easily in practical applications, firstly, this paper discussed the relaxation voltage data related to the battery's aging characteristics from the experimental…

• SU
  Sichuan University of Science and Engineering