A novel extended Kalman filter-based battery internal and surface temperature estimation based on an improved electro-thermal model

The lithium-ion battery temperature monitoring plays a prominent role in developing an appropriate battery thermal management system to ensure battery security, and improve battery performances, and it is also a vital fundamental module for the development of battery management system. To address this issue, this paper proposes a novel estimation procedure for the internal and surface temperatures of a lithium-ion battery cell based on an improved electro-thermal model (IETM). First, an electro-thermal model of lithium-ion battery is built up by combing the temperature-dependent electrical model and the two-state thermal model together. Then, the parameterization scheme of this IETM is carried out to facilitate the real-time estimation of the battery internal and surface temperatures. Furthermore, based on the extended Kalman filter (EKF), the battery IETM is employed to estimate both the battery internal and surface temperatures without requiring extra temperature sensors. Finally, the effectiveness and feasibility of the proposed battery temperature estimation method are demonstrated by performing the simulation and experiment verification.

Automated summary

This paper proposes a novel estimation procedure for the internal and surface temperatures of a lithium-ion battery cell based on an improved electro-thermal model, which can estimate the battery temperatures in real-time without requiring extra temperature sensors. The effectiveness and feasibility of the proposed method are demonstrated through simulation and experiment verification.