A novel extended Kalman filter-based battery internal and surface temperature estimation based on an improved electro-thermal model. (September 2021)

• Record Type: Journal Article
• Authors: Pang, Hui; Guo, Long; Wu, Longxing; Jin, Jiamin; Zhang, Fengqi; Liu, Kai
• Is Part Of: Journal of energy storage. Volume 41(2021)
• Publication Date: 2021-09
• Subjects: Lithium-ion battery -- An improved electro-thermal model -- Battery internal and surface temperature -- Extended Kalman filter; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2021.102854
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: An electro-thermal model of lithium-ion battery is built up by considering the influence of a wide temperature variation on its terminal voltage and thermal behaviors. A novel extended Kalman filter-based battery internal and surface temperature estimation approach is proposed. Validations of the proposed battery temperature estimation are carried out under US06 condition in a wide temperature range. Abstract: 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.