Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

• Published in: Energies (Basel) Vol. 11; no. 5; p. 1112
• Main Authors: Xie, Jiale, Ma, Jiachen, Chen, Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2018
• Subjects: adaptive extended Kalman filter; Parameter identification; Peukert equation; state-of-charge; thermal evolution model
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11051112

To achieve accurate state-of-charge (SoC) estimation for LiFePO4 (lithium iron phosphate) batteries under harsh conditions, this paper resorts to the Peukert’s law to accommodate different temperatures and load excitations. By analyzing battery heat generation and dissipation, a thermal evolution model (TEM) is elaborated and exploited for on-line parameter identification of the equivalent circuit model (ECM). Then, a SoC estimation framework is proposed based on the Adaptive Extended Kalman Filter (AEKF) algorithm. Experimental results on a LiFePO4 pack subject to the Federal Urban Driving Schedule (FUDS) profile under different temperatures and initial states suggest that the proposed SoC estimator provides good robustness and accuracy against changing temperature and highly dynamic loads.