A Data-Driven Based State of Energy Estimator of Lithium-ion Batteries Used to Supply Electric Vehicles. (August 2015)

• Record Type: Journal Article
• Authors: Zhang, Yong Zhi; He, Hong Wen; Xiong, Rui
• Is Part Of: Energy procedia. Volume 75(2015)
• Page Start: 1944
• Page End: 1949
• Publication Date: 2015-08
• Subjects: electric vehicles -- lithium-ion battery -- data-driven -- recursive least square -- extended Kalman filter -- state of energy; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2015.07.228
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: The state of energy (SoE) of Li-ion batteries is a critical index for the remainder range forecasting, energy optimization and management. The paper attempts to make three contributions. (1) The definition of SoE is proposed and elaborated, which includes the output energy of battery, the internal resistance heating and the energy consumed on the electrochemical reactions. Based on this definition, the new mathematical model of estimating SoE is built, which can realize the real-time estimation of SoE. (2) Based on the combined general battery model, the recursive least square (RLS) method with an optimal forgetting factor is used to identify the model parameters. The parameter identification results are obtained at relative SoE points, and the verification results indicate that the proposed battery model is accurate enough to simulate the battery characteristics. (3) Based on the SoE mathematical model and the combined general battery model, the extended Kalman filter (EKF) is built to estimate the SoE online. The simulation results show that the EKF-based SoE estimator performs well even under different incorrect initial SoE.