Lithium-Ion Battery SOC Estimation and Hardware-in-the-Loop Simulation Based on EKF. (February 2019)

• Record Type: Journal Article
• Authors: Guo, Lin; Li, Junqiu; Fu, Zijian
• Is Part Of: Energy procedia. Volume 158(2019)
• Page Start: 2599
• Page End: 2604
• Publication Date: 2019-02
• Subjects: state of charge -- parameter identification -- Extended Kalman Filter -- hardware-in-the-loop -- bench test; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2019.02.009
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: It is difficult to estimate accurate SoC of power battery and meet the practical application due to the complexity of the algorithm. To promote the real-time and feasibility of the SoC algorithm, this paper proposes a set of solutions and verification taking lithium-ion battery as an example: First, an equivalent circuit model is established and a series of power battery tests were designed to provide data support for battery model and off-line parameter identification. In addition, the Extended Kalman Filter (EKF) algorithm is used for the SoC Estimation. To verify the feasibility and effectiveness of the SoC algorithm in the battery management system (BMS). The paper builds the model with chip computing capabilities and performs hardware-in-the-loop simulation tests using SpeedGoat as a platform and the Real-Time Workshop as an automatic code generation tool. Furthermore, the bench experiment of the power battery module is set up to verify and test the SoC algorithm. The results show that the proposed SoC estimation is more practical with the actual SoC estimation error less than 5%.