An online state of health estimation method based on battery management system monitoring data. (8th April 2020)

• Record Type: Journal Article
• Authors: Liu, Fang; Liu, Xinyi; Su, Weixing; Lin, Hui; Chen, Hanning; He, Maowei
• Is Part Of: International journal of energy research. Volume 44:Number 8(2020)
• Page Start: 6338
• Page End: 6349
• Publication Date: 2020-04-08
• Subjects: battery mathematical model -- NLS‐GA -- SOH; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.5351
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods.