Abstract
A precise mathematical model is crucial for the battery management system to ensure the secure and stable battery operation. This paper presents a big data-driven battery management method utilizing the Support Vector Regression (SVR) algorithm, with the ability to work stably under dynamic conditions and whole battery life cycle. The rain-flow cycle counting algorithm is used to reflect the battery degradation phenomenon in this paper, and The SVR algorithm is used to establish the battery model. The idea is to reduce the impact of data quality on the model, so as to utilize the battery big data effectively and improve the battery modeling accuracy. Finally, a conjunction working mode between the Cloud-based BMS (C-BMS) and BMS in vehicles (V-BMS) is also proposed, provided as an applied case of the model. Using the battery data to verify the model effectiveness and accuracy, the error of the battery SoC estimation is within 3%.
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