Volume 01: Proceedings of Applied Energy Symposium: MIT A+B, United States, 2019

Big data driven Deep Learning algorithm based Lithium-ion battery SoC estimation method: A hybrid mode of C-BMS and V-BMS Shuangqi Li, Hongwen He, Jianwei Li, Hanxiao Wang


Batteries are the bottleneck technology of electric vehicles (EVs), which hosts complex and hardly observable internal chemical reactions. This paper presents a big data-driven battery management method utilizing the deep learning algorithm, with the ability to work stably under dynamic conditions and whole battery life cycle. First, a Deep Belief Network-Extreme Learning Machine (DBN-ELM) algorithm-based battery model is established to extract the deep structure features of battery data, and in which the rain-flow cycle counting algorithm is used to reflect the battery degradation phenomenon. Next, to improve real-time performance of Battery Management System (BMS), a conjunction working mode between the Cloud-based BMS (C-BMS) and BMS in vehicles (V-BMS) is proposed, and a battery State of Charge (SoC) estimation method based on the interaction between C-BMS and V-BMS is also presented. Using the battery data to verify the model effectiveness and accuracy, the error of the battery SoC estimation is within 3%.

Keywords electric vehicle, battery energy storage, battery management system, big data, deep learning

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