基于BMS与云平台的动力电池健康管理体系

The Power Battery Health Management System Based on BMS and Cloud Platform

摘要:

为解决电动汽车现有BMS系统对锂离子动力电池SOH评估与预测难以满足多种工况条件、各种类动力电池,且难同时兼顾预测精度与反馈速度等应用缺陷,提出了一套全新的EVs电池健康管理系统设计思路,采用了结合云计算与存储平台,融入BMS评估体系等关键方法；通过BMS增加5G通讯模块,利用5G/4G信号实时上传电芯数据,经过云平台搭载的多种SOH评估模型与算法,多线程在线计算得到预测结果,及时反馈至用户端和BMS,实现电池健康管理；该体系的设计案例展示出较好的未来应用价值,为电动汽车电池管理设计提供了新方向。

Abstract:

To solve the electric vehicles battery management system(BMS) for lithium ion power battery state of health(SOH) assessment and prediction is difficult to meet a variety of working conditions, various types of power battery, and difficult to balance between accuracy and feedback speed applications such as defects, puts forward a new set of EVs battery health management system design idea, adopting the combination of cloud computing and storage platform, Integrate BMS evaluation system and other key methods; Add 5G communication module through BMS, use 5G/4G signal to upload cell data in real time, through a variety of SOH evaluation models and algorithms equipped with cloud platform, multi-thread online calculation to get the prediction results, timely feedback to the user and BMS, to achieve battery health management. The design case of this system shows good future application value and provides a new direction for EV battery management design. To solve the electric car existing BMS system for lithium ion power battery SOH assessment and prediction is difficult to meet a variety of working conditions, various types of power battery, and difficult to balance between accuracy and feedback speed applications such as defects, puts forward a new set of EVs battery health management system design idea, adopting the combination of cloud computing and storage platform, Integrate BMS evaluation system and other key methods; Add 5G communication module through BMS, use 5G/4G signal to upload cell data in real time, through a variety of SOH evaluation models and algorithms equipped with cloud platform, multi-thread online calculation to get the prediction results, timely feedback to the user and BMS, to achieve battery health management. The design case of this system shows good future application value and provides a new direction for EV battery management design.