基于MC9S12DP512与CAN总线的EV电池管理系统*

作者：胡建红,朱建新 日期：2007-12-27/span> 浏览：4216 查看PDF文档

基于MC9S12DP512与CAN总线的EV电池管理系统*
胡建红,朱建新
(上海交通大学 汽车电子技术研究所，上海 200240)

摘 要：为实现电动汽车动力电池组的实时监测与管理，采用了带MSCAN、且具有强大功能模块的嵌入式微处理器MC9S12DP512，完成了电动汽车用电池管理系统的硬件设计，并改进了CAN通讯接口电路设计，实现了对电压、电流、温度的实时精确采集。提出了基于Ah累积计量法的SOC复合估算策略。试验表明，该系统能实现对电池组荷电状态十分准确的估算。
关键词：MC9S12DP512;控制器局域网总线;电池管理系统;电动汽车；开路电压

Battery management system applied for EV based on MC9S12DP512 and CAN bus
HU Jianhong, ZHU Jianxin
(Institute of Automotive Electronic Technology, Shanghai Jiaotong University, Shanghai 200240, China)

Abstract: Aimed at realizing realtime supervising of traction battery packed on electric vehicle，using the embedded microprocessor MC9S12DP512 with its onchip MSCAN controller and the other powerful functional modules, the hardware was developed for battery management system in electric vehicle. The design was improved for the CAN communication interface circuits. Precise data samplings of voltages, current and temperatures were realized. The complex estimation strategy for SOC was proposed based on Ah accumulation computation.The test results indicate that the system can comparatively precise evaluates the state of the battery pack.
Key words: MC9S12DP512; controller area network (CAN) bus; battery management system (BMS); electric vehicle (EV); open circuit voltage (OCV)

参考文献(Reference)：
［1］林成涛，王军平，陈全世.电动汽车SOC估计方法原理与应用［J］.电池，2004，34(5)：376-378．
［2］SANTHANAGOPALAN S, WHITE R E. Online estimation of the state of charge of a lithium ion cell［J］. Journal of Power Sources，2006, 161(2):1346-1355.
［3］PLETT G L. Highperformance batterypack power estimation using a dynamic cell model［J］. IEEE Trans.on Vehicular Technology，2004,53(5):1856-1593.
［4］JUNG D Y, LEE B H, KIM S W. Development of battery management system for nickel metal hybrid batteries in electric vehicle applications［J］. Journal of Power Sources，2002,109(1):1-10.
［5］MEISSNER E, RICHTER G. Battery monitoring and electrical energy management: precondition for future vehicle electric power systems［J］. Journal of Power Sources，2003, 116(1):79-98.
［6］LEE D T, SHIAH S J, LEE C M, et al. State of charge estimation for electric scooters by using learning mechanisms［J］. IEEE Trans. on Vehicular Technology,2007,56(2):544-556.