【作者】 赵强；

【导师】 周美兰；

【作者基本信息】 哈尔滨理工大学 ， 电力电子与电力传动， 2016， 硕士

【摘要】 面临着日益突出的燃油供求矛盾和环境污染问题,世界各主要汽车产业强国纷纷加快了新能源汽车的研发步伐,电动汽车已经成为了汽车产业的发展方向。其中,电池是电动汽车必不可少的动力装置,而磷酸铁锂电池以性能上的优越性成为了动力源的首选。作为电动汽车的动力装置,对磷酸铁锂电池组的安全性、成本和续航里程等均有较高的要求。电池管理系统正是致力于研究提升动力电池组使用的安全性、可靠性和效率的关键技术之一。本文以由8个电池单体构成的50Ah磷酸铁锂电池组为研究对象,分析其机理,对其性能进行了测试。设计了一套以DSP为核心的电池管理系统,对电池工作状态进行监测,提高了电池荷电状态(State of Charge,SOC)估算的精度,增强了电池使用的安全性与可靠性。首先,对电池管理系统的研究现状进行了总结,对电池的特性进行了深入研究。通过对比不同SOC估算方法的优缺点,在原有BP(Back Propagation,BP)神经网络的基础上,提出了一种改进的粒子群算法(Particle Swarm Optimization,PSO)加以优化,提高了网络的SOC估算精度。其次,以TMS320F2812为控制芯片,对电池管理系统(Battery Management System,BMS)的硬件和软件电路进行了设计。根据系统要求,分别设计了电池状态信息检测电路、通信电路、均衡控制电路和保护电路等硬件电路。同时,完成了主程序设计、电池信息检测子程序设计、通信子程序设计、SOC估算子程序设计等。最后,对本文所设计的电池管理系统进行了整体调试。搭建了电池管理系统整体测试平台,对BMS的信号采集功能和通信功能进行了验证,通过设计实验,完成了电池管理系统均衡控制和SOC估算功能的测试。实验表明,本文所设计的电池管理系统信号采集精度符合管理系统的设计要求,各部分之间能够有效地进行通信,均衡控制和SOC估算有较好的效果。更多还原

【Abstract】 Facing with the increasingly prominent contradiction between supply and demand of fuel and environmental pollution problems, the world’s leading automobile producers have accelerated the pace of research and development of new energy vehicles. Electric Vehicles（EV） have become the developmental direction of automobile industry. This trend shows that the battery electric vehicle power device is essential, and LiFePO₄ battery with superior performance has become the first choice of power electric cars. As the power device of electric vehicles, high requirements have been put forward for its safety, cost and endurance etc. As one of the key technologies, Battery Management System（BMS） is committed to improve application security, reliability and efficiency of power battery.This dissertation chose the 50 Ah LiFePO₄ battery composing of 8 single cell as the research object, analyzing the mechanism of performance testing. The dissertation designed a set of battery management system with DSP as the core to monitor the battery working state, improved the estimation accuracy for Battery State of Charge（SOC） and enhanced the security and reliability of the battery.Firstly, through consulting a large number of literatures, the research status of BMS was analyzed and summarized, the characteristics and working principle of LiFePO₄ batteries were in-depth studied. In addition, by comparing the advantages and disadvantages of existing SOC estimation methods, an improved PSO-BP neural network for SOC estimation was proposed to improve the accuracy of SOC by optimizing the original BP neural network.Secondly, using TMS320F2812 as the control chip, the hardware and software circuits of battery management system were designed. According to the system requirements, the hardware circuits include the battery working state information detection circuit, communication circuit, a balanced control circuit and protection circuit, etc. Meanwhile, all system software designs were completed, including the main program design, the battery information detection subroutine design, CAN communication subroutine design, SOC estimation subroutine design, etc.Finally, the overall debugging of battery management system was completed in this dissertation. By setting up the test platform for BMS, signal acquisition and communication functions of BMS were verified. Though designing the experiment, the test for balanced control and SOC estimation function of BMS were finished. Experimental results show that the signal acquisition accuracy of BMS satisfy the design requirements in this dissertation. BMS reveals effectively communication between the parts, good performances of balance control and SOC estimation.更多还原