锂离子电池状态估算方法研究与实现

发布时间：2018-11-15 18:57

【摘要】：近年来,面对能源危机、环境污染这些日益严峻问题的威胁,世界各国都在加紧研发电动汽车。电动汽车以其优良的节能环保无污染的特点成为未来汽车产业的发展重点。其中,作为电动汽车的动力源的电池成为制约电动汽车发展的瓶颈。电池管理系统(Battery Management System,BMS)作为全面监控和管理电池的关键,通过充放电均衡检测保障电池正常合理工作,能够有效提高电池循环使用寿命,避免不合理使用和降低不必要的风险。其中,电池及电池组剩余电量(State of Charge,SOC)和健康状态(State of Health,SOH)的在线估算是电池管理系统合理高效运行的关键。研究具有较高精度的SOC及SOH估计算法对于电池管理系统而言是极其重要的,它能够为延长电池寿命、提高电池利用率等提供有效的支持。本文以锂离子电池状态估算算法为主要研究内容,在分析了现有BMS研究水平的基础上,结合锂离子电池本身的特点进行锂离子电池管理算法的研究和实现。论文首先通过对电池模型的研究介绍,选择二阶RC等效电路模型并通过MATLAB建立了锂离子电池非线性模型并进行了参数辨识,结合实验数据验证了所建模型的有效性。然后通过对现在几种常用的电池荷电状态(SOC)估算算法的优缺点进行分析,基于所建立的锂离子电池二阶模型研究了基于扩展卡尔曼滤波的锂离子电池SOC估算算法,采用Simulink对SOC估算算法进行仿真验证。对于锂离子电池健康状态,通过对实验数据的深入分析,提出了一种双脉冲SOH的快速检测算法。最后,根据对锂离子电池SOH的估算采用粒子滤波(Particle Filter)算法对锂离子电池剩余使用寿命进行了预测在MATLAB中进行代码实现和仿真,与实验数据进行对比验证,达到了较好的预测精度。本文通过对锂离子电池剩余电量及健康状态估算方法的研究,以及对电池寿命的预测算法的实现,结合试验数据进行对比,达到了较好的估算精度,为锂离子动力电池在电动汽车及储能系统中的应用奠定了技术基础。
[Abstract]:In recent years, facing the threat of energy crisis and environmental pollution, every country in the world is speeding up the research and development of electric vehicles. Electric vehicle (EV) has become the focus of automotive industry in the future because of its excellent characteristics of energy saving, environmental protection and no pollution. Among them, as the power source of electric vehicles, battery becomes the bottleneck of the development of electric vehicles. Battery management system (Battery Management System,BMS) is the key to the overall monitoring and management of the battery. The battery cycle life can be effectively improved by the charge / discharge balance detection to ensure the normal and reasonable operation of the battery. Avoid unreasonable use and reduce unnecessary risks. The online estimation of (State of Charge,SOC and (State of Health,SOH is the key to the reasonable and efficient operation of the battery management system. It is very important to study SOC and SOH estimation algorithms with high accuracy for battery management system, which can provide effective support for prolonging battery life and improving battery utilization rate. In this paper, the state estimation algorithm of lithium ion battery is taken as the main research content. Based on the analysis of the existing BMS research level, the research and implementation of the lithium ion battery management algorithm are carried out according to the characteristics of the lithium ion battery itself. The second order RC equivalent circuit model is selected and the nonlinear model of lithium-ion battery is established by MATLAB. The validity of the model is verified by the experimental data. Then the advantages and disadvantages of several commonly used (SOC) estimation algorithms are analyzed. Based on the established second-order model of lithium-ion battery, the SOC estimation algorithm based on extended Kalman filter is studied. The SOC estimation algorithm is simulated by Simulink. For the healthy state of lithium ion battery, a fast detection algorithm of double pulse SOH is proposed by analyzing the experimental data. Finally, according to the estimation of lithium ion battery SOH, the residual service life of lithium ion battery is predicted by particle filter (Particle Filter) algorithm. The code realization and simulation are carried out in MATLAB, and the results are compared with the experimental data. Good prediction accuracy has been achieved. In this paper, the method of estimating the residual charge and health state of Li-ion battery is studied, and the prediction algorithm of battery life is realized. In combination with the experimental data, the estimation accuracy is achieved. It lays a technical foundation for the application of lithium ion power battery in electric vehicle and energy storage system.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM912

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