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锂电池组荷电状态估计方法和非耗散均衡策略研究
Alternative TitleState of Charge Estimation Method and Non-dissipative Balance Strategy for Lithium Batteries
齐志佳1,2
Department工艺装备与智能机器人研究室
Thesis Advisor袁学庆
Keyword电池管理系统 锂电池特性 锂电池建模 SOC估计算法 非耗散均衡管理技术
Pages81页
Degree Discipline机械电子工程
Degree Name硕士
2019-05-17
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract近年来,世界各国积极推动和引导电动汽车行业的发展,我国尤其重视电动汽车行业,在政策支持和市场推动的双重激励下,我国已经成为全球最大、增长最快的电动汽车市场。作为电动汽车的核心技术之一,电池技术尚不能完全满足电动汽车的动力需求。 电池管理系统(Battery Management System, BMS)由于在发挥电池性能方面具有重要作用,因此成为各个企业和高校持续关注的研究热点,其功能也不断完善。其中荷电状态(State Of Charge,SOC)估计技术作为 BMS 最重要的部分,指示电池的剩余容量信息,是其他一切相关技术的研究基础,此外,改善电池组一致性对电池组使用寿命的延长和整体性能的提升有重要意义。因此,本文针对 BMS 中的 SOC 估计技术和均衡管理技术两大核心内容展开了研究,主要研究内容如下: (1)以磷酸铁锂电池为研究对象,研究对 SOC 估计影响较大的内阻、滞后和开路电压特性,为建立电池模型提供理论支持;(2)研究锂离子电池建模方法,以一阶 Thevenin 模型为基础加入滞后模型,并加入温度参数以便研究温度对锂离子电池建模的影响建立了电池等效电路模型,利用子空间辨识结合最小二乘法,设计静态和动态实验辨识不同温度下的电池模型参数,对改进模型的精度与对温度的鲁棒性进行仿真验证,为 SOC 估计算法提供模型基础;(3)研究 SOC 估计算法, 根据扩展 Kalman 滤波和无迹 Kalman 滤波算法原理为电池模型搭建 SOC 估计算法框架,进行仿真验证并将两种算法的估计精度和鲁棒性进行对比分析,获得较好的 SOC 估计效果;(4) 研究均衡管理方法,提出了由双向反激变换器和通道选择电路组成的非耗散均衡电路,对电路参数进行了设计,并提出了利用占空比改变均衡电流的方法,利用 Simulink 中的 SimPowerSystem 模块建立均衡电路模型,对其仿真验证并给出了占空比与均衡电流的对应关系。选定 SOC 为均衡变量并以 SOC分散度作为均衡评价标准,设计均衡管理策略并提出了利用模糊控制算法调节均衡电流的方法, 搭建均衡模型进行仿真验证并与其他均衡方案进行对比分析。
Other AbstractIn recent years, many countries around the world actively promote and guide the development of the electric vehicle industry. China attaches great importance to the electric vehicle industry. Under the dual incentives of policy support and market promotion, China has become the largest and fastest growing electric vehicle market in the world. As one of the core technologies of electric vehicles, battery technology can not fully meet the power requirements of electric vehicles. As the Battery Management System (BMS) plays an important role in the performance of batteries, it has become a research hotspot that enterprises and universities continue to pay attention to, and its functions are constantly improving. State of Charge(SOC)estimation technology is the most important part of BMS. It indicates the residual capacity information of batteries. It is the basis of all other related technologies. In addition, improving the consistency of batteries is of great significance for prolonging the service life of batteries and improving the overall performance of batteries. Therefore, this paper focuses on SOC estimation technology and balance management technology in BMS. The main research contents are as follows: (1) Taking lithium iron phosphate battery as the research object, the characteristics of internal resistance, hysteresis effect and open-circuit voltage, which have great influence on SOC estimation, are studied to provide theoretical basis and data basis for establishing lithium battery model. (2) The method of lithium battery modeling is studied. By adding the hysteresis model to the Thevenin equivalent circuit model, and adding the temperature parameter to study the effect of temperature on the model, a improved battery equivalent circuit model is established. The parameters of battery model at different temperatures are identified by state subspace identification combined with least square method. The accuracy and robustness of the improved model is verified by simulation, which provides a model basis for SOC estimation algorithm. (3) The SOC estimation algorithm based on Kalman filter is studied. According to the principle of Expended Kalman Filter and Unscended Kalman Filter algorithm, the SOC estimation algorithm framework for battery model is built. The simulation is carried out and the estimation accuracy and robustness of the two algorithms are compared and analyzed to obtain better effect. (4) Study the method of equalization management, put forward the non-dissipative equalization circuit composed of bi-directional flyback converterand channel selection circuit, design the circuit parameters, and put forward the method of changing the equalization current by duty cycle, build the equalization circuit model by Simulink, and give the corresponding relationship between duty cycle and equalization current. Selecting SOC as equilibrium variable and taking SOC dispersion as equilibrium evaluation criterion, the balance management strategy is designed and the method of adjusting balancing current by fuzzy control algorithm is put forward. The balancing model is built and verified by simulation and compared with other balance strategy . The results show that the balance strategy used in this paper achieves better results in balancing speed and balancing effect. To a certain extent, the output capacity of battery pack can be increased, which has certain application value.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/25183
Collection工艺装备与智能机器人研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
齐志佳. 锂电池组荷电状态估计方法和非耗散均衡策略研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2019.
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