考虑参数慢时变及饱和特性的动力电池状态估计方法研究

As the most core component of the electric vehicles (EVs), the power battery is the key technological bottleneck that restricts the development of EVs. The efficient management, optimal control technique are very crucial for reducing the cost of Evs and improving the power battery safety. The power battery is a class of complex nonlinear, slow time-varying parameter saturation system. The precision modeling and the internal state estimation of battery are challenging, which are difficult to be solved by using the traditional methods. Therefore, it is very urgent to establish new theory and methods to resolve them. Many key problems will be studied here, for example, the time-varying system of the power battery will be constructed considering the sensitive parameters variation characteristics, and the state of health (SOH) estimation and the state of available power (SOAP) estimation will be investigated based on the nonlinear observer design theory and the parameter adaptive technology. Starting with the new perspective of the saturation characteristics for the power battery system, the state of charge (SOC) estimation will be studied based on the robust H∞ filtering technology. The effectiveness of the new theory and methods will be illustrated on experimental platform. This subject belongs to the control theory, electrical engineering, electrochemical and other interdisciplinary frontier, not only has great significance to develop China's Evs technology and to advance its industrialization, also plays a significant role in promoting the theory research and apply of the related disciplines.

动力电池作为电动汽车最为核心的部件，是制约电动汽车规模发展的关键技术瓶颈，其高效管理、优化控制技术对于降低电动汽车的使用成本、提高动力电池使用安全性至关重要。动力电池是一类复杂的非线性、参数时变饱和系统，其高精度建模与内部状态估计极具挑战性，传统方法难以解决，亟待寻求新理论新方法予以突破。本项目拟考虑敏感参数的变化特性建立动力电池时变系统模型；基于非线性观测器设计理论及参数自适应技术分别研究动力电池系统的SOH估计、SOAP估计；从动力电池系统饱和特性新视角入手，基于鲁棒H无穷滤波技术研究电池SOC估计；并搭建试验平台验证新理论新方法的有效性。本课题属于控制理论、电气工程、电化学等多学科交叉的前沿，不仅对发展我国电动汽车技术推进其产业化具有重要意义，而且对于相关学科的理论研究和应用有显著的促进作用。

动力电池作为电动汽车最为核心的部件，是制约电动汽车规模发展的关键技术瓶颈，其高效管理、优化控制技术对于降低电动汽车的使用成本、提高动力电池使用安全性至关重要。动力电池是一类复杂的非线性、参数时变系统，其高精度建模与内部状态估计极具挑战性。本项目在充分考虑电池敏感特性的前提下建立了参数时变非线性系统模型；基于非线性观测器设计理论及参数自适应技术估计内部状态。首先开展了各种温度各种倍率下的试验，探寻了电池参数随温度、电流倍率变化而变化的规律；研究了开路电压与荷电状态对应关系在不同条件下的稳定性。基于试验分析结果建立了考虑参数时变特性的非线性系统模型。基于非线性系统控制理论，设计了估计电池健康状态的非线性观测器；基于滑模观测器和模糊神经网络优化的扩展卡尔曼滤波理论设计了电池荷电状态估计器。设计了电池串的网状结构开关电容均衡器。并且在项目的进展中，一直加强关于H无穷平滑估计及非线性系统高增益观测器设计方面的基础理论研究。本课题属于控制理论、电气工程、电化学等多学科交叉的前沿，不仅对发展我国电动汽车技术推进其产业化具有重要意义，而且对于相关学科的理论研究和应用有显著的促进作用。