基于离散事件/混杂系统的锂离子混合电池系统诊断与控制研究

Despite extensive R&D effort, current BMS technology remains inadequate for reliable and effective monitoring, diagnosis, control, and management of battery systems in electric vehicle, renewable energy, and smart grid applications. The main technical obstacle systems from variations of battery characteristics during operation, battery aging, mixing of different battery types, measurement noises, unreliability of traditional voltage monitoring approach, and inability of current estimation methods in achieving individualized and real-time battery cell characterization..Taking an holistic viewpoint, this project introduces innovative methodologies and develops new prototypes for a real-time, adaptive, and intelligent diagnosis and management system for battery hybrid power sources system. Our methodologies combined parameter identification and state estimation of individual cells, stochastic time-series analysis for monitoring and diagnosis, centralized,decentralized and hybrid system active diagnosis of discrete event system, and hierarchical management frameworks using discrete event and hybrid systems. Methodology development, algorithm implementation, prototype system evaluation, and system verification on vehicles and renewable energy generators will be carried out at SJTU. The new system will introduce critically needed capability of real-time tracking and adaptation into the BMS technology, and as such substantially enhance reliability, energy efficiency, battery life cycles, and safety of battery systems in diversified utilities. The project will be carried out collaboratively by an team with diversified and complementary expertise in battery chemistry and material, battery modeling and system identification, and battery diagnosis and monitoring. The team will work closely with major auto companies (GM, Ford, Shanghai Automotive) and battery companies (A123, LG Chem) for product verification.

电池系统的故障诊断与控制已成为当今国际研究热点之一。本研究探索一种基于离散事件/混杂系统的锂离子混合电池系统新的诊断机制与控制方法。包括：基于分散式控制离散事件系统理论，研究锂离子复杂电池系统多故障主动诊断机制，解决大量离散事件信息提取、隔离、辨识与诊断及模型动态更新问题；基于混杂系统理论，研究混杂系统控制诊断方法，统一处理离散动态和连续动态，解决连续量与离散量并存的复杂电池系统在线故障与失效的诊断问题及模型动态更新问题；研究分散式控制和混杂系统控制的分级分布集成诊断方法，解决多模块混合结构复杂电池网络系统故障与失效的集成在线诊断与监督控制及系统诊断体系动态更新问题，确保系统安全、可靠、节能及长寿命运行。通过本研究，能够扩展和完善电池系统诊断与控制的理论体系，为设计可靠、高效的电池系统，实现电池系统智能诊断与控制提供新的技术支撑，对形成具有自主知识产权的电池储能系统具有较高的实用价。

国家自然科学基金项目面上项目：基于离散事件/混杂系统的锂离子电池系统诊断与控制研究，批准号51677119。 .随着新能源电池技术的发展，可靠性与安全性已经成为电池系统的重要研究方向。本研究探索了一种基于离散事件/混杂系统的锂离子混合电池系统新的诊断机制与控制方法。包括：基于分散式控制离散事件系统理论，研究了基于模型的锂离子复杂电池系统多故障主动诊断机制，解决了离散事件信息提取、隔离、辨识与诊断及模型动态更新问题；基于混杂系统理论，研究了混杂系统控制诊断方法，统一处理离散动态和连续动态，解决了连续量与离散量并存的复杂电池系统在线故障与失效诊断问题及模型动态更新问题；研究了分散式控制和混杂系统控制的分级分布集成诊断方法，解决了多模块混合结构复杂电池系统故障与失效的集成在线诊断与监督控制及系统诊断体系动态更新问题，以确保系统安全、可靠、节能及长寿命运行。通过四年的研究，发表了40篇论文，其中期刊SCI收录8篇，期刊EI收录7篇，EI收录会议论文9篇，中文核心13篇。申请发明专利6项，已授权专利3项，实审阶段2项，初审阶段1项。指导培养研究生15名，指导本科生毕业设计8名，指导大学生创新实践活动（PRP）4项。其中毕业研究生9名。参加国际学术交流会议5次。获奖10项。