锂离子电池健康状态评估方法研究进展

Research progress of health assessment methods for lithium ion batteries

电化学储能是现代电力系统中不可缺少的一环,其特点是能量密度大、响应速度快、转换效率高、建设周期短、站址选择多等。储能电站的应用场景非常宽泛,如在电源侧平滑出力波动及处理跟踪、电源调频辅助服务、备用电源等;电网侧用来参与电网调峰调频、优化电网潮流分布、改善电能质量、虚拟电厂、延缓输电设备拥堵升级、微网等;用户侧可以完成削峰填谷、智能交通、社区储能、需量电费管理等。储能电站在快速发展的同时,安全是第一要素。而储能系统安全的关键在于电池系统的安全,在于如何精确预估电池的健康状态。储能电站电池的健康状态评估对电站的日常维护成本、健康运行及运维工作量等起关键性作用。综述了电池健康状态SOH(state of health)的影响因素,分析了线性误差预测方法、粒子群结合BP神经网络法、动态贝叶斯网络法的研究过程及结论验证,探讨了不同评估方法的可行性。在完善电池健康状态评估算法理论体系、实际应用技术研究方面具有潜在价值。

Electrochemical energy storage is an indispensable part of modern power system,characterized by high energy density,rapid response speed,high conversion efficiency,short construction cycle,and multiple site selection.The application scenario of energy storage power station is very broad,such as smooth output fluctuation and processing tracking on the power side,power FM auxiliary service,backup power supply,etc.,the power grid side is used to participate in the peak FM of the power grid,optimize the trend distribution of the power grid,improve the power quality,virtual power plant,delay the upgrading of power transmission equipment congestion,micro-network,etc.,and the user side can complete peak filling,intelligent transportation,community energy storage,electricity demand management,etc.With the rapid development of energy storage station,safety is the first element.The key to the safety of energy storage system lies in the safety of the battery system,the core of which lies in how to accurately predict the health of the battery.The health status assessment of the energy storage station battery plays a key role in the daily maintenance cost,healthy operation and operation workload of the power station.The factors of battery health state SOH(state of health)was summarized,the linear error prediction method particle group combination BP neural network method,dynamic Bayesian network method,and the conclusion verification,were analyzed,and the feasibility of evaluation method was discussed.It has potential value in perfecting the theoretical system of battery health assessment algorithm and applying technology research.