摘要
随着锂离子电池在智能电网、新能源汽车等领域的大规模应用,其充放电能力,即峰值功率的准确预测对于保障系统的安全、可靠运行至关重要。从单体和系统两个层面归纳分析锂离子电池功率状态预测方法的研究进展:针对电池单体预测方法,主要包括测试查表法、黑箱法、等效电路及电化学模型法等,重点阐述多参量约束的等效电路模型法,并进行分类与对比分析;针对电池系统,从电池系统模型及功率状态预测算法两个角度出发,分别讨论了串联型、非串联型电池系统的功率状态预测算法和大数据驱动的智能预测方法,并分析各方法的优缺点及应用领域;结合下一代云计算、大数据、数字孪生等发展趋势,对锂离子电池功率状态预测方法进行展望,为促进电池全生命周期管理技术的研发与应用提供一些思路。
With the large-scale application of lithium-ion batteries in smart grid and new energy vehicles,the accurate prediction of their charging and discharging capacity,namely peak power prediction,is very important to maintain the safe and reliable operation of the system.This paper analyzes the state of the art of state of power prediction methods for lithium-ion batteries from the single and system levels:①For cell prediction methods,mainly including look-up table method,black box method,equivalent circuit model and electrochemical model method.The equivalent model method with multi-parameter constraint is emphatically introduced.The classification and comparative analysis of those methods are also carried out.②For battery system,viewing from battery system model and state of power estimation methods,the state of power prediction algorithm of series and non-series battery system and the intelligent prediction method driven by big data are discussed.Moreover,the advantages and disadvantages of these methods and the application field are analyzed.③Combined with the development trends of next-generation cloud computing,big data,digital twin,etc.,the state of power prediction methods of lithium-ionbatteries are forecasted,which provides some ideas for the development and application of battery all life cycle management technology.
作者
彭思敏
徐璐
张伟峰
杨瑞鑫
王前进
蔡旭
PENG Simin;XU Lu;ZHANG Weifeng;YANG Ruixin;WANG Qianjin;CAI Xu(School of Electrical Engineering,Yancheng Institute of Technology,Yancheng 224051;School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081;State Grid Jiangsu Electric Power Co.,Ltd.Xiangshui Power Branch Company,Yancheng 224600;Wind Power Center,Shanghai Jiao Tong University,Shanghai 200240)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2022年第20期361-378,共18页
Journal of Mechanical Engineering
基金
国家自然科学基金(62003293)
中国博士后科学基金(2021M690395)
江苏高校“青蓝工程”(2021-11)
盐城工学院校级科研(xjr2021052)资助项目。
关键词
功率状态
状态预测
电池系统
等效模型
发展趋势
state of power
state prediction
battery system
equivalent model
development trend
