为了全面展示锂电池剩余电量估算方法的研究进展,本文查阅了 Web of science、知网、国家知识产权局等数据库中2013年以来的相关论文和专利,综述了锂电池剩余电量的主流估算方法.针对常用的直接估算的方法(安时积分法、开路电压法和阻抗表征)、基于等效电路模型的方法、基于电化学模型的方法和基于人工智能神经网络等的锂电池剩余电量估算方法,本文汇总了各方法的估计误差,结果为安时积分法的最大估计误差可达15%;开路电压法最大估计误差为12.4%;电化学阻抗谱法平均估计误差小于3.8%;卡尔曼滤波法的估计误差小于1%;粒子群滤波法的平均误差可小于1%;基于电化学模型的方法平均误差小于2%;基于神经网络的方法平均误差小于2%;多方法混合和多参量联合估计的方法最大误差小于5%,平均误差小于2.5%.结果表明,卡尔曼滤波法相较于直接估算的方法和其他基于模型的方法,精确度更高且更容易实现;基于神经网络的方法无需对电池模型进行分析即可获得较为准确的结果;多种方法混合使用和利用多种参量修正估算值的方法进一步提高了估算精度.本文还针对电动汽车以及植入式医疗电子设备对于剩余电量估算方法的需求,对比分析了各方法的估算精度、优点、难点及适用电池类型,阐明估算方法的具体应用方案,并展望估算方法在这两个领域的发展方向.本文可为相关领域的研究和从业人员提供全面、详实的锂电池剩余电量估算方法的研究现状及发展方向信息.
Overview of state-of-charge estimation methods and application for Lithium-ion batteries
In order to comprehensively show the research progress of the estimation method of the residual power of Lithium-ion batteries,this paper reviewed the relevant papers and patents in the databases of Web of science,cnki,the patent library of the China National Intellectual Property Administration et al since 2013,and summarized the mainstream estimation methods of the residual power of Lithium-ion batteries.This article summarizes the estimation errors of commonly used direct estimation methods(ampere hour integration method,open circuit voltage method,and impedance characterization),methods based on equivalent circuit models,methods based on electrochemical models,and methods based on artificial intelligence neural networks for estimating the remaining battery capacity of Lithium-ion batteries.The results show that the maximum estimation error of ampere hour integration method can reach 15%;the maximum estimation error of the open circuit voltage method is 12.4%;the average estimation error of electrochemical impedance spectroscopy is less than 3.8%;the estimation error of kalman filtering method is less than 1%;the average error of particle swarm filtering method can be less than 1%;the average error of the method based on electrochemical model is less than 2%;the average error of neural network-based methods is less than 2%;the maximum error of the multi method mixing and multi parameter joint estimation method is less than 5%,and the average error is less than 2.5%.The results indicate that the kalman filter method has higher accuracy and is easier to implement compared to direct estimation methods and other model-based methods;the method based on neural networks can obtain more accurate results without analyzing the battery model;the mixed use of multiple methods and the use of multiple parameters to correct the estimated values have further improved the estimation accuracy.This article also compares and analyzes the estimation accuracy,advantages,difficulties,and applicable battery types of various methods for estimating remaining power in electric vehicles and implantable medical electronic devices.It clarifies the specific application plans of estimation methods and looks forward to the development direction of estimation methods in these two fields.This article can provide comprehensive and detailed information on the research status and development direction of Lithium-ion battery remaining capacity estimation methods for researchers and practitioners in related fields.
Lithium-ion batteriesSOCelectric vehiclesimplantable electronic medical devices
NETL
NSTL
万方数据
