This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 A...This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.展开更多
由于传统无迹卡尔曼滤波估算方法具有局限性,为了能准确估算动力电池荷电状态(state of charge,SOC),提出了一种基于无迹卡尔曼粒子滤波的动力电池SOC估算方法.以三元锂电池为研究对象,建立了电池二阶RC等效电路模型,通过对电池进行充...由于传统无迹卡尔曼滤波估算方法具有局限性,为了能准确估算动力电池荷电状态(state of charge,SOC),提出了一种基于无迹卡尔曼粒子滤波的动力电池SOC估算方法.以三元锂电池为研究对象,建立了电池二阶RC等效电路模型,通过对电池进行充放电试验辨识出模型参数,并验证模型准确性.采集了实际工况下的电池数据,分别用无迹卡尔曼滤波算法、粒子滤波算法和无迹卡尔曼粒子滤波算法估算电池SOC,在MATLAB中进行了仿真试验,并对估算的电池SOC进行比较.结果表明:无迹卡尔曼粒子滤波算法可以快速准确地估算出电池SOC,误差小于2.5%,优于另外2种算法.展开更多
In this paper, a square root cubature particle filter approach was designed to estimate the state of charge of lithium-ion battery,which not only enhanced the numerical stability and guaranteed positive definiteness o...In this paper, a square root cubature particle filter approach was designed to estimate the state of charge of lithium-ion battery,which not only enhanced the numerical stability and guaranteed positive definiteness of the state covariance, but also increased accuracy and decreased computation quantity. Due to the fractional characteristics of the battery capacitance, a fractional order model was used to formulate the lithium-ion battery. Considering the high accuracy and easy convergence, a particle swarm optimization algorithm was utilized to identify the model parameters. The above-mentioned approach was modelled and translated into C code, which was downloaded into battery control unit of battery management system for experimental validation. Two kinds of dynamic cycles were utilized to validate the proposed approach at different temperatures, where both unscent Kalman filter and cubature Kalman filter were compared with the proposed approach. Experimental results indicate that the proposed approach has better accuracy and robustness, and fractional order model is more accurate than integer order model.Therefore, the square root cubature particle filter with fractional order model of lithium-ion battery is a good candidate to estimate the state of charge.展开更多
The lithium-ion batteries have drawn much attention as the major energy storage system.However,the battery state estimation still suffers from inaccuracy under dynamic operational conditions,with the unstable environm...The lithium-ion batteries have drawn much attention as the major energy storage system.However,the battery state estimation still suffers from inaccuracy under dynamic operational conditions,with the unstable environmental noise influencing the robustness of estimation.This paper presents a 5th-order cubature Kalman filter with improvements on adaptivity for real-time state-of-charge estimation.The second-order equivalent circuit model is developed for describing the characteristics of battery,and parameter identification is carried out according to particle swarm optimization.The developed method is validated in stable and dynamic conditions,and simulation results show a satisfactory consistency with the experimental results.The maximum estimation error under static conditions is less than 3%and the maximum error under dynamic conditions is 5%.Numerical analysis indicates that the method has better convergence and robustness than the traditional method under the disturbances of initial error,which demonstrates the potential for EV applications in harsh environments.The proposed method shows application potential for both online estimations and cloud-computing system,indicating its diverse application prospect in electric vehicles.展开更多
荷电状态(state of charge,SOC)估算技术是锂电池管理系统中最重要的功能之一。针对磷酸铁锂电池组展开研究,以准确估计电池组中各单体荷电状态为目的,首先采用一阶戴维南(Thevenin)模型结合安时法建立综合电池模型;采用一种平方根采样...荷电状态(state of charge,SOC)估算技术是锂电池管理系统中最重要的功能之一。针对磷酸铁锂电池组展开研究,以准确估计电池组中各单体荷电状态为目的,首先采用一阶戴维南(Thevenin)模型结合安时法建立综合电池模型;采用一种平方根采样点卡尔曼滤波(square root sigma point Kalman filter,SRSPKF)方法,配合在线递推最小二乘(recursive least square,RLS)算法,同时实现对电池等效模型参数的辨识以及对电池荷电状态的估算。理论上讲,SRSPKF算法使系统状态直接以其方差的平方根形式传播,可显著降低常规Sigma点卡尔曼滤波器(sigma points Kalman filter,SPKF)算法的复杂性。实验结果表明,相对SPKF而言,SRSPKF具有更强的状态估计误差抑制能力,采用SRSPKF可以获得比SPKF更准确的SOC估计结果。展开更多
文摘This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.
文摘由于传统无迹卡尔曼滤波估算方法具有局限性,为了能准确估算动力电池荷电状态(state of charge,SOC),提出了一种基于无迹卡尔曼粒子滤波的动力电池SOC估算方法.以三元锂电池为研究对象,建立了电池二阶RC等效电路模型,通过对电池进行充放电试验辨识出模型参数,并验证模型准确性.采集了实际工况下的电池数据,分别用无迹卡尔曼滤波算法、粒子滤波算法和无迹卡尔曼粒子滤波算法估算电池SOC,在MATLAB中进行了仿真试验,并对估算的电池SOC进行比较.结果表明:无迹卡尔曼粒子滤波算法可以快速准确地估算出电池SOC,误差小于2.5%,优于另外2种算法.
基金supported by the National Key Research and Development Program of China (Grant No. 2017YFB0103104)the Key Research and Development Program of Jiangsu Province (Grant No. BE2021006-2)the Innovation Project of New Energy Vehicle and Intelligent Connected Vehicle of Anhui Province,and the Foundation of State Key Laboratory of Automotive Simulation and Control (Grant No. 20201107)。
文摘In this paper, a square root cubature particle filter approach was designed to estimate the state of charge of lithium-ion battery,which not only enhanced the numerical stability and guaranteed positive definiteness of the state covariance, but also increased accuracy and decreased computation quantity. Due to the fractional characteristics of the battery capacitance, a fractional order model was used to formulate the lithium-ion battery. Considering the high accuracy and easy convergence, a particle swarm optimization algorithm was utilized to identify the model parameters. The above-mentioned approach was modelled and translated into C code, which was downloaded into battery control unit of battery management system for experimental validation. Two kinds of dynamic cycles were utilized to validate the proposed approach at different temperatures, where both unscent Kalman filter and cubature Kalman filter were compared with the proposed approach. Experimental results indicate that the proposed approach has better accuracy and robustness, and fractional order model is more accurate than integer order model.Therefore, the square root cubature particle filter with fractional order model of lithium-ion battery is a good candidate to estimate the state of charge.
基金This work is supported by the National Key Research and Development Program of China(2018YFB0105400).
文摘The lithium-ion batteries have drawn much attention as the major energy storage system.However,the battery state estimation still suffers from inaccuracy under dynamic operational conditions,with the unstable environmental noise influencing the robustness of estimation.This paper presents a 5th-order cubature Kalman filter with improvements on adaptivity for real-time state-of-charge estimation.The second-order equivalent circuit model is developed for describing the characteristics of battery,and parameter identification is carried out according to particle swarm optimization.The developed method is validated in stable and dynamic conditions,and simulation results show a satisfactory consistency with the experimental results.The maximum estimation error under static conditions is less than 3%and the maximum error under dynamic conditions is 5%.Numerical analysis indicates that the method has better convergence and robustness than the traditional method under the disturbances of initial error,which demonstrates the potential for EV applications in harsh environments.The proposed method shows application potential for both online estimations and cloud-computing system,indicating its diverse application prospect in electric vehicles.
文摘荷电状态(state of charge,SOC)估算技术是锂电池管理系统中最重要的功能之一。针对磷酸铁锂电池组展开研究,以准确估计电池组中各单体荷电状态为目的,首先采用一阶戴维南(Thevenin)模型结合安时法建立综合电池模型;采用一种平方根采样点卡尔曼滤波(square root sigma point Kalman filter,SRSPKF)方法,配合在线递推最小二乘(recursive least square,RLS)算法,同时实现对电池等效模型参数的辨识以及对电池荷电状态的估算。理论上讲,SRSPKF算法使系统状态直接以其方差的平方根形式传播,可显著降低常规Sigma点卡尔曼滤波器(sigma points Kalman filter,SPKF)算法的复杂性。实验结果表明,相对SPKF而言,SRSPKF具有更强的状态估计误差抑制能力,采用SRSPKF可以获得比SPKF更准确的SOC估计结果。
