A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)ba...A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)balancing strategy with a reduced switching-frequency(RSF)is proposed in this paper.The proposed RSF algorithm not only reduces the switching losses,but also features good balancing performance both in the unbalanced and balanced initial states.The results are verified by extensive simulations in MATLAB/Simulink surroundings.展开更多
A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was appli...A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.展开更多
This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The m...This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.展开更多
In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, ...In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.展开更多
Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC es...Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.展开更多
考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM...考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM)。基于此模型,提出串联电池组SOC、容量多尺度联合估计算法。该算法由2个部分组成,一是基于AR-ECM的MDM及差异化模型参数辨识策略:条件辨识策略和定频分组辨识策略;二是基于多时间尺度H无穷滤波(multi-timescale H infinity filter,Mts-HIF)的电池组SOC、容量联合估计算法。通过将所提出MDM中的自回归平均模型(autoregression mean model,AR-MM)与传统MDM中的n阶RC平均模型(nRC mean model,nRC-MM)比较,结果表明所提出的AR-MM在复杂运行工况下具有更优的动态跟随性能。依据最小化信息量准则(akaike information criterion,AIC),AR-MM具有更优的复杂度与精度的权衡。通过与基于多时间尺度扩展卡尔曼滤波(multi-timescale extended Kalman filter,Mts-EKF)联合状态估计算法比较,结果表明所提出的Mts-HIF状态估计算法具有更优的鲁棒性、精度和收敛速度。展开更多
针对磷酸铁锂电池(LiFePO_(4))平坦的开路电压OCV(open circuit voltage)与荷电状态SOC(state of charge)滞回特性在充、放电切换工况下传统等效电路模型估计OCV存在精度较低的问题,提出电池迟滞建模。为了突出LiFePO_(4)电池考虑滞回...针对磷酸铁锂电池(LiFePO_(4))平坦的开路电压OCV(open circuit voltage)与荷电状态SOC(state of charge)滞回特性在充、放电切换工况下传统等效电路模型估计OCV存在精度较低的问题,提出电池迟滞建模。为了突出LiFePO_(4)电池考虑滞回特性的必要性,对3种电池模型的复杂性、准确性和适用性进行综合评价和对比分析。结果表明,一阶RC模型不考虑滞回的影响,仅适用纯充电或纯放电的工况;一阶RC滞回模型在一阶RC模型的基础上增加1个滞回量,虽考虑了滞回特性的影响,但滞回量受参数辨识影响较大,OCV估计存在波动;Preisach模型对存在充、放电切换工况的估算精度较好,但训练数据时间成本较高。NEDC(new European driving cycle)充、放电工况下对不同模型结合算法估计SOC,估计误差均在5%以内,其中Preisach误差在3%以内。展开更多
电化学储能电站在应用于调频、调压等功率波动性工况时,存在能量效率较低、荷电状态(state of charge,SOC)不均衡等问题。该文提出考虑能量效率和SOC均衡的电池储能电站双层功率分配策略,其主要包括单元优化层和子系统优化层:单元优化...电化学储能电站在应用于调频、调压等功率波动性工况时,存在能量效率较低、荷电状态(state of charge,SOC)不均衡等问题。该文提出考虑能量效率和SOC均衡的电池储能电站双层功率分配策略,其主要包括单元优化层和子系统优化层:单元优化层通过充电/放电优先级分区计算实际运行单元数量及其编号,建立以储能单元能耗最小为目标的优化模型,并采用遗传算法求解最优解集;子系统优化层引入基于电化学阻抗的电池能耗模型,以储能子系统能耗最低和SOC均衡为目标建立多目标优化模型,并采用非支配快速排序遗传算法(non-dominated sorting genetic algorithms-II,NSGA-II)进行求解。通过某地区锂电池储能电站实际参数验证所提策略的有效性,结果表明,与SOC比例分配策略和单层功率分配策略相比,所提功率分配策略在降低电站能耗的同时能最大程度实现SOC均衡,保障电站双向调节能力,提高储能电站经济性。展开更多
文摘A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)balancing strategy with a reduced switching-frequency(RSF)is proposed in this paper.The proposed RSF algorithm not only reduces the switching losses,but also features good balancing performance both in the unbalanced and balanced initial states.The results are verified by extensive simulations in MATLAB/Simulink surroundings.
基金Sponsored by the National High Technology Research and Development Program of China("863"Program)(2003AA501800)
文摘A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.
基金Open Fund Project of State Key Laboratory of Large Electric Transmission Systems and Equipment Technology(No.SKLLDJ042017005)。
文摘This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61004048 and 61201010)
文摘In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.
文摘Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.
文摘考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM)。基于此模型,提出串联电池组SOC、容量多尺度联合估计算法。该算法由2个部分组成,一是基于AR-ECM的MDM及差异化模型参数辨识策略:条件辨识策略和定频分组辨识策略;二是基于多时间尺度H无穷滤波(multi-timescale H infinity filter,Mts-HIF)的电池组SOC、容量联合估计算法。通过将所提出MDM中的自回归平均模型(autoregression mean model,AR-MM)与传统MDM中的n阶RC平均模型(nRC mean model,nRC-MM)比较,结果表明所提出的AR-MM在复杂运行工况下具有更优的动态跟随性能。依据最小化信息量准则(akaike information criterion,AIC),AR-MM具有更优的复杂度与精度的权衡。通过与基于多时间尺度扩展卡尔曼滤波(multi-timescale extended Kalman filter,Mts-EKF)联合状态估计算法比较,结果表明所提出的Mts-HIF状态估计算法具有更优的鲁棒性、精度和收敛速度。
文摘针对磷酸铁锂电池(LiFePO_(4))平坦的开路电压OCV(open circuit voltage)与荷电状态SOC(state of charge)滞回特性在充、放电切换工况下传统等效电路模型估计OCV存在精度较低的问题,提出电池迟滞建模。为了突出LiFePO_(4)电池考虑滞回特性的必要性,对3种电池模型的复杂性、准确性和适用性进行综合评价和对比分析。结果表明,一阶RC模型不考虑滞回的影响,仅适用纯充电或纯放电的工况;一阶RC滞回模型在一阶RC模型的基础上增加1个滞回量,虽考虑了滞回特性的影响,但滞回量受参数辨识影响较大,OCV估计存在波动;Preisach模型对存在充、放电切换工况的估算精度较好,但训练数据时间成本较高。NEDC(new European driving cycle)充、放电工况下对不同模型结合算法估计SOC,估计误差均在5%以内,其中Preisach误差在3%以内。
