The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.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
The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging...The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.展开更多
The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their correspon...The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.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
为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯...为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。展开更多
为解决目前新能源汽车电池荷电状态(State of Charge,SOC)估算方法存在的最大绝对误差偏大问题,文章提出一种基于遗传算法的优化方法。该方法通过构建物理电路模型来等效电池内部结构,并引入充放电分区机制补偿滞后状态,完成电流值修正...为解决目前新能源汽车电池荷电状态(State of Charge,SOC)估算方法存在的最大绝对误差偏大问题,文章提出一种基于遗传算法的优化方法。该方法通过构建物理电路模型来等效电池内部结构,并引入充放电分区机制补偿滞后状态,完成电流值修正。同时,离散化处理模型参数,并利用最小二乘法完成参数辨识。在神经网络(Back Propagation,BP)中,采用遗传算法估算并优化模型,以输出精确的SOC估算结果。实验结果显示,利用该方法估算的最大绝对误差稳定在0.0~0.2,显著提升了估算精度,满足预期的误差要求。展开更多
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
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supported by research on value model and technology application of patent operation of science and technology project(52094020000U)National Natural Science Foundation of China(52177193).
文摘The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.
基金the financial support from the China Scholarship Council(CSC)(No.202207550010)。
文摘The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.
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.
文摘为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。
文摘为解决目前新能源汽车电池荷电状态(State of Charge,SOC)估算方法存在的最大绝对误差偏大问题,文章提出一种基于遗传算法的优化方法。该方法通过构建物理电路模型来等效电池内部结构,并引入充放电分区机制补偿滞后状态,完成电流值修正。同时,离散化处理模型参数,并利用最小二乘法完成参数辨识。在神经网络(Back Propagation,BP)中,采用遗传算法估算并优化模型,以输出精确的SOC估算结果。实验结果显示,利用该方法估算的最大绝对误差稳定在0.0~0.2,显著提升了估算精度,满足预期的误差要求。
