在电池管理系统中,二阶Thevenin等效电路模型是一种广泛应用的锂离子电池模型,合理的RC环节可以准确地描述电池的动态特性。电池模型的具体参数一般通过递推最小二乘法(recursive least square,RLS)在特定工况下辨识得到。直接采用RLS...在电池管理系统中,二阶Thevenin等效电路模型是一种广泛应用的锂离子电池模型,合理的RC环节可以准确地描述电池的动态特性。电池模型的具体参数一般通过递推最小二乘法(recursive least square,RLS)在特定工况下辨识得到。直接采用RLS算法进行参数辨识通常无法得到2个有效的RC环节参数,使得电池模型精度与适用性存在一定局限性。针对该问题,文中设计一种新型复合参数辨识工况,并基于此工况提出一种融合约束因子的递推最小二乘法用于电池建模。该方案同时参考2种具有代表性的工况数据,可同时获得SOC-OCV曲线及各荷电状态(stateofcharge,SOC)下电池模型的参数。经验证,该方案构造的电池模型在全SOC周期内,对不同电流的工况适应性较强,SOC估算精度较高。展开更多
为克服支持向量机(support vector machine,SVM)在线辨识过程需要较大的内存开销的问题,该文将递推最小二乘法(recursive least square,RLS)与最小二乘支持向量机(least squares support vector machine,LS-SVM)回归相结合,利用RLS在线...为克服支持向量机(support vector machine,SVM)在线辨识过程需要较大的内存开销的问题,该文将递推最小二乘法(recursive least square,RLS)与最小二乘支持向量机(least squares support vector machine,LS-SVM)回归相结合,利用RLS在线调整支持向量机的权向量和偏移量,实现了系统逆动力学模型的在线辨识。在获得逆动力学模型的基础上,设计了一种基于逆动力学递推最小二乘支持向量机的控制算法,利用RLS在线调整控制器参数。过热汽温辨识和控制的仿真结果表明,辨识出的逆动力学模型具有较高的精度,所设计的控制器能获得较好的控制性能和有较强的适应能力。展开更多
The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robus...The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions.The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory.In this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio.The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time progressing.The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification robustness.Once the road type is recognized,the maximum road friction coefficient and optimal slip ratio are determined.The numerical simulation tests are conducted on two typical road friction conditions（single-friction and joint-friction）by using CarSim software.The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim.The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.展开更多
文摘在电池管理系统中,二阶Thevenin等效电路模型是一种广泛应用的锂离子电池模型,合理的RC环节可以准确地描述电池的动态特性。电池模型的具体参数一般通过递推最小二乘法(recursive least square,RLS)在特定工况下辨识得到。直接采用RLS算法进行参数辨识通常无法得到2个有效的RC环节参数,使得电池模型精度与适用性存在一定局限性。针对该问题,文中设计一种新型复合参数辨识工况,并基于此工况提出一种融合约束因子的递推最小二乘法用于电池建模。该方案同时参考2种具有代表性的工况数据,可同时获得SOC-OCV曲线及各荷电状态(stateofcharge,SOC)下电池模型的参数。经验证,该方案构造的电池模型在全SOC周期内,对不同电流的工况适应性较强,SOC估算精度较高。
文摘为克服支持向量机(support vector machine,SVM)在线辨识过程需要较大的内存开销的问题,该文将递推最小二乘法(recursive least square,RLS)与最小二乘支持向量机(least squares support vector machine,LS-SVM)回归相结合,利用RLS在线调整支持向量机的权向量和偏移量,实现了系统逆动力学模型的在线辨识。在获得逆动力学模型的基础上,设计了一种基于逆动力学递推最小二乘支持向量机的控制算法,利用RLS在线调整控制器参数。过热汽温辨识和控制的仿真结果表明,辨识出的逆动力学模型具有较高的精度,所设计的控制器能获得较好的控制性能和有较强的适应能力。
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2006AA110101)
文摘The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions.The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory.In this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio.The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time progressing.The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification robustness.Once the road type is recognized,the maximum road friction coefficient and optimal slip ratio are determined.The numerical simulation tests are conducted on two typical road friction conditions（single-friction and joint-friction）by using CarSim software.The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim.The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.
