基于多源信息融合的行驶工况识别及其在整车转矩分配中的应用
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摘要
传统的行驶工况判别方法多采用单参数或者双参数进行判别,为提高工况识别精度,针对混合动力汽车双动力源的特点,提出一种多源信息融合的汽车行驶工况识别方法,基于Daubechies小波对多传感器采集到的时间序列进行分解,利用单支小波重构的方法获得每个传感器不同频段下分解信号的数据特征信息,然后基于变属性权重的模糊C-均值聚类方法将不同传感器不同频段的数据特征信息进行一次聚类识别;最后对不同频段下同一工况的隶属度值加权,采用SOM自组织映射网络进行二次聚类融合实现最终的行驶工况识别。将本文所提方法应用于混合动力汽车整车转矩分配中,不同工况下调用不同的转矩分配三层前馈神经网络模型,以提高整车的经济性能。试验结果验证了本文所提方法的有效性。
Single or double parameters are adopted in traditional discriminant methods of running condition to distinguish the working condition. In order to improve the identification accuracy, a multi-source information fusion of the motor running condition recognition method is proposed, which aims at the double power sources characteristic of the hybrid cars. The data characteristic information of the decomposed signal in different frequency bands of each sensor is obtained by using single wavelet reconstruction method, so that feature information of different frequencies for each sensor data are obtained. Then different frequencies data feature information from different sensors are conducted by a clustering recognition based on variable weight fuzzy C-average clustering method. Finally, the membership values under different frequencies, belonging to a particular category, are obtained. The final running condition recognition is conducted by the SOM self-organizing map network secondary clustering fusion. In order to improve the vehicle's economy target,the proposed method is applied to torque distribution control of hybrid electric vehicle, and a three-layer feedforward neural network model with different driving conditions with different torque distribution neural network model was assigned. The experimental results verified the effectiveness of the proposed method.
Single or double parameters are adopted in traditional discriminant methods of running condition to distinguish the working condition. In order to improve the identification accuracy, a multi-source information fusion of the motor running condition recognition method is proposed, which aims at the double power sources characteristic of the hybrid cars. The data characteristic information of the decomposed signal in different frequency bands of each sensor is obtained by using single wavelet reconstruction method, so that feature information of different frequencies for each sensor data are obtained. Then different frequencies data feature information from different sensors are conducted by a clustering recognition based on variable weight fuzzy C-average clustering method. Finally, the membership values under different frequencies, belonging to a particular category, are obtained. The final running condition recognition is conducted by the SOM self-organizing map network secondary clustering fusion. In order to improve the vehicle's economy target,the proposed method is applied to torque distribution control of hybrid electric vehicle, and a three-layer feedforward neural network model with different driving conditions with different torque distribution neural network model was assigned. The experimental results verified the effectiveness of the proposed method.
引文
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[3]MAZUROWSKI M A,HABAS P A,ZURADA J M,et al.Training neural network classifiers for medical decision making:The effects of imbalanced datasets on classification performance[J].Neural Networks,2008,21(23):427-436.
[4]CONSTANTINE S,KYLE M.Life cycle assessment of greenhouse gas emissions from plug-in hybrid vehicles:Implications for policy[J].Environmental Science&Technology,2008,42(9):3170-3176.
[5]姜平,石琴,陈无畏.基于马尔科夫的城市道路行驶工况构建方法[J].农业机械学报,2009,40(11):26-30.JIANG Ping,SHI Qin,CHEN Wuwei.Driving cycle construction methodology of city road based on markov process[J].Transactions of the Chinese Society for Agricultural Machinery,2009,40(11):26-30.
[6]肖仁鑫,李涛,秦颖,等.并联式混合动力汽车能量管理的马尔可夫决策[J].西南交通大学学报,2012,47(6):982-988.XIAO Renxin,LI Tao,QIN Ying,et al.Markov decision process for the energy management of parallel hybrid vehicles[J].Journal of Southwest Jiaotong University,2012,47(6):982-988.
[7]李丹,顾宏,张立勇.基于属性权重区间监督的模糊C均值聚类算法[J].控制与决策,2010,25(3):457-465.LI Dan,GU Hong,ZHANG Liyong,Fuzzy C-means algorithm with interval-supervised attribute weights[J].Control and Decision,2010,25(3):457-465.
[8]郭磊,陈进,朱义,等.基于小波函数的核主元特征约简方法研究[J].振动工程学报,2009,22(3):287-291.GUO Lei,CHEN Jin,ZHU Yi,et al.Feature reduction method based on wavelet kernel PCA[J].Journal of Vibration Engineering,2009,22(3):287-291.
[9]徐超,张培林,任国全,等.基于改进半监督模糊C-均值聚类的发动机磨损故障诊断[J].机械工程学报,2011,47(17):55-60.XU Chao,ZHANG Peilin,REN Guoquan,et al.Engine wear fault diagnosis based on improvedsemi-supervised fuzzy C-means clustering[J].Journal of Mechanical Engineering,2011,47(17):55-60.
[10]杨伟斌,陈全世,田光宇,等.插电式混合动力汽车换挡规律及转矩分配策略[J].机械工程学报,2013,49(14):91-98.YANG Weibin,CHEN Quanshi,TIAN Guangyu,et al.Shifting schedule and torque distribution strategy for the plug-in hybrid electric vehicle[J].Journal of Mechanical Engineering,2013,49(14):91-98.
[11]余卓平,杨鹏飞,熊璐.控制分配理论在车辆动力学控制中的应用[J].机械工程学报,2014,50(18):99-107.YU Zhuoping,YANG Pengfei,XIONG Lu.Application of control allocation in distributed drive electric vehicle[J].Journal of Mechanical Engineering,2014,50(18):99-107.
[12]LIAN Yufeng,TIAN Yantao,HU Leilei,et al.A new braking force distribution strategy for electric vehicle based on regenerative braking strength continuity[J].J.Cent.South Univ.,2013,20:3481-3489.