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

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。

Driving Cycle Recognition Algorithm Based on Multi-source Information Fusion and Application in Vehicle Torque Distribution

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.