[动力电池]融合车、路、人信息的电动汽车续驶里程估算

Driving Range Estimation for Electric Vehicles through Vehicles，Roads， and Human Information Fusion

Aiming at driving cycle and driving style influenced significantly on remaining range estimation， which resulted in the remaining range estimation difficulty， a estimate method was adopted，which combined driving cycle with driver’s driving style，to estimation the driving range of electric vehicles. The typical driving condition fragments were selected based on large sample and traffic characteristics of Zhengzhou， which through principal component analysis and fuzzy C-means clustering technique.Under MATLAB/Simulink，a model to identify driving patterns was established based on principal component analysis and learning vector quantization by the typical driving condition fragments and driving style fuzzy recognition model， the remaining range estimation were carried out， which adopted joint simulation method，and the results of remaining range estimation were optimized by Kalman filtering. The simulation analysis and hardware in the loop test results show that driving range estimation for electric vehicles through vehicles，roads， and human information fusion method， reduces the remaining range estimation errors， and confirmes the feasibility of the method at the same time.