Based On Driving Pattern Recognition Optimal Energy Management Strategy Of Extended-range Electric City Bus

The Extended-Range Electric Vehicle(E-REV) is an important technology development direction of new energy vehicles. For E-REV, which equipped with two power sources, energy distribution strategy has a great influence on consumption economy, and driving cycle is the key factor of developing energy distribution strategy. Thus, this dissertation based on driving pattern recognition of real world driving data, proposes an online optimal energy distribution strategy for an Extended-Range Electric City Bus.K-means cluster analysis is used to classify the collected real world driving data into four clusters. And then four feature cycles are reconstructed, which is necessary for offline optimization and online driving pattern recognition.Based on an Extended-Range Electric City Bus, the mathematical model of energy distribution strategy optimization is built, and the theoretic methods, such as Dynamic Program(DP), Pontryagin's Minimum Principle(PMP) and Equivalent Consumption Minimization Strategy(ECMS) are shown. Then the equivalency between ECMS and PMP is proven. Considering online application performance, ECMS is selected as core optimal algorithm of the proposed energy distribution strategy.For ECMS, the suitable equivalent factor is the basis of optimal online control. Thus, several simulation experiments are implemented to analyze the effect of the control parameters on the optimization results, which are used to adjust equivalent factor. Then the offline optimization of four feature cycles by ECMS are done. And the best control parameters are selected to form the knowledge base for online optimization calculation.Based on real-time driving data, the online optimal energy distribution strategy recognizes current driving cycle as one of the four feature cycles with Euclidean distance of feature variables as measure criterion. Then, the corresponding control parameters of the recognized feature cycle are applied to calculate the optimal power of engine and battery by ECMS to realize the best consumption economy.