Mathematical Methods Applied to Economy Optimization of an Electric Vehicle with Distributed Power Train System

This research presents mathematical methods to develop a high-efficiency power train system for a microelectric vehicle (MEV). First of all, to get the optimal ratios of a two-speed gearbox, the functional relationship of energy consumption and transmissions is established using the design of experiment (DOE) and min-max fitting distance methods. The convex characteristic of the model and the main and interactive effects of transmissions on energy consumption are revealed and hill-climbing method is adopted to search the optimal ratios. Then, to develop an efficient and real-time drive strategy, an optimization program is proposed including shift schedule, switch law, and power distribution optimization. Particularly, to construct a mathematical predictive distribution model, firstly Latin hypercube design (LHD) method is adopted to generate random and discrete operations of the MEV; secondly the optimal power distribution coefficients under various LHD points are confirmed based on offline genetic algorithm (GA); then Gauss radial basis function (RBF) is utilized to solve the low-precision problem in polynomial model. Finally, simulation verifications of the optimized scheme are carried out. Results show that the proposed mathematical methods for the optimizations of transmissions and drive strategy are able to establish a high-efficiency power train system.