Optimal Study On Parameter Matching And Control Strategy For Extended-range Electric Vehicle

As a new technical route of electric vehicle, parameter matching of powertrain components is the primary key technology in extended-range electric vehicle’s R&D process, and energy management strategy is regarded as vehicle’s soul and core technology.This paper aims to reduce manufacturing costs, fuel consumption and decrease the working time of range-extender, optimal study was done using multi-objective method according to the two aspects of parameter matching and energy management strategy, the research contains:Firstly, essential difference with similar vehicles were analyzed from the perspective of powertrain structures, driven and working mode; Four working modes were subdivided as pure electric driving, parallel driving, driving power and braking energy recovery; Full text content were summaried and research significance and innovation were presented.Secondly, the selection of key powertrain components were completed by using lithium-ion batteries as power batteries, PM synchronous motor as drive motor, a small four-cylinder and four-stroke gasoline engine with ISG PM synchronous generator were composed as range-extender system; The dynamic design requirements were proposed according to a SAIC prototype vehicle, then parameter matching of drive motor, power batteries and range-extender’s engine-generator system were finished, which provided parameter basis for vehicle performance simulation and parameter optimization.Then, energy management strategy for this project was proposed. Control models were built by Matlab/Simulink, including mode switch control strategy, engine’s multiple operation points control strategy by engine’s three working points identified, generator control strategy was designed by vector control strategy, and simulation was done to verificate generator’s quick response to speed mutation target, battery control strategy was designed based on Thevenin circuit model.And then, use AVL-Cruise software to build vehicle model, dynamic parameter and control strategy were validated to be accurate in the NEDC driving cycle. Simulationresults indicated that the vehicle can meet the dynamic requirements for maximum speed,maximum gradability and acceleration, driving range was improved greatly in range-extended mode, and low emissions and good fuel economy performance were revealed in driving process.Finally, a linear weighted multi-objective genetic algorithm was proposed based on the targets of manufacturing costs, the equivalent fuel consumption in two modes and acceleration time. Results showed that the optimized programme reduced manufacturing cost with the consideration in vehicle’s dynamic and economic performance. Under the constraint of target range and battery SOC, optimal calculation of SOC threshold value was done in the short and long distance driving mode respectively. The range-extender’s start-stop times and running time were decreased after optimization, the distance by electric driving was increased in the target range, and the fuel consumption and emission were reduced.