Research On Hybrid Electric Vehicle Self-adaptive Control Strategy

With the development of global economy, environmental pollution and energy crisis have become increasingly serious. As one of the most common means of transport, automobile has become an important source of energy demand and atmospheric pollution. Under this background, an important task of automobile research and development is to achieve sustainable development of automobile industry, changing the status of automobile industry radically, effectively alleviating or completely removing the dependence on oil resources. Thus, the new energy vehicle with reliable power, efficient operation, low carbon emissions and other valuable characteristics has become the research focus. Among them, the hybrid electric vehicle (HEV) is the most maturely developed and most widely used new energy vehicle.The energy management control strategy of hybrid electric vehicle has become an important means to improve vehicle energy economy and reduce emissions, while actual driving conditions would influence the energy saving effect of control strategy. In order to better adapt to dynamic changes in driving conditions, control strategy should be able to identify driving conditions at real time, and adapt to optimal parameters based on the identification. However, the difference between theoretically built typical driving conditions and real driving conditions leads the hybrid electric vehicle to a poor driving condition adaptability. Therefore, the focus of this study is to design a self-adaptive control strategy based on driving condition identification.Based on independently developed data acquisition monitoring system, a typical driving condition that matches Dalian local road conditions is firstly constructed in this paper. Then, fuzzy recognition algorithm is applied to identify road conditions online, and ant colony optimization algorithm is applied to optimize "engine shutdown torque coefficient" and "pure electric vehicle speed limit" parameters off-line based on static logic threshold control strategy, so optimal control parameters under each condition can be obtained. Finally, simulation results prove the accuracy of the proposed algorithm and effectiveness of control strategy optimization method.