Research On Key Technologies Of Economic Driving Assistance System

Reducing energy consumption is an eternal theme of the automotive industry.Fuel consumption is not only related to the performance of the vehicle itself,but to a large extent depends on the driver’s behavior.Eco-driving has been demonstrated to have great potential to reduce fuel consumption of existing vehicles by changing driving behavior,which has become an important research direction in the field of automobile energy-saving.With its obvious benefits,eco-driving assistance system has already been focused on and applied in some vehicle products.However,the current economic driving assistance system mainly uses the on-board signal and using a fixed economic judgment criteria.On the one hand,with the wide application of ADAS(Advanced Driver Assistance System),such as ACC(Adaptive Cruise Control),etc.,the future traffic condition can be predicted by using signals from these ADAS systems.This makes it possible to further improve the effect of eco-driving systems.On the other hand,the power demands of different drivers are different from each other.And even for a same driver,his/her power demand may vary with traffic conditions.Fixed judgment criteria cannot adapt to different power demands.Furthermore,most of these systems only use on board signals.To solve the above problems,a new economic driving assistance system is presented.Most of the empirical driving strategies are obtained by statistical analysis.The relationship between driving behavior and economic performance is fuzzy,which is hard to design economical driving assistant algorithm.To solve the above problem,an economic model has been first built and validated.Based on this model,the relationship between automobile economy and driving behavior is analyzed in a quantitative manner.On the basis of the quantitative analysis of the factors affecting the automobile economy,driving auxiliary strategy is designed considering acceleration,deceleration and gearshift.Firstly,considering that current economic criteria for the acceleration of behavior is fixed,the economic acceleration threshold considering power demand is proposed.maximum acceleration is linearized by introducing dynamic index,the calculation model of acceleration threshold is obtained by using the dynamic index and vehicle speed as input.Secondly,considering that the driver fails to make full use of the traffic information,braking energy consumption optimization algorithm based on vehicle information has been designed,the algorithm can select the optimal coast time according to the information of the preceding car and avoid the energy loss caused by braking.Finally,gearshift optimization strategy based on speed and power demand have been designed to solve the power shortage caused by upshift,economic performance of upshift is effectively determined and driving force is introduced to evaluate driving force after upshift.The effect of driving auxiliary strategy is simulated and verified under NEDC condition and city cycle.Results show driving assistant strategy based on the proposed method has good fuel saving performance.It provides an effective way to solve the problem of increasing fuel consumption due to improper driving behavior.Furthermore,the effectiveness of the system is verified by test bench under environmental disturbances.