| The combination of intelligence and electrification is the frontier of the development of vehicle technology.In the process of intelligent upgrading of electric vehicles,advanced intelligent driving systems are continuously integrated into electric vehicles,which makes electric vehicles more and more automatic.However,some problems including redundant configuration,high cost,insufficient utilization of key components et al still exist in the aspects of system architecture,key sensor configuration and energy-saving control of intelligent electric vehicle.In order to handle these problems,the intelligent driving system architecture,optimal sensor selection of and energy saving control for electric vehicles are researched in this thesis.To optimize and integrate the intelligent electric vehicle,the structure sharing scheme of intelligent driving system is proposed.Focusing on the multi-functional integrated cooperative optimization of sensing,control and execution,a novel structure sharing architecture for intelligent driving system of electric vehicles is designed,which includes sensor information sharing,controller resource sharing and actuator operation co-control.The characteristics and advantages of the proposed architecture is analyzed.The critical technologies related to the structure sharing of intelligent driving system are studied,and the key contents are proposed with regards to sharing of the multi-sensor system,sharing of the vehicle control,sharing of the execution system and designing of the system function safety after structure sharing.Based on the structure sharing architecture,an optimal selection and deployment method of environmental sensors for intelligent driving system is proposed.Considering the detection performance and installation position of sensors,the multi-sensor system model is established.The evaluation index system of sensor configuration is constructed in terms of function,economy and reliability,which helps transform the problem of optimal selection and deployment of sensors into a multi-objective combinatorial optimization problem.By using multi-objective algorithm,the frontier solution of sensor configuration considering both performance and cost is solved,and the comprehensive optimization of sensor selection and layout is realized.A comparison of various algorithms has been carried out to illustrate the feasibility of the proposed method.The simulation results demonstrate that the optimized sensor configuration scheme by the proposed approach can meet the requirements of detection range,detection accuracy and reliability,reducing the system cost simultaneously.In order to make full use of the information resources of ego-vehicles and further reduce the energy consumption of electric vehicles,an intelligent energy-saving control method for electric vehicles based on sensor sharing is proposed.Based on the detected motion of proceeding vehicle,the driving safety situation is evaluated and classified in real time,then the decision making and switching control of energy-saving modes is carried out accordingly.The Model Predictive Control method is used to optimize the motor torque in each mode by off-line optimization,and the output torque is dynamically optimized in real time,which can reduce the energy consumption in the context of driving safety.The simulation results illustrate that the proposed method reduces the energy consumption 9.6% on average under the urban driving cycle condition in China.In order to verify the actual control performance of the proposed energy-saving control method,experiments on the electric buses were carried out under the actual traffic conditions of typical cities in China.The experimental results show that the proposed control method can effectively reduce the energy consumption of the vehicle as well as ensuring the driving safety.The average energy consumption under two kinds of road conditions is reduced by 7% and 5.9% respectively. |
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