Research On Safety And Energy-saving Control Technology For Plug-in HEV Based On Intelligent Driving Assistance System

Facing energy crisis and environmental pollution,developing new energy vehicles,from the perspective of transportation and automotive industry to drive and lead the energy industry revolution,has become the consensus of automotive technology research.PHEV provides more superior economy and emission performance compared with HEV and wider driving range compared with EV,which is currently the most suitable form of new energy vehicles for development,and its control strategy,as the core of ensuring coordination between multiple power sources,directly affects the vehicle’s economy and power performance.However,the control strategy of PHEV is usually based on the standard driving cycles when it is developed,which is somewhat different from the actual road driving process and ignores the impact of the dynamic motion time-variation of the preceding vehicle,so a further optimization is needed in terms of energy saving.In this paper,a safety-improving and energy-saving control strategy based on intelligent driving assistance system is proposed through key control parameters optimization,security situation estimation and motor torque amendment,as is shown the following.A combinatorial optimization algorithm,which combines MIGA and NLPQL,can realize the global optimization dynamically and the optimization efficiency can be effectively improved.The combinatorial optimization algorithm is introduced to optimize the key control parameters of PHEV control strategy by using actual driving cycle,respectively.Through the comparison in actual driving process,the energy consumption of the control strategy developed by actual driving cycle is reduced by3.04%and 3.94%,compared with that developed by NEDC and FUDS,respectively.Through sharing and using the information acquired from physical sensors of intelligent driving assistance system,the desired acceleration,to describe the driver’s driving behaviors,the HWT and TTC^-1,to characterize the inter-vehicle dynamic motion,are computed in real time,and the security situation of the current driving scenario is estimated by fuzzy inference.In order to improve the rationality and accuracy of fuzzy inference,a driving scenario database is built by PreScan software,and fuzzy clustering and MOS are employed to approximate the membership function of each variable.Through comparative analysis,the RMSE of the security situation estimation system is 0.0813,compared with MOS,and its validity is verified.Based on the quantized value of security situation,the driving scenarios are divided into 4 levels.The motor driving torque limit and braking torque increase are in different degrees when the vehicle driving in different levels,which can effectively avoid larger acceleration and obtain more braking energy recycling.Combining the key control parameters optimization and motor torque amendment,a simulation test is conducted,and the results indicate that the energy consumption of the energy-saving control strategy proposed in this paper is improved by 6.69%and 5.70%respectively compared with that developed by NEDC and FUDS,is further reduced by 2.09%compared with that developed by actual driving cycle,and has a certain peak-cutting and valley-filling function on the speed change,which can get a simultaneous and dual improvement of safety and economy.