Research On Control Strategy Of Energy Recovery System Of New Energy Vehicles

At present,the energy crisis and environmental pollution problems are becoming more and more serious,from 2012 to 2021,the global carbon dioxide emissions increased by40%,of which vehicle emissions accounted for 60%,and its exhaust emissions became one of the important sources of environmental pollution.New energy vehicles have the advantages of high efficiency and energy saving and green cleaning,which is an important direction for the development of vehicle technology in the future,but subject to problems such as short mileage and long charging time,the development of new energy vehicles is still subject to many restrictions,energy recovery is the key means to effectively improve the mileage,and it is one of the hot technologies that new energy vehicle companies are most concerned about.Taking new energy electric vehicles as the research object,this paper proposes a composite energy recovery control strategy,considers a variety of related influencing factors,introduces a fuzzy control strategy,in the process of vehicle taxiing,deceleration and braking,through the method of cascade subdivision and identification of the driver’s braking strength,under the premise of ensuring the safe driving of the vehicle,the regenerative braking force of the drive motor is accurately distributed,thereby improving the energy recovery utilization rate of the vehicle.This paper builds a vehicle dynamics model on the Simulink/Cruise software joint simulation platform,simulates and analyzes the control strategy according to the requirements of the national standard,and verifies the energy recovery control strategy proposed in the semi-physical motor bench,the main research content is as follows:(1)The basic principle of energy feedback flow of new energy vehicles is introduced,and the control method of motor torque in regenerative braking system is analyzed in detail.According to the driving form,vehicle parameters and performance indicators of the target vehicle,the calculation and matching of the motor,battery and transmission system parameters are completed,and the key elements affecting energy recovery are analyzed from the perspective of energy conversion.(2)Through the Cruise vehicle simulation software,the vehicle model is built,including sub-models such as motors,batteries,and wheels,and the whole vehicle is interconnected through signal,electrical and mechanical connections.According to the national standard GB/T 18385-2005 "Electric Vehicle Power Performance Test Method" and GB/T 18386-2017 "Electric Vehicle Energy Consumption Rate and Cruising Range Test Method" simulation test of the target vehicle.The braking dynamic change law of the vehicle in the braking process is analyzed,and the technical difficulty,braking safety and energy recovery rate of the four typical energy recovery control strategies of ideal,optimal,coasting and parallel are compared according to the I curve and ECE regulations of the front and rear axle braking force in the braking process.(3)Based on the above four types of energy recovery strategies,an improved braking force distribution control strategy is proposed.The control strategy is based on the driver’s braking strength demand,aiming at the maximum energy recovery,a multi-factor fuzzy control membership function model is designed,which takes SOC,V and Z parameters as the input quantity and the power distribution ratio K of the electric mechanism as the output quantity.In order to ensure the safety of the vehicle,in emergency braking and ABS working conditions,the motor can quickly exit the braking and secondary locking.According to the improved regenerative braking force distribution scheme,the strategy model is established in Simulink and the C language is selected to compile into a DLL file,the Simulink and Cruise interfaces are created,and the strategy model is loaded into Cruise for simulation test,according to the test standards of the Ministry of Industry and Information Technology,NEDC and FTP75 cycle conditions are selected to track and analyze the effectiveness of the control strategy in this paper from four dimensions of motor torque,current,100 km power consumption and effective energy recovery rate.The co-simulation results show that the energy recovery rate of the proposed energy recovery control strategy reaches 14.92% in NEDC working condition and 21.54% in FTP75 working condition.Compared with the traditional parallel energy recovery control strategy of Cruise,the improvement rate reaches respectively 8.12% and 3.66%.In the simulation test of vehicle braking performance,the control strategy model in this paper takes 30km/h,50km/h and 100km/h as the initial speed to conduct the simulation test with low braking intensity Z=0.1,Z=0.35,medium braking intensity Z=0.5 and high braking intensity Z=0.75,respectively.The simulation results show that the target vehicle braking with the same SOC value and the same braking intensity,the higher the initial speed,the longer the braking time and the more energy recovery;braking at the same initial speed and within the same braking time,the greater the braking intensity,the greater the braking force involved in the motor and the more energy recovery,while the shorter the braking and parking process time,the less the motor involved in the braking time and the less energy recovery.It conforms to the design rules of fuzzy control method,the braking distance of the model under high intensity has met the requirements of GB 12676-2014 "Technical Requirements and Test Methods for Braking Systems of Commercial Vehicles and Trailers".(4)Through the physical test of the energy recovery control strategy of this model on the semi-physical bench motor experiment bench,the results showed that: the effective energy recovery rate of the proposed strategy is 15.61% in NEDC and 23.27% in FTP75,in the braking safety performance test,taking the initial speed of 30km/h and 50km/h as examples,the emergency braking distance of the control strategy in this paper is 4.29 m when the initial speed is 30km/h,and 11.89 m when the initial speed is 50km/h,which is consistent with the overall trend compared with the co-simulation results.The feasibility of the proposed control strategy is verified.