Research On Intelligent Energy Management Strategy For Range-extended Electric Vehicle Based On Travel Characteristics

Range-extended electric vehicle(R-EEV),equipped with auxiliary power units(APU),not only retains the characteristics of simple structure and strong power of battery electric vehicle,but also makes up for the shortcomings of low energy density and insufficient driving range of battery electric vehicle to a great extent,therefore it is an ideal vehicle to deal with energy and environmental problems at this stage.Mastering key technologies such as R-EEV powertrain matching optimization and energy management strategy development and upgrading could realize energy conservation,emission reduction and quality improvement of vehicle.At present,the existing researches still have deficiencies on how to apply the travel characteristics to the real-time optimized energy management control more effectively and the impact of the APU system working mode on the energy consumption,emission and battery life.Therefore,combined with the configuration characteristics of R-EEV,it is of great significance to deeply study the real-time optimized energy management strategy based on travel characteristics(travel mileage,driving style and road condition)and develop a working condition identification algorithm with high recognition accuracy and low complexity for realizing the real-time optimized energy management control.Relying on the National Key Research and Development Program of China"Dynamic modeling optimization and dynamic control method of plug-in/extended range hybrid power system(2016YFB0101402-01)",the research designed an intelligent mode switching energy management strategy based on travel characteristic,established a multi-objective optimization model and framework for system energy consumption,emission and battery capacity loss rate,completed powertrain simulation modeling,parameter matching and optimization,designed and optimized the working mode of APU system,and developed intelligent energy management control strategy.The specific contents are as follows:(1)The matching and optimization of power system parameters based on users'demands is realized by establishing the vehicle house of quality model.The weight of performance index is determined by analytic hierarchy process,the entropy method and grey relational analysis.Sensitivity analysis,range and variance analysis of technical parameters are carried through orthogonal test results,and then the coupling law between performance indexes and technical parameters is explored.The particle swarm optimization is used to optimize the five parameters:APU system power,driving motor power,power battery capacity,number of battery cells and total reduction ratio of the transmission system.A co-simulation model of the power and control system of the R-EEV vehicle is built in AVL/Cruise and Matlab/Simulink software for the subsequent design of energy management strategy.(2)The optimization design of APU system working mode/area is carried out to explore the impact of different working modes of APU system on system energy consumption,emission and battery capacity attenuation rate from the multi-objective perspective.Based on the DP algorithm,the optimal working curve of the energy consumption-emission characteristics of APU system is obtained.Considering the oil-power conversion efficiency,the comprehensive emission index and the battery capacity loss rate,the comprehensive performance evaluation index I_comovpis defined.Under the research framework of multi-objective optimization problem,based on BB-MOPSO algorithm and weight coefficient matrix,taking the number of operating points intervals of APU system(N_csop)and the power range at this speed(?_i),the type and value of switching threshold parameters between APU operating points as the optimized objects,the multi-objective optimization analyses are carried out,and the following conclusions are obtained:1)The four working modes N_csop=1+line?N_csop=2?N_csop=3 and N_csop=4 have good performance in energy consumption,emission and battery health,and I_comovp is better.When N_csop?5,the performance of energy consumption,emission,battery health becomes worse,and I_comovpdecreases significantly.2)Compared with the speed-based switching strategy,the power-based switching strategy can reduce the charging and discharging current,reduce charge/discharge frequency and improve the battery service life on the premise of maintaining the same energy consumption and emission level.The above conclusions lay a foundation for the realization of the optimal energy allocation of intelligent mode switching energy management strategy.(3)According to the R-EEV configuration,the concept of optimal power deviation degree of APU system is proposed.This parameter is the core parameter of the approximate optimal energy management strategy with battery energy dropping slowly.The prediction and division method of working condition types based on prediction and parameter identification reduces the algorithm complexity,and solves the difficult problem of real-time travel characteristics recognition and quantitative information.On this basis,an approximate optimal energy management strategy with battery energy dropping slowly that can be optimized in real time is designed.Without predicting the accurate vehicle speed sequence information,on the premise that the battery So C drops along the target track,the APU system works more in the optimal working point/area to realize the optimal power distribution of the APU system and the battery.(4)An intelligent mode switching energy management strategy based on travel characteristics is designed,a parameter identification algorithm based on variable scale windows is used to obtain speed sequence information of working conditions in real time,and a fuzzy algorithm is used to identify driving style and road conditions.The mode switching refers to the switching of the cooperative working state of APU and battery system with reference to the driving range and battery So C state,that is,the switching between CD-EV+CS-Blend mode and CD-Blend mode;and the switching between multi-point mode and point-line mode of APU system working mode/area with reference to the vehicle's speed sequence information under real-time working conditions to switch the APU system working mode/area between multi-point mode and point-line mode.The results show that the intelligent mode switching strategy based on travel characteristics can complete the mode switching function according to the established rules,realize"electricity first,oil later"for short distance and"oil-electricity coordination"for long distance,and real-time optimized APU system working mode switching control,and improve the comprehensive control performance of the system,including energy consumption,emission and battery health.(5)The power system bench is built,and the bench test is carried out to verify the feasibility and effectiveness of each function in the energy management strategy designed by the research.The strategy functions include:driving style and road condition feature recognition algorithm function,APU system start-stop function,APU system steady-state power generation function,APU system dynamic mode switching function and intelligent mode switching function.The test results show that the performance of the strategy is basically the same under the simulation and test conditions,and the control effect is good.