| Extended-range Electric Vehicle(EREV) is based on an electric vehicle while equipped with auxiliary power units that can propel the vehicle under low battery conditions. This is a compromise between the range and the overall weight as well as the production cost of electric vehicles under current technology level. In this paper, which combines the international cooperation program of the Department of Technology(2010DFB837650), where a compact extended-range electric vehicle was set as the target, the configuration design, optimization as well as the control strategy of EREV were investigated, and the powertrain frontal simulation platform and the performance testing platform of for EREV were set up where the design methods and the control strategies were verified.First of all, the basic configurations, operation mechanism and the energy flow method were first explained, and the difference between EREV and conventional EV was demonstrated in terms of degree of hybridization interms of power and energy as well as major driving system. In order to improve the usual deficiency of EREV such as excessive overall weight and weak performance, an EREV power distribution configuration solution including separated driving shaft, AWD system and dual clutch coupling was adopted. Compared to other practical configurations, this solution is much better balanced in terms of performance, weight, complexity as well as costs.Then, the EREV powertrain simulation platform was set up based on Matlab/Simulink software combined with LMS AMEsim and Gamma GT-Suite. During the modeling process, the comparison research between different internal combustion engine modeling methods was conducted by establishing experimental model based on steady state data and theoretical model based on Wiebe combustion theory and 1D CFD, as well as the mean value model based on Hendricks method, so that the advantages and disadvantages of different modeling methods was discussed and each of the methods was applied into the appropriate focus of study.Thirdly, research was conducted on the parameter matching and design method of the EREV and a set of corresponding configuration solutions could be concluded. A cyclic testing analysis method based on power probability density was proposed. During the parameter matching process, the power performance of the vehicle was classified into three levels, and the matching design of the key components within the powertrain was conducted level by level. When analyzing certain driving conditions and matching the efficiency of the engine or motor, the main focus was on the energy conversion within a certain operation condition instead of the duration of the certain working condition. Through scientifically dividing the target optimization range under tested working conditions, combined with mathematical expectation as the calculation method, a data processing algorithm for simulation and testing was designed. The results show that, the matching design configuration of the target vehicle was able to meet the expected performance standards and surpassed the original vehicle in terms of power performance, mileage, energy consumption for electricity generation and the efficiency of regenerating braking.Fourthly, in order to make full use of the advantages of the dual motor configuration in terms of efficiency, the frequent switching between single motor two-wheel drive mode and dual motor four wheel drive mode should be avoided. In this paper, a decision making system for driving mode and driving force distribution based on “driver-vehicle-environment” was designed. With the aim of minimizing power loss, the system will optimize the torque distribution between the front and rear motor. With the use of fuzzy judgment method, combined with information such as road resistance, the intention of the driver can be recognized and dynamic decision making for diving mode selection can be achieved. It has a decision quality assessment mechanism that judges and counts non-ideal decision makings on the driving mode and then makes real-time adjustments to the three sets of control parameters within the finite state machine. The results show that, this control strategy can improve the mileage of EREV under electric driven mode, while avoiding non-ideal mode switching.Fifthly, within the engineering capabilities, in order to solve the lack of consideration of realistic road conditions of the instant optimization strategy, a power management strategy was designed corresponding to operation conditions. This strategy can improve the ECMS equivalent fuel consumption model and unify the fuel consumption under both series and parallel connection modes. With the addition of the mode decision maker between series and parallel connection, the road conditions can be identified and the driving mode switching threshold can be adjusted in real time. The results show that, the control strategy introduced above can precisely identify the realistic road conditions and reduce the fuel consumption effectively under different road conditions. In order to reduce wear and energy consumption of the APU under low temperature, the optimized control strategies for APU cold-start and warm-up process was established. During the research process, the influence of engine temperature on efficiency and the relation between the rate of temperature rise and engine working conditions under different temperature was measured through experimental methods. Then, with the overall energy consumption through the warm-up process set as the optimization target, a dynamic programming control model was set up and got the optimization power output sequence. Meanwhile, based on the ECMS instant optimization control model, three sets of fuzzy controller corresponding to compensation control for power, engine speed and start-stop threshold during warming up. Results show that, the two control strategies can reduce fuel consumption and time needed for warming up respectively.Finally, according to the proposed configuration solution, an EREV powertrain performance test platform was set up and the control system was developed through Motohawk rapid prototype platform. Through the benchmark test,the feasibility and effectiveness of major design methods and control strategies were verified. |
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