Study On The Energy Management And Operating Optimization For Range Extended Electric Vehicle

Environmental pollution and energy crisis are major challenges for the development of automobile industry in China.There is a consensus among China government,auto companies and research institutions that developing energy-efficient and new-energy vehicle is an efficient way to achieve the adjustment of automobile industry,and “all electric drive”is considered as the development direction.Range extended electric vehicle(REEV)has the advantages of lower cost,unlimited driving range,without complex mechanical transmission device,clean and efficient,and thus it is a good choice for the automobile industry in China.Focuses on the energy management and operating optimization problem of the REEV,the main contributions and innovations of this dissertation are as follows:1.In order to clarify the the aims,difficulty and influence factors of energy management and operating optimization of REEV,the structure features of power train,power flow features of different operating mode,basic working features of battery and critical vehicle performance parameters of the REEV are analyzed thoroughly.2.In order to avoid the continuous decline in battery state of charge(SOC),to reduce the regulation frequency of engine speed and generator torque,and to improve the vehicle fuel economy when REEV is working in range-extended mode,an auxiliary power unit(APU)power flow optimization method is proposed.Firstly,the measured vehicle real-time electric power is revised according to the generator efficiency,battery SOC and power variation rate in turn,and then the target engine speed and generator torque are obtained according to APU fuel-electricity conversion efficiency features and optimal APU fuel-electricity conversion efficiency curve.Finally,bench experiment is performed under three typical driving cycles,the experimental results under NEDC,FTP and HWFET driving cycles show that the proposed approach avoides the continuous decline in battery SOC,and improves the vehicle fuel economy effectively,it also lower the performance demands on engine speed and generator torque control systems.3.Focuses on how to improve the APU efficiency within a specified period of time and meanwhile output specified electrical energy,an APU fuel efficiency optimal control approach based on PRP conjugate gradient method is proposed.First,the discrete-time range extender fuel efficiency optimization model is developed,in which the fuel efficiency for a given energy command is chosen as the cost function,and the torque of the engine and generator are utilized as the optimized variables.Then,the methods of processing the maximum speed and torque of engine and generator are analyzed,and the step-by-step details to implement the proposed PRP conjugate gradient method based fuel efficiency optimal control approach are described.Finally,simulation is carried out and the results show that the optimal solutions obtained by the proposed approach improve the fuel efficiency of the range extender effectively.4.An APU operating point optimization approach based on dynamic combined cost map(DCM)is presented.The influence of coolant temperature,catalyst temperature and air/fuel(A/F)ratio on fuel consumption characteristics and emission characteristics are quantitatively analyzed firstly.Then,the dynamic combined cost map is derived by combining the individual cost maps with predefined weighting factors,so as to balance the potentially conflicting goals of fuel consumption and emissions reduction in the choice of operating point.The PSO is utilized to search the optimum APU operating point in the DCM.Finally,the effectiveness of the proposed approach is validated under bench experiments.5.In order to overcome the drawbacks of the weighted sum method when solving multi-objective optimization problem,the APU fuel consumption and emissions multiobjective optimization model(MOPM)is constructed firstly,in which the APU characteristics of fuel-electricity conversion efficiency,HC emissions,CO emissions and NOx emissions are all considered.Then,the multi-objective particle swarm optimization(MOPSO)algorithm and weighted metric decision method are utilized to solve the MOPM.The APU global optimal operating point and multi-objective optimal operating curve under specific power are obtained consequently.The experimental results under NEDC,FTP and HWFET driving cycles show that the proposed approach improves the fuel economy and emissions effectively.6.In order to solve the APU operating point multi-objective optimization problem more effectively,the self-adaptive multi-objective differential evolution algorithm(SAMODE)is proposed in one hand.In SAMODE,both the mutation vector generation strategy and the control parameters are self-adapted according to the feedback information of the evolution process.Consequently,the mutation vector generation strategy along with the control parameters which generate better solutions will be chosen adaptively at different evolution phases and for different individuals.Furthermore,the adaptive grids mechanism is employed to improve the diversity of the solutions due to its low computation complexity and high diversity preserving ability.In the other hand,the fuzzy optimality decision maker(FODM)is proposed to find out the best compromise operating point from the Pareto optimal set.Finally,the SAMODE and FODM are employed to solve the APU operating point MOPM which contains four objective functions fuel-electricity conversion efficiency,HC emissions,CO emissions and NOx emissions,and the optimization results show that the proposed SAMODE and FODM can be used as an effective method to solve the APU operating point multi-objective optimization problem.Finally,the main works and innovations of this thesis are summarized and the works in future are prospected.