Multi-Objective Collaborative Optimization For The Parameters Of Dual Motor Coaxial Series-Parallel Hybrid Powertrain In Hybrid Buses

The technology of Plug-in Hybrid Electric Vehicle(PHEV)is one of the effective ways to solve the current environmental pollution and energy shortage problems.In this study,a Plug-in Hybrid Electric Bus(PHEB)powertrain is taken as the research object.By the coordinated optimization method of structure parameters and control strategy,the problems of the torsional vibration in the engine start condition,the noise of the transmission system in the engine intervention condition,and the fuel economy promotion are studied together.A system level and uncertainty multi-objective optimization methond based on reliability and robustness analysis is proposed.The main research achievements are as follows:(1)A collaborative vibration suppression optimization method based on structure and control parameters is proposed.A 5-degree-of-freedom Lagrange equation is built,and the nonlinear stiffness of the three stage double mass flywheel is derived.In order to build a system-level optimization model,the torsional vibration damper's stiffness parameters and ISG motor torque control parameters are taken as the design variables,and the engine speed?ISG motor and spline shaft torque are taken as the contraints,and the average torque of the transmission system in the engine start condition is takend as the optimization objective.By the method of sensitivity analysis and the main effect analysis,the design variables are processed by “dimensionality reduction”.The results show that,under the premise of satisfying the constraints,the average torque of the transmission system is reduced from 1956 Nm to 1820 Nm,and the maximum torque at the spline shaft is reduced from 1496 Nm to 1283 Nm.(2)A RBF neural network based collaborative optimization method for structural parameters and control strategy is proposed.The fuel enonomy,the comfort index of the transmission system and the noise index of the engine intervention condition in PHEB are analysised together.The results show that there is a coupling relationship between the three indexes in the speed deceleration ration of the main reducer and the speed threshold of the hybrid drive mode.Through the Pareto frontier of multi-objective optimization,it can be seen that there is a convex function relationship between the fuel economy and the confort index of the transmission system.On the premise of ensuring that the fuel enonomy index and the noise index of the engine intervention condition does not exceed the condition,the comfort index of the transmission system can be increased by 22.5%.(3)The complex system optimization problems with the characters of multi-variable,multi-objective,multi-condition,multi-coupling,and multi-scale are discussed.Aiming at the multidisciplinary design optimization model of PHEB,the advantages and disadvantages of four typical multidisciplinary design optimization methods(AIO,CO,CSSO,BLISS)are analyzed and compared.The comparison results show that the method of CO has the fastest convergence speed,but it can not solve the Pareto front of the objective function.Since the method of CSSO has the advantage of modification,a representation method of the agent model based on RBF neural network in the framework is proposed.The optimization results show that,compared with the traditional optimization method,the improved method of CSSO can reduce the convergence iteration steps from3500 to 500,and can find the Pareto frontier of multi-objective optimization problem.(4)The method of “ascending dimension screening” is proposed which can screen the Pareto point set from the robust dimension.The method of MVM?FORM?SORM is derived,and the method of Monte Carlo is used to compare the accuracy of the three algorithms in calculating the reliability index of constraint conditions.It is found that the calculation of the SORM is the closest to Monte Calo method,and an uncertainty multidisciplinary optimization method is proposed.With the combination of deterministic optimization,robustness and reliability analysis,the problem of excessive computation in the uncertain optimization method based on the traditional Monte Carlo method is solved.By changing the mean square variance of PHEB different design variables,the constraint conditions can reach the target of 6? under the influence of typical uncertain factors.(5)By the method of the bench test measurement of the PHEB powertrain,the static resistance of the engine,the transient shock torque at the moment of diesel fuel compression and the time domain signal of the torque of the ISG motor are obtained.The torque of the spline shaft between the ISG motor and the torsional vibration damper is optimized before and after optimization.The results show that the maximum torque of the spline shaft of the powertrain is reduced from 1442 Nm to 1138 Nm.The error between the experimental results and the simulation results is 13%.The test results also show that the torque variation of the optimized main reducer is more stable in the condition of engine intervention.