Construction Of Actual Cycle Considering Slope And Applying To Optimization Of Control Parameters For Hybrid Commercial Vehicles

Driving cycle is the driving characteristics of the vehicle in a specific area,which is used to judge the pollutant emissions and fuel consumption of the vehicle.It provides a basis for vehicle parameter design and optimal control,and has an important impact on the fuel economy,emission,comfort and reliability of the vehicle.Due to China’s vast territory and diverse terrain,there are obvious differences in traffic conditions and road conditions in different regions,resulting in a large deviation between some characteristic parameters of standard test conditions and actual road driving conditions.At the same time,the standard test conditions do not involve the slope,and the default slope is zero.However,according to the research,the road slope has a great influence on the fuel consumption,which makes it significantly different in the evaluation of vehicle energy consumption.Therefore,it is of great significance for the follow-up study of hybrid vehicles to construct the actual driving conditions considering the slope.In this paper,the actual driving conditions of commercial vehicles considering slope are constructed,and the forward simulation model of P2 configuration hybrid commercial vehicle is built.Taking the actual driving conditions as the cycle conditions,the problem that the rule strategy can not give full play to the fuel saving potential is solved by optimizing the control parameters,so as to improve the fuel economy of the vehicle under the actual driving conditions.The specific work completed is as follows:（1）The actual working conditions of commercial vehicles considering slope are constructed.Based on the vehicle networking platform,the historical driving data of the vehicle are collected,and the data missing problems of the original data are corrected to obtain the data that can be directly applied to the subsequent working conditions.A road slope calculation and optimization method based on mileage is proposed to provide data basis for the construction of subsequent slope conditions.The Markov chain method is used to divide the state of the processed vehicle speed and slope data,and the Kneser-Ney smoothing method is used to estimate the state transition probability.Finally,the actual driving cycle of commercial vehicle considering slope is constructed based on Markov Monte Carlo method.Compared with the original data,the average error of the characteristic parameters of the actual driving cycle is 5.16%,and the probability density distribution of velocity-acceleration is basically consistent with the original data,which verifies the accuracy and effectiveness of the constructed driving cycle.In addition,there are obvious differences between the actual driving conditions and the standard test conditions in terms of average speed and average acceleration in the acceleration section,which reflects the influence of the actual operating environment.（2）Modeling and energy management strategy of P2 hybrid commercial vehicle.The configuration characteristics and different working modes of the P2 configuration hybrid commercial vehicle are analyzed in detail.The forward simulation model of the vehicle including vehicle longitudinal dynamics,engine,drive motor,power battery and driver is established.Taking the actual driving condition as the driving cycle,the rule-based energy management strategy commonly used in engineering and the dynamic programming-based energy management strategy which can ensure the global optimization are built.The results of DP algorithm are used as the standard to evaluate the advantages and disadvantages of the rule-based strategy,the vehicle simulation fuel consumption under the dynamic programming strategy is 13.85%lower,indicating that there is still a large optimization space based on the rule strategy,which provides a reference evaluation standard for the subsequent management strategy control parameter optimization research.（3）Optimization of rule strategy control parameters based on actual driving conditions.The deviation rate of fuel consumption rate between the upper and lower limits of the optimal working range of the engine and the optimal fuel economy curve k_h and k_l,the minimum working speed n₀ of the engine and the SOC value of the maintenance SOC_obj are selected as the optimized control parameters,and the feasible region and initial value are determined.Taking the fuel consumption of 100 km as the objective function,the maximum speed,the maximum climbing degree and the acceleration time of 0～50 km/h as the constraints.The multi-island genetic algorithm is selected as the optimization algorithm,and the actual driving conditions are used as the cycle conditions.The control parameters are optimized in the Isight software environment.The results are compared and analyzed from three aspects:battery SOC,engine and motor torque output and fuel consumption.The simulation results show that the fuel consumption of the rule strategy is reduced by7.21%after the control parameter optimization,and the fuel saving effect is obvious,which verifies the effectiveness of the control parameter optimization.