In this paper, a multi-objective hierarchical prediction energy management strategy is proposed to achieve optimal fuel cell life economy and energy consumption economy for a range extended fuel cell vehicle.First, a global state of charge rapid planning method is proposed based only on the expected driving distance.Then, the vehicle speed information in the prediction horizon is estimated by a vehicle speed prediction module based on the back propagation neural network. According to the predicted speed and state of charge reference, a novel fusion algorithm that combines the direct configuration method and sequential quadratic programming is proposed to achieve optimal fuel cell life economy and energy consumption economy in the prediction horizon. Simulation results validate that the proposed strategy can effectively reduce the operating costs compared with that of the charge depletion-charge sustaining strategy and the equivalent consumption minimization strategy, thereby proving the feasibility of the proposed strategy.

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This paper focuses on the dynamic modeling and analysis of dry dual clutch transmissions (DCTs) during vehicle launch and shifts. The system model incorporates the clutch torque control strategies for DCT vehicle launch and shifts. This model is capable of quantitatively analyzing and predicting dual clutch transmission dynamic characteristics and performance metrics, such as launch response and shift patterns, thus providing an analytical tool for DCT torque control and calibration. The model is applied for a test vehicle equipped with a dry dual clutch transmission. The simulation data on vehicle launch and shift dynamic characteristics are highly agreeable to the data obtained on the test vehicle.

Abstract:The driving pattern has an important influence on the parameter optimization of the energy management strategy (EMS) for hybrid electric vehicles (HEVs). A new algorithm using simulated annealing particle swarm optimization (SA-PSO) is proposed for parameter optimization of both the power system and control strategy of HEVs based on multiple driving cycles in order to realize the minimum fuel consumption without impairing the dynamic performance. Furthermore, taking the unknown of the actual driving cycle into consideration, an optimization method of the dynamic EMS based on driving pattern recognition is proposed in this paper. The simulation verifications for the optimized EMS based on multiple driving cycles and driving pattern recognition are carried out using Matlab/Simulink platform. The results show that compared with the original EMS, the former strategy reduces the fuel consumption by 4.36% and the latter one reduces the fuel consumption by 11.68%. A road test on the prototype vehicle is conducted and the effectiveness of the proposed EMS is validated by the test data.

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