Distributed electric powertrain test bench with dynamic load controlled by neuron PI speed-tracking method

Abstract

Based on the real-time model and platform, a dynamic hardware-in-the-loop (HIL) testing method for the distributed powertrain of electric vehicle (EV) is proposed. Compared with static point testing, the dynamic HIL test can provide a more realistic working environment for the EV's distributed electric powertrain (DEP) early development. The test data of electric motor's efficiency and maneuver performance under a dynamic work condition are more authentic and meaningful. Meanwhile, the driver-vehicle-road real-time (DVRRT) model is set up to emulate the actual condition. The speed-tracking control method with proportional-integral (PI) gains tuned by the neuron network algorithm is used to generate the distributed real-time loads. Maximum adhesion limitation is added once the slipping is detected in the real-time model. Simulation and experiment of the test bench are done. The generated distributed load is compared with both the theoretical one and the simulated one in the Carsim software platform. Two comparisons show the similar results. The load accuracy is high, but there is a short time delay. The mechanical work measured by the experiment test bench is highly consistent (97.5%) with the theoretical value. As a result, the proposed test bench and its control method can be used for DEP efficiency test.