Composite braking AMT shift strategy for extended-range heavy commercial electric vehicle based on LHMM/ANFIS braking intention identification

Abstract

In order to improve the safety and braking energy recovery rate of the composite braking system for extended-range heavy commercial electric vehicle, AMT shift control strategy is studied based on Layering Hidden Markov Model/Adaptive Neuro-fuzzy Inference System (LHMM/ANFIS) braking intention identification model. Firstly, according to the requirement of the AMT shift strategy in braking process, the braking intentions were classified into normal braking condition and emergency braking condition. Then combined with the composite braking force distribution, the motor braking power generation characteristics and the critical condition of dangerous working state, the AMT shift strategy was analyzed and established under two braking conditions. Finally, to verify the control effect, the verification test was carried out with the initial braking speed of 60 km h−1 under the normal braking condition and the emergency braking condition separately on the hardware in the loop simulation platform based on A&D 5435 and the testing vehicle. Meanwhile, simulation study was completed in Matlab/Simulink under NEDC_90 cycle condition. The experiment and simulation results show that the developed AMT shift control strategy can accurately identify the braking intention, and the transmission shifts correctly according to corresponding conditions, which can also make the motor operating points closer to the high efficiency area. Therefore, the AMT shift control strategy proposed in this paper can effectively improve the braking energy recovery rate, and ensure the braking safety and stability.