Aiming at the adaptive problem of driving conditions in the energy management process of Hybrid Electric Vehicle (HEV), the research of HEV powertrain multi-intelligent body control system based on condition recognition is carried out with two-axle parallel HEV as the research object. Firstly, the theory of Multi-Agent System (MAS) is introduced into the vehicle power system, and a vehicle powertrain control model is constructed; the working condition recognition module is established, and it is embedded into the ADVISOR vehicle simulation software together with the energy management strategies of four different working condition control types, and compared and analyzed with the electrically-assisted strategies, and the experiments are carried out on the basis of the D2P technology. Based on D2P technology, an experimental rig is built to verify the effectiveness and feasibility of the proposed strategies.
In order to solve the problem that minimum control strategy of equivalent fuel consumption for plug-in hybrid electric vehicles (PHEVs) is not effective, an ECMS energy management strategy based on improved annealing algorithm was designed. Firstly, based on the traditional control model, the optimized PID equivalent factor control model is constructed. In order to solve the problem of overfitting when optimizing the traditional annealing algorithm (SA), tempering link and memory search function were added to the algorithm, and the improved algorithm was used to optimize the PID equivalent factor, and the vehicle dynamics simulation was carried out in MATLAB/SIMULINK environment. The results show that: Under the Worldwide Light-duty Test Cycle (WLTC) conditions, compared with the rule-based logical threshold control strategy and the pre-optimized ECMS strategy, the optimized ECMS strategy increased the fuel saving rate by 18.49% and 3.28%, respectively. Under the New European Driving Cycle (NEDC) conditions, compared with the rule-based logical threshold control strategy and the pre-optimized ECMS strategy, the fuel saving rate is increased by 20.20% and 2.61%, respectively. The proposed model effectively reduces fuel consumption and has good robustness.
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