Robust energy management method and system for intelligent networked hybrid electric vehicle

Abstract

The invention provides a robust energy management method and system for an intelligent networked hybrid electric vehicle. The method comprises the steps of obtaining an energy-saving driving decisionbased on man-machine cooperation and global and real-time working condition updating based on intelligent network connection; executing an energy management strategy based on deep reinforcement learning based on the energy-saving driving decision and the global and real-time working condition update; performing strategy robustness correction on the energy management strategy based on deep reinforcement learning to obtain a corrected robustness control strategy; and applying the corrected robust control strategy to the hybrid electric vehicle to obtain an energy distribution result of the hybrid electric vehicle. According to the robust energy management method for the intelligent networked hybrid electric vehicle, the robustness problem of the deep reinforcement learning energy managementstrategy is effectively solved, the energy-saving effect of the hybrid electric vehicle in the networked environment is improved, and the method has good engineering application value.

Description

technical field [0001] The invention relates to the technical field of automobile power, in particular to a robust energy management method and system for an intelligent network-connected hybrid electric vehicle. Background technique [0002] In recent years, hybrid energy management strategies based on deep reinforcement learning have been extensively studied and compared with strategies based on rules and optimal control theory, their superiority has been demonstrated. This strategy can achieve better fuel economy and emission performance through extensive training and combined with intelligent network information. [0003] The hybrid energy management strategy based on deep reinforcement learning is still in the theoretical stage, mainly due to the robustness problem. Due to the complexity and uncertainty of the actual working conditions, it is difficult to reflect all the real working conditions in the offline calibration and training process; at the same time, there ar...

AI Technical Summary

Problems solved by technology

Due to the complexity and uncertainty of the actual working conditions, it is difficult to reflect all the real working conditions in the offline calibration and training process; at the same time, there are unmodeled dynamic features in the training model, so the actual control process often causes battery overcharging, Over-discharge, causing the system to collapse, so it is not yet feasible for industrial applications

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