A Method of Energy Management for Parallel Hybrid Power System Based on DQN Variant

Abstract

The invention discloses an energy management method of a hybrid hybrid power system based on a DQN variant, which belongs to the technical field of hybrid hybrid vehicles and can improve the training convergence speed and the fuel economy of the vehicle; the invention includes: establishing a hybrid hybrid system The hybrid electric vehicle model obtains the environmental parameters that affect the energy management strategy, including road gradient and vehicle quality; uses the dynamic programming (DP) algorithm to solve the optimal energy management strategy, and saves the experience into the optimal experience pool (OEB) , combined with Hybrid Experience Replay (HER) technology, using the Dueling DQN strategy to train the model, obtain the trained deep reinforcement learning agent, and perform the energy management of the hybrid hybrid vehicle under different working conditions. The HER technology and DQN variant Dueling architecture constructed by the present invention can effectively improve the training convergence speed, vehicle fuel economy and algorithm robustness.

Description

technical field [0001] The invention belongs to the technical field of hybrid hybrid vehicles, and in particular relates to an energy management method for a hybrid hybrid system based on a DQN variant. Background technique [0002] In today's increasingly serious energy crisis, vehicle emission standards are gradually becoming stricter, and the use of pure fuel vehicles has been challenged, while hybrid vehicles take into account the long driving range of fuel vehicles and the emission-free electric vehicles to solve the problem of fossil fuel combustion Therefore, the energy management of hybrid power has always been the key to research. [0003] At present, the energy management of hybrid electric vehicles is mostly a rule-based strategy. By setting a certain energy management threshold, the most common rule for plug-in hybrid electric vehicles is to first consume battery energy, and then maintain battery power. energy control. The representative benchmark of the optimi...

AI Technical Summary

Problems solved by technology

[0004] However, there are some shortcomings in the methods in the prior art. The energy management effect based on rules is often not significant enough. For a single working condition, a lot of experience and knowledge are required. The optimization-based DP requires the global working conditions to be known, and it cannot be real-time due to the long calculation time. Online application, the existing model prediction can be optimized and carried out in real time, but the step size of the prediction control cannot be selected too large, and there is still a large gap compared with the optimization result of DP

And many optimization methods are not considered comprehensively, ignoring the road slope information and the change of the car's own vehicle quality

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