Hybrid power bus energy management method, hybrid bus energy management method equipment and storage medium

Abstract

The invention discloses a hybrid bus energy management method, hybrid bus energy management method equipment and a storage medium. The method comprises: obtaining parameters influencing energy management of an experimental vehicle under the bus fixed route working condition; based on the parameters influencing the energy management and the observed quantity training model, obtaining a trained deepreinforcement learning agent; and obtaining parameters influencing the energy management in actual driving of the bus and an observed quantity, and performing energy management of the hybrid power bus under a fixed route working condition based on the parameters influencing the energy management in actual driving and the trained deep reinforcement learning agent. By the adoption of the technicalscheme, more effective control over energy management of the hybrid power bus can be achieved, and energy consumption is reduced.

Description

technical field [0001] The present application belongs to the technical field of hybrid electric vehicles, and in particular relates to a method, device and storage medium for energy management of hybrid electric buses. Background technique [0002] The energy management of hybrid electric vehicles is mostly a rule-based strategy. By setting a certain energy management threshold, the most common plug-in hybrid electric vehicle rule is to first consume battery energy, then maintain battery power, and carry out regulatory regulation. energy control. [0003] The representative benchmark of the strategy based on optimization is DP (Dynamic Programming, dynamic programming). The relative optimal energy management of the hybrid bus obtained offline when the global working condition information is known, it uses the known speed working conditions to allocate the corresponding optimal energy demand for the hybrid bus engine and battery to obtain Optimal energy management. [000...

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Problems solved by technology

[0006] Through the above method, it can be seen that the effect of rule-based energy management is not significant enough, and for a single working condition, the optimization-based DP requires the global working conditions to be known, and the real-time online application cannot be performed because the calculation time is too long, and the existing model prediction can be done Optimization and real-time, but the predictive control step size cannot be selected too large, and there is still a large gap compared with the optimization result of DP

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