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A PHEV energy management method based on deterministic policy gradient learning

A technology of energy management and PHEV, which is applied in climate sustainability, biological neural network models, hybrid vehicles, etc., can solve the problems of training efficiency and strategy generalization ability to be improved

Active Publication Date: 2019-10-18
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

However, the training efficiency and policy generalization ability of energy management strategies based on basic reinforcement learning algorithms, especially those using tabular policy representations, still need to be improved

Method used

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  • A PHEV energy management method based on deterministic policy gradient learning
  • A PHEV energy management method based on deterministic policy gradient learning
  • A PHEV energy management method based on deterministic policy gradient learning

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Embodiment Construction

[0095] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0096] A PHEV energy management method based on deterministic policy gradient learning provided by the present invention, such as figure 1 As shown, it specifically includes the following steps:

[0097] Step 1. Use the deep neural network (DNN) to build the action network (Actor) and action value network (Critic) respectively, and together form the basic network framework (AC network) of the deterministic policy gradient learning algorithm to build the PHEV energy management policy model learning ; and perf...

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Abstract

The invention provides a PHEV energy management method based on deterministic policy gradient learning, and realizes closed-loop application including policy training, online application, effect detection, feedback update, etc. in PHEV energy management based on deterministic strategy gradient learning. Compared with the prior art, the PHEV energy management method has higher accuracy, greatly improves the efficiency and reliability of PHEV energy management and has the beneficial effects that many current management policies do not have.

Description

technical field [0001] The present invention relates to a plug-in hybrid electric vehicle (Plug-in Hybrid Electric Vehicle, referred to as PHEV) energy management technology, in particular to a deterministic strategy gradient learning algorithm for PHEV energy, including strategy training, online application, effect detection, feedback Closed-loop management method and application of update etc. Background technique [0002] For urban working conditions, the advantages of plug-in hybrid electric vehicles (Plug-in Hybrid Electric Vehicle, PHEV for short) in energy saving and emission reduction are very prominent, but how to coordinate the energy distribution among various vehicle power sources to achieve efficient energy management is of great importance to Playing to its strengths is crucial. Since PHEVs are equipped with large-capacity power batteries and can be charged through the grid in a timely manner, the state of charge (SoC) of the power batteries can vary in a wide...

Claims

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Application Information

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IPC IPC(8): B60W20/11B60W50/00G06N3/04
CPCB60W20/11B60W50/00B60W2050/0008G06N3/045Y02T90/14
Inventor 何洪文李岳骋彭剑坤
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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