A Method for Energy Management of Hybrid Electric Vehicle Based on Reinforcement Learning

Abstract

The invention relates to a method for energy management of hybrid electric vehicles based on reinforcement learning, comprising the following steps: obtaining the energy management strategies of hybrid electric vehicles under different cycle working conditions based on the Q-learning algorithm in reinforcement learning; writing the energy management strategies into Microcontroller: determine the current cycle working conditions, the data acquisition system obtains the current driving parameters and transmits them to the microcontroller, the microcontroller obtains the control action based on the energy management strategy under the current cycle working condition, and transmits the control action to the vehicle control The vehicle controller adjusts the power system according to the control action. Compared with the prior art, the present invention obtains the energy management strategy of the hybrid electric vehicle under different cycle working conditions based on reinforcement learning and writes it into the micro-controller of the hybrid electric vehicle. The optimal control action in the state is fast, which can meet the sampling frequency of current and even future state monitoring of hybrid electric vehicles.

Description

technical field [0001] The invention relates to the technical field of hybrid electric vehicle control, in particular to an online energy management method for a hybrid electric vehicle based on reinforcement learning. Background technique [0002] In order to save resources, reduce environmental pollution, and achieve energy saving and emission reduction, hybrid electric vehicles have become one of the important directions for the development of the automobile industry today. As a key control technology for hybrid electric vehicles, energy management strategies directly affect the fuel economy of automobiles and become Research focus of hybrid power system. [0003] In recent years, the research on energy management strategies of HEVs can be mainly divided into two categories. One is rule-based control algorithms, such as logic threshold and fuzzy logic control algorithms. Rule-based control algorithms have clear logic and fast calculation, but the optimization effect is l...

AI Technical Summary

Problems solved by technology

However, this method is an energy management strategy that collects data in real time and optimizes it in real time. It has the common problems of many energy management strategies: a large amount of calculation, a long iteration time, and it is difficult to take into account real-time and optimal solutions. Sampling frequency, or reducing the number of iterations, the optimization effect will obviously decrease, and it is difficult to apply it on a real vehicle

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