Fuel cell vehicle energy management method based on deep reinforcement learning algorithm

Abstract

The invention relates to a fuel cell vehicle energy management method based on a deep reinforcement learning algorithm, and belongs to the field of new energy vehicles. The method comprises the following steps: S1, acquiring state information of a fuel cell vehicle; s2, building a fuel cell vehicle energy management system model; and S3, constructing a fuel cell vehicle energy management strategyby using a deep reinforcement learning algorithm, and solving a multi-objective optimization problem including fuel economy and fuel cell efficiency, thereby obtaining an optimal energy distribution result. According to the invention, the deep reinforcement learning algorithm is applied to the energy management system of the fuel cell vehicle, so that the optimization and real-time performance aregood; meanwhile, the working efficiency of the fuel cell is considered in the reward function, and a new thought is provided for energy management.

Description

technical field [0001] The invention belongs to the field of new energy vehicles, and relates to a fuel cell vehicle energy management method based on a deep reinforcement learning (DQN) algorithm. Background technique [0002] At present, traditional vehicles are facing problems such as environmental pollution, global warming, and limited oil resources, making automakers turn their attention to the research of hybrid vehicles, electric vehicles and fuel cell vehicles. As a transition model from traditional cars to future clean cars, hybrid vehicles usually consist of energy storage systems, electric motors and internal combustion engines, which still consume fuel oil and generate pollution. At the same time, due to the limited driving distance and long charging time of electric vehicles composed of batteries and electric motors, it has become a major obstacle to its commercialization. Therefore, with the development of fuel cell technology, the fuel cell vehicle (Fuel cell...

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Problems solved by technology

However, the energy management strategies mentioned above are difficult to satisfy real-time performance and optimality at the same time. For example, although energy management based on rules and local optimization can be applied to real-time control, its optimality cannot be guaranteed; Although the energy management strategy can obtain the global optimal solution, the amount of calculation is too large to be applied to real-time control of real vehicles

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