Fuel cell automobile energy-saving driving optimization method and device

Abstract

The invention discloses an energy-saving driving optimization method and device for a fuel cell vehicle. The method comprises the steps that a fuel cell vehicle system model and a system power balance model of the fuel cell vehicle system model are established; in the electric quantity maintaining mode, the optimal energy management strategy of the fuel cell vehicle under various working conditions is solved; training based on the obtained optimal database to obtain a data-driven energy management model; establishing a training scene containing a signal lamp, and defining a state space and an action space; according to the data-driven energy management model, obtaining fuel consumption of the fuel cell vehicle in a certain state and under a corresponding action, and establishing a related return function model; and training in the training scene to obtain an energy-saving driving optimization model. According to the method provided by the invention, the advantages of joint optimization and hierarchical optimization are combined, a pre-optimized energy management strategy can be fused into fuel cell vehicle speed planning based on deep reinforcement learning, and the balance of energy consumption economy and real-time performance is realized.

Description

technical field [0001] The invention relates to the field of fuel cell vehicle speed planning and energy management, in particular to an energy-saving driving optimization method for fuel cell vehicles. Background technique [0002] Intelligent and connected vehicles are expected to revolutionize the transportation of people and goods, thereby improving road safety, improving ride comfort, improving traffic efficiency and reducing energy consumption. Through vehicle-to-infrastructure V2I communication, connected vehicles can obtain real-time signal light information on the road, and realize energy-saving driving by optimizing vehicle speed trajectory. [0003] Fuel cell vehicle is a vehicle driven by hybrid energy, and its energy-saving driving involves speed planning and energy management. The optimal solution to this problem is to minimize the total energy consumption of the hybrid energy, and conduct joint optimization of vehicle speed and energy. However, due to the cu...

AI Technical Summary

Problems solved by technology

By completely decoupling vehicle motion planning and powertrain control, hierarchical optimization can reduce computational costs, but also sacrifices some optimality

[0004] In the existing technology, there are few energy-saving driving methods involving fuel cell vehicles in signal light scenarios, and it is difficult to achieve a balance between optimality and real-time performance

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