Energy management optimization control method for hybrid electric vehicle based on V2X

Abstract

The invention discloses a hierarchical energy management optimization control method for a hybrid electric vehicle based on V2X. Based on the Internet of Vehicles, vehicle-to-vehicle communication and vehicle-to-infrastructure communication are carried out; through traffic signal lamp timing and a model predictive algorithm integrated by an upper comptroller, the optimal target vehicle speed sequence for a given time window is acquired, the optimal target vehicle speed sequence is fed back to a driver of each vehicle in a wireless transmission mode, and the driver performs acceleration or braking according to the optimal target vehicle speed sequence. Compared with the prior art, the hierarchical energy management optimization control method for the hybrid electric vehicle based on V2X of the invention has following advantages that the optimal target vehicle speed is acquired, oil consumption for the hybrid electric vehicle can be effectively reduced on the basis of avoiding stopping for the red lamp or collision, real-time energy management for the hybrid electric vehicle is realized, and a new idea is provided for real-time energy management and optimization for the hybrid electric vehicle.

Description

technical field [0001] The present invention relates to an energy management optimization control method for a hybrid electric vehicle, in particular to a V2X-based hierarchical energy management optimization control method for a hybrid electric vehicle. Background technique [0002] The energy management control system of hybrid electric vehicles is a nonlinear, multivariable, time-varying, and non-differentiable system. It is a hot and difficult point in the field of hybrid electric vehicles, which directly affects the power, economy, and comfort of hybrid electric vehicles. and emission performance. [0003] The rule-based control method has achieved industrial application, while the optimization-based control method mainly stays in the theoretical research stage. In order to get the theoretical optimal solution, some scholars combine model prediction (model predictive control, MPC) and dynamic programming (dynamic programming, DP). Considering the time cost of calculat...

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Problems solved by technology

However, the combined algorithm of MPC and DP, ECMS, PMP or improved methods of these algorithms, as well as some other optimization-based control algorithms, have always been unable to achieve real-time control

Moreover, in the process of using these methods to solve the problem, the controlled object model is generally simplified to varying degrees, such as ignoring the driver's driving intention, actual road conditions, the influence of signal lights, and the mutual influence between vehicles, etc. The optimal solution obtained is not the optimal solution in the true sense

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