Long-distance high-timeliness economical cruise vehicle speed planning method

Abstract

The invention relates to a long-distance high-timeliness economical cruise vehicle speed planning method, which comprises the following steps of: firstly, acquiring road coordinates and altitude dataof a future distance through a global positioning system and a geographic information system according to travel planning; then, adopting a composite second-order cubic Bezier curve for fitting to generate a position-based road gradient and an initial vehicle speed curve; secondly, encoding and decoding the vehicle speed curve by adopting a genetic algorithm, and taking a cruise control curve as an initial population; randomly generating a vehicle speed curve by adopting selection, crossover and mutation algorithms, and evaluating the fuel consumption fitness; and obtaining an optimal vehiclespeed curve through iterative computation, and storing the optimal vehicle speed curve in a vehicle control unit memory. Finally, a PID controller is adopted for optimal vehicle speed tracking control, a vehicle speed curve is randomly generated through a genetic algorithm, fuel consumption evaluation is conducted, and dynamic planning or quadratic planning is avoided.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to economical cruising of vehicles. Background technique [0002] With the rapid development of intelligent transportation systems, traffic information is widely used in the energy management of hybrid electric vehicles, which broadens the scope of application of energy management strategies and enables them to adapt to various working conditions, effectively promoting the development of related fields. The mainstream optimization methods used in management strategy optimization are divided into two types: dynamic programming and model predictive control. Scholar Cheng Q adopted a vehicle speed optimization scheme based on dynamic programming in his research on ecological driving assistance systems (ChengQ, Nouveliere L, Orfila O.A new eco-driving assistance system for a lightvehicle:Energy management and speed optimization[C] / / 2013 IEEE Intelligent Vehicles Symposium(IV).I...

AI Technical Summary

Problems solved by technology

However, due to the small distance of each optimization calculation, the optimal speed of the vehicle in the entire road cannot be guaranteed.

[0003] It can be seen that the real-time calculation of vehicle speed optimization based on dynamic programming cannot be guaranteed, which limits its application in the study of vehicle economical driving, while the vehicle speed optimization method based on model predictive control cannot guarantee the global optimum. With its inevitable defects, it is urgent to propose a cruise speed planning method that satisfies the global optimum, has a long optimization time domain, and has high timeliness

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