Research On Model Free Adaptive Control Method For Braking Energy Recovery Of Electric Buses

In recent years,the pure electric bus has been rapidly developed and applied,but the bottleneck of its short mileage and insufficient power has not been solved.The research shows that under urban driving conditions,the braking process consumes 30%to 50% of driving energy,so the recovery of braking energy can effectively improve the driving efficiency of pure electric buses.However,only the rear wheel braking of pure electric buses can recover energy;From the perspective of safety,the vehicle should avoid braking completely by the rear wheels.On the basis of ensuring the safety and effectiveness of vehicle braking,the key issue of braking energy recovery control is to reasonably distribute the braking torque of front and rear wheels and recover as much braking energy as possible.In the current design process of braking energy recovery controller,most methods are based on the accurate mathematical model of the controlled object.However,the vehicle and braking system are complex nonlinear systems with multiple model variables,which operate under changing parameters and conditions.Obviously,it is not feasible to establish an accurate vehicle dynamics model and then design the control system.Therefore,based on the data driven model-free adaptive control(MFAC)method,this paper proposes an anti-saturation model-free adaptive control(AS-MFAC)algorithm,and proposes two parameter optimization methods for AS-MFAC.The main contents of this article include:(1)This paper studies and analyzes the automatic regeneration of pure electric buses,and proposes a model-free adaptive control algorithm with anti-saturation function(ASMFAC)for the distribution of braking force between the front and rear wheels of pure electric buses.The algorithm introduces a new parameter,called anti-saturation factor,which can effectively deal with actuator saturation in the control process and desaturation.Rigorous mathematical analysis and convergence proof are carried out for the algorithm.Matlab numerical simulation proves the effectiveness of the algorithm.(2)For the parameter optimization of anti-saturation model-free adaptive control algorithm,a grey wolf optimizer-based anti-saturation model-free adaptive control algorithm(GWO-AS-MFAC)is proposed.GWO algorithm can achieve the goal of controller parameter optimization by imitating the wolf group cooperation mechanism.The effectiveness of the algorithm is verified by a series of numerical simulations.Through the AVL Cruise simulation platform,the feasibility and effectiveness of the application of the algorithm in the brake energy recovery system are verified.(3)Aiming at the problem of parameter optimization of anti-saturation model-free adaptive control algorithm,an improved multiverse optimization-based anti-saturation model-free adaptive control algorithm(IMVO-AS-MFAC)is proposed.The IMVO algorithm is based on the phenomenon of multiple universes,and achieves the goal of parameter optimization by simulating the principle that the matter in each universe is transferred from a black hole to a white hole.This paper improves the initial population distribution and location update strategy of IMVO algorithm.The improved algorithm has better optimization effect than the traditional algorithm.The final numerical simulation results show that GWO-AS-MFAC algorithm has a good convergence rate,while IMVO-AS-MFAC algorithm has a good convergence effect,and a more suitable algorithm can be selected according to the actual requirements.