A Fuzzy-based Sliding Mode Control Approach for Acceleration Slip Regulation of Battery Electric Vehicle

Due to quick response and large quantity of electric motor torque,the traction wheels of battery electric vehicle are easy to slip during the initial phase of starting.In this paper,a sliding mode control approach of acceleration slip regulation is designed to prevent the slip of the traction wheels.The wheel slip ratio is used as the state variable for the formulation of system dynamics model.The fuzzy algorithm is utilized to adjust the switch function of sliding mode controller.After stability and robustness analysis,the sliding mode control law is transferred into C code and downloaded into vehicle control unit,which is validated under wet and dry road conditions.The experimental results with a small overshoot and a quick response during starting indicate that the sliding mode controller has good control efect on the slip ratio regulation.This article proposes an acceleration slip regulation method that improves the safety during acceleration for battery electric vehicle.

Fuzzy Sliding Mode Control Based on Longitudinal Force Estimation for Electro-mechanical Braking Systems using BLDC Motor

This paper focuses on the controller design using fuzzy sliding mode control(FSMC)with application to electro-mechanical brake(EMB)systems using BLDC Motor.The EMB controller transmits the control signal to the motor driver to rotate the motor.The torque distribution of motors is studied in this paper actually.Firstly,the model of the EMB system is established.Then the state observer is developed to estimate the vehicle states including the vehicle velocity and longitudinal force.Due to the fact that the EMB system is nonlinear and uncertain,a FSMC strategy based on wheel slip ratio is proposed,where both the normal and emergency braking conditions are taken into account.The equivalent control law of sliding mode controller is designed on the basis of the variation of the front axle and rear axle load during the brake process,while the switching control law is adjusted by the fuzzy corrector.The simulation results illustrate that the FSMC strategy has the superior performance,better adaptability to various types of roads,and shorter braking distance,as compared to PID control and traditional sliding mode control technologies.Finally,the hardware-in-loop(HIL)experimental results have exemplified the validation of the developed methodology.

Determination of the dynamic characteristics of locomotive drive systems under re-adhesion conditions using wheel slip controller

To investigate the re-adhesion and dynamic characteristics of the locomotive drive system with wheel slip controller,a co-simulation model of the train system was established by SIMPACK and MATLAB/SIMULINK.The uniform running and starting conditions were considered,and the influence of structural stiffness of the drive system and the wheel slip controller on the re-adhesion and acceleration performance of the locomotive was investigated.The simulation results demonstrated that the stick-slip vibration is more likely to occur in locomotives with smaller structural stiffnesses during adhesion reduction and recovery processes.There are many frequency components in the vibration acceleration spectrum of the drive system,because the longitudinal and rotational vibrations of the wheelset are coupled by the wheel‒rail tangential force when stick-slip vibration occurs.In general,increasing the structural stiffness of the drive system and reducing the input energy in time are effective measures to suppress stick-slip vibration.It should also be noted that inappropriate matching of the wheel slip controller and drive system parameters may lead to electro-mechanical coupling vibration of the drive system,resulting in traction force fluctuation and poor acceleration performance.

Benchmark of an intelligent fuzzy calculator for admissible estimation of drawbar pull supplied by mechanical front wheel drive tractor

This paper proposes a calculator for estimation of drawbar pull supplied bymechanical front wheel drive tractor based on nominal input variable of tractor drivingmode in two-wheel drive(2WD)and four-wheel drive(4WD),and numeral input variables of tractor weight(53.04–78.45 kN)and slip of driving wheels(1.4–15.1%)utilizing intelligent fuzzy systems.The systemswere developed bymeans of various input membership functions,output membership functions,defuzzification methods,and training cycles.The prominent developed system for estimation of the drawbar pull yielded a user-friendly intelligent fuzzy calculator with admissible accuracy(coefficient of determination=0.993).Data obtained from the calculator revealed increasing nonlinear trend of the drawbar pull in range of 12.9–57.5 kN as concurrent augment of slip of the wheels and tractor weight,for 2WD mode.In case of the 4WD mode,it nonlinearly raised from 12.8 to 77.7 kN.Therefore,effect of the slip and weight on the drawbar pull was found synergetic.Moreover,the drawbar pull ranges elucidated that the drawbar pull proliferated as the 4WD mode was employed rather than the 2WD mode.Generally,benchmark of the prominent developed intelligent fuzzy system,not only provide simple calculator with the widest applicability for different tractormodels,but also produces added values in enrichment of realization level in domain of tractor drawbar pull concepts.

Reliable execution of a robust soft computing workplace found on multiple neuro-fuzzy inference systems coupled with multiple nonlinear equations for exhaustive perception of tractor-implement performance in plowing process

Tendency towards computer simulations linked to agricultural machinery has enormously increased in recent years.In this regard,the principal contribution of current research was to develop soft computing simulation workplaces for performance prognostication of tractor-implement system in plowing process.Two neurofuzzy strategies based on multiple adaptive neuro-fuzzy inference systems(MANFIS)scenario and the MANFIS coupled with multiple nonlinear equations(MNE)scenariowere executed in theworkplace.Additionally,neural strategy based on artificial neural network(ANN)scenario was also fulfilled in the workplace.Operational variables of plowing depth(10–30 cm),forward speed(2–6km/h),and tillage implement type(moldboard,disk,and chisel plow)were considered as theworkplace inputs and ten performance parameters were taken as the workplace outputs.According to the obtained prognostication accuracy,simulation time,and user-friendly configuration of three scenarios(ANN,MANFIS,andMANFIS+MNE),the MANFIS+MNE was recognized as the prominent simulation scenario.According to the MANFIS+MNE workplace results,for each tillage implement,the compound effect of plowing depth and forward speed on some performance parameters(required draft force of implement,tractor rear wheel slip,fuel consumption per working hour,specific volumetric fuel consumption,tractor drawbar power,energy requirement for tillage implement,overall energy efficiency,and tractor tractive efficiency)was nonlinearly synergetic.However,it was nonlinearly antagonism in case of specific draft force and fuel consumption per tilled area.The MANFIS+MNE workplace simulation results provide opportunity for technical farmer associations involved in the decision-making of agricultural machinerymanagement in order to gain exhaustive fundamental insights into the compound effect of plowing depth and forward speed on performance of tractor-implement systems in plowing process.