International Journal of Advances in Scientific Research and Engineering (ijasre) E-ISSN : 2454-8006 DOI: 10.31695/IJASRE.2020.33801 Volume 6, Issue 4 May - 2020 www.ijasre.net Page 24 Licensed Under Creative Commons Attribution CC BY-NC Modeling and Simulation Performance of a Small Electric Vehicle on Different Floor Slopes Ramesh G. Pungle Professor Department of Mechanical Engineering P. E. S. College of Engineering Aurangabad, Maharashtra India ______________________________________________________________________________________ ABSTRACT This paper presents the development of a mathematical model of a small electric vehicle which is propelled by permanent magnet direct current (PMDC) motor. The tractive force required to drive the vehicle is obtained from (PMDC) motor. The torque-speed characteristics of the motor are used to obtain tractive force at given vehicle load and floor conditions. The simulation model of the vehicle for acceleration is developed, considering various forces that are acting in favour and opposite to the vehicle acceleration and also floors having different slopes. The vehicle parameters used in simulation are determined beforehand. The vehicle acceleration model is simulated in MATLAB for various floor slopes and simulation performance results are presented for the vehicle that is moving on floors like flat, upslope, downslope and more. Key Words: Modeling, Simulation, Acceleration, Vehicle, Slope. ______________________________________________________________________________________________ 1. INTRODUCTION Recently, electric vehicles including fuel-cell and hybrid vehicles have been developed very rapidly as a solution to energy and environmental problems. The selection of traction motors for the electric vehicle (EV) propulsion systems is a very important step that requires special attention. In fact, the automotive industry is still seeking for the most appropriate electric propulsion system. In this case, key features are efficiency, reliability and cost. The process of selecting the appropriate electric propulsion systems is however difficult and should be carried out at the system level. In fact, the choice of electric propulsion systems for EVs mainly depends on three factors: driver expectation, vehicle constraint, and energy source. The eclectic vehicle modeling generally consists of modeling of electric and vehicle systems. The mathematical model formulated in this paper consist of various forces like tractive force in favour of a vehicle acceleration and forces acting opposite is rolling resistance, aerodynamic drag and inertia. The grade resistance force favoursthe acceleration when a vehicle is moving on downslope and in opposes acceleration when a vehicle is moving on upslope. A problem with vehicle kinematics models is that by empirically developing mathematical expressions that describe the acceleration patterns of the vehicle, the actual components that affect the motion of the vehicle; the tractive force provided by the prime mover (PMDC motor) and the resistance forces opposing the vehicle‟s motion are not modeled explicitly. Therefore, these models are diffic ult to calibrate and do not generally provide a good fit to field data for each of the acceleration, speed, distance, and time domains. They also do not account for different vehicle types, roadway grades, and other factors that affect the vehicle acceleration patterns.The system takes voltage as an input to the electric motor, and output is the rotational speed of electric motor or the linear motion of the electric vehicle. The electric motors are capable of generating high torque at low speed, can operate efficiently over a greater range of speeds and can be smoothly controlled [1]. 2. RELATED WORK Torsten Butz et al., proposed a two-level optimization scheme, which allows estimating unknown chassis model parameters of the commercial vehicle simulation package DYNA4.. The basic parameter estimation task yields a nonlinear least-squares problem, which was numerically solved by a Levenberg–Marquardt algorithm. To increase the sensitivity of the unknown parameters with respect to the least-squares objective, the parameter estimation scheme has been enhanced by a method for optimal experimental