Electric Vehicle Designing, Modelling and Simulation

Vempalli, Sai Krishna; Ramprabhakar, J.; Shankar, Shobha; Prabhakar, G.

doi:10.1109/i2ct42659.2018.9058161

Cited by 17 publications

(8 citation statements)

References 6 publications

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“…While modeling an EV, the vehicle dynamics as well as the power train model need to be taken into consideration. 23 Vehicle dynamics subsystem examines the resistive forces that are present on the vehicle under a specific circumstance. Basically, three resistive forces will be acting on a vehicle.…”

Section: Electric Vehicle Modelingmentioning

confidence: 99%

Modeling and performance analysis of a lithium‐ion battery pack with an electric vehicle power‐train for different drive cycles and highway conditions

Harikrishnan,

Kanakasabapathy

2024

Energy Storage

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Lithium‐ion cell chemistries are favored in the automotive sector, as they enable electric vehicles (EVs) to compete with traditional gasoline‐powered vehicles in terms of performance, range, and cost. The life and performance of these packs depend upon the Battery management system which monitors and controls the pack. The modeling, simulation, and analysis of a lithium‐ion battery pack that closely resembles an actual automobile battery are the focus of this paper. Real cells that are commercially available and specifically developed for automotive applications are modeled in the simulation environment. The cell characterization and formation of pack topology with heat exchange are addressed. A commercial electric car's power train model and EV dynamics are used in the simulation environment to test the modeled battery pack over various drive cycles. The performance analysis is upscaled by considering different aspects like resistive forces while driving, ambient temperature, initial conditions while the vehicle starts, etc. The battery manufacturer can verify the battery's performance under different load conditions, with a high degree of similarity to real‐world scenarios. The simulations are conducted on MATLAB/Simulink 2021b software.

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Section: Electric Vehicle Modelingmentioning

confidence: 99%

Modeling and performance analysis of a lithium‐ion battery pack with an electric vehicle power‐train for different drive cycles and highway conditions

Harikrishnan,

Kanakasabapathy

2024

Energy Storage

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“…The power required for an electric vehicle at a constant speed on a flat road is shown for Pm1, the storage capacity that is required to meet the requirement of the power battery for a uniform driving condition as follows 𝐶 𝑚1 = 𝑃 𝑚1 𝑡 𝑚1 3.6𝑉 𝑏𝑎𝑡𝑡 (9) Where Cm1 is the storage capacity of the power battery required for uniform driving, tm1 is the uniform driving time of the electric vehicle, Vbatt is the power battery terminal voltage The power required for an electric vehicle accelerates is shown for Pm2 , the storage capacity that is required to meet the requirement of the power battery for acceleration driving condition as follows 𝐶 𝑚2 = 𝑃 𝑚2 𝑡 𝑚2 3.6𝑉 𝑏𝑎𝑡𝑡 (10) "Optimizing Electric Vehicle Performance, Range and Parameter Estimation through NEDC Urban and Suburban Analysis using MATLAB"…”

Section: Storage Capacity Of the Power Batterymentioning

confidence: 99%

“…These experiments provide valuable data that refines our understanding of EV driving range and performance under varying scenarios, contributing to the reliability of our parameter estimation techniques.Furthermore, we highlight the role of predictive modeling, data analytics, and advanced technologies in the realm of real-time parameter estimation [8]. These tools hold the potential to offer precise and convenient range predictions for EV users, thereby enhancing user confidence and facilitating a smoother transition to electric mobility [9].As the global shift towards electric mobility continues to gain momentum, understanding the factors that optimize EV performance and range is paramount. This research aspires to contribute to these ongoing efforts, enabling more informed and confident electric vehicle communities and ultimately driving us closer to a greener and more sustainable future [10].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Electric Vehicle Performance, Range and Parameter Estimation through NEDC Urban and Suburban Analysis using MATLAB

Ghorai,

Nayak

2024

ETJ

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This research paper focuses on electric vehicle (EV) driving range and parameter estimation, with an emphasis on analyzing the New European Driving Cycle (NEDC) urban and suburban drive cycles. By examining these drive cycles, we obtain critical data on the moments when an electric vehicle transitions between constant velocity, acceleration, and deceleration phases, which significantly affect power consumption and driving range. We investigate various techniques for parameter estimation, including battery capacity, energy consumption rates, and powertrain efficiency, essential for improving EV performance and providing realistic range expectations. Empirical experiments in diverse driving conditions contribute valuable data to refine our understanding of EV driving range and performance in different scenarios. The research underscores the role of predictive modeling, data analytics, and advanced technologies in real-time parameter estimation, offering precise and convenient range predictions to enhance user confidence.

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“…The vehicle's propulsion system produces a tractive force (FTR). where it must outweigh the accelerating force (Fa) and the road load force (FRL) then, the vehicle forward at the appropriate speed [12] [14].…”

Section: Modeling and Design Of An Evmentioning

confidence: 99%

“…Fig. (12) shows the motor characteristic curves at a turnon angle of 45⁰: motor speed, phase current, developed motor tor,que and the average power. The maximum torque and current ripples range from 75:270 (N.m) and 0:115 (A).…”

Section: Case1: Rotor Position At Different Turn-on Angles and Step L...mentioning

confidence: 99%

Improved PI-Controller Of Switched Reluctance Motor For Torque Ripple Minimization In Electric Vehicle Applications

azazy

Abdel-Kader

alsherif

et al. 2023

Engineering Research Journal - Faculty of Engineering (Shoubra)

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This paper presents an improved PI-controller for minimizing the ripple torque of switched reluctance motor (SRM) drives in electric vehicle applications. The motor speed is controlled by an optimized proportional-integral PI-controller in the outer loop, and the motor torque is controlled by an additional optimized PI-controller with a hysteresis controller in the inner loop. The controller gains are obtained based on a Harris Hawk's Optimization (HHO) Because of the nonlinear characteristics of the SRM. The objective function is to minimize the integration of the absolute error (IES). A 6/4 SRM is used to investigate the performance of the motor with the proposed controller using Matlab/Simulink toolbox. According to the simulation results, the suggested controller achieves to acceptable ripple torque and achieves the target speed.

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Electric Vehicle Designing, Modelling and Simulation

Cited by 17 publications

References 6 publications

Modeling and performance analysis of a lithium‐ion battery pack with an electric vehicle power‐train for different drive cycles and highway conditions

Modeling and performance analysis of a lithium‐ion battery pack with an electric vehicle power‐train for different drive cycles and highway conditions

Optimizing Electric Vehicle Performance, Range and Parameter Estimation through NEDC Urban and Suburban Analysis using MATLAB

Improved PI-Controller Of Switched Reluctance Motor For Torque Ripple Minimization In Electric Vehicle Applications

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