Modelling of an Electric Vehicle for Tractive Force Calculation Along With Factors Affecting the Total Tractive Power and Energy Demand

Saleem, Adeel; Liu, Nain; Hu, Junjie; Iqbal, Atif; Hayyat, Muhammad Aftab; Mateen, Muhammad

doi:10.1109/icomet48670.2020.9073845

Cited by 16 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, the two-axis axial reference frame is transformed into three-phase references for the observed motor currents. This transformation is known as the α-β to a-b-c transformation [15]. cos sin cos sin…”

Section: Proposed Methodsmentioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

View full text Add to dashboard Cite

This study presents a novel approach to enhance the speed control performance of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicles (EVs) through the implementation of a Genetic Algorithm (GA)-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFOPID) controller. PMSM technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in EV drive systems. However, the inherent non-linearity, dynamics, and uncertainties of PMSMs pose significant control challenges. The proposed GA-AFFOPID controller, tuned using a genetic algorithm, exhibits superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances. Comparative analysis with traditional control methods demonstrates the exceptional performance of the GA-AFFOPID controller, achieving a 1.796% lower overshoot, 0.97% faster rise time, 4.25% lower steady-state error, and 0.35% faster settling time compared to the adaptive fuzzy fractional order PID controller. These results highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications, paving the way for improved energy efficiency and overall performance of electric vehicle propulsion systems.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The external longitudinal forces acting on the moving vehicle are acceleration forces, 𝐹 , rolling resistance force, 𝐹 , aerodynamic drag force, 𝐹 , and hill-climbing force, 𝐹 , which resists the motion of the vehicle if going uphill and helps motion when going downhill. The dynamic model of a moving tracked electric vehicle with mass, 𝑚, can be written as [35]:…”

Section: Dynamic Equations Of Motionmentioning

confidence: 99%

Enhanced Energy Efficiency through Path Planning for Off-Road Missions of Unmanned Tracked Electric Vehicle

İnal,

Cansever,

Yalçın

et al. 2024

Vehicles

View full text Add to dashboard Cite

The primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle’s operational range by 7.45%.

show abstract

“…The battery has a nominal voltage of 360 V and a capacity of 24 kWh [13]. It consists of 48 modules and 4 cells (2 in parallel, 2 in series), amounting to 192 cells [14]. The total voltage of the battery pack is approximately 403.2 V. The possible arrangement of the cells in a battery pack has been studied in general by Emadi [22].…”

Section: Nissan Leaf Batterymentioning

confidence: 99%

Electric Vehicle Modelling and Simulation of a Light Commercial Vehicle Using PMSM Propulsion

Babangida¹,

Szemes²

2021

Hung. J. Ind. Chem.

View full text Add to dashboard Cite

Even though the Internal Combustion Engine (ICE) used in conventional vehicles is one of the major causes of global warming and air pollution, the emission of toxic gases is also harmful to living organisms. Electric propulsion has been developed in modern electric vehicles to replace the ICE.The aim of this research is to use both the Simulink and Simscape toolboxes in MATLAB to model the dynamics of a light commercial vehicle powered by electric propulsion. This research focuses on a Volkswagen Crafter with a diesel propulsion engine manufactured in 2020. A rear-wheel driven electric powertrain based on a Permanent Magnet Synchronous Motor was designed to replace its front-wheel driven diesel engine in an urban environment at low average speeds.In this research, a Nissan Leaf battery with a nominal voltage of 360 V and a capacity of 24 kWh was modelled to serve as the energy source of the electric drivetrain. The New European Driving Cycle was used in this research to evaluate the electric propulsion. Another test input such as a speed ramp was also used to test the vehicle under different road conditions. A Proportional Integral controller was applied to control the speed of both the vehicle and synchronous motor. Different driving cycles were used to test the vehicle. The vehicle demonstrated a good tracking capability in each type of test. In addition, this research determined that the fuel economy of electric vehicles is approximately 19% better than that of conventional vehicles.

show abstract

Modelling of an Electric Vehicle for Tractive Force Calculation Along With Factors Affecting the Total Tractive Power and Energy Demand

Cited by 16 publications

References 10 publications

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Enhanced Energy Efficiency through Path Planning for Off-Road Missions of Unmanned Tracked Electric Vehicle

Electric Vehicle Modelling and Simulation of a Light Commercial Vehicle Using PMSM Propulsion

Contact Info

Product

Resources

About