Dynamic Modelling and Current Control for Switched Reluctance Motor Drives in Electric Vehicles

Ibrahim, Muhammed A; Alsammak, Ahmed Nasser B

doi:10.1088/1757-899x/1295/1/012006

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Moreover, it is crucial that the power required for propulsion must be analyzed under real-word operating conditions to evaluate EV performance and optimize the characteristics of the electric motor (EM), battery, controller, and converter. The actual operation of the vehicle is highly unpredictable, influenced by factors such as road conditions, energy consumption (EC), temperature, road grade, and driving behavior [4,5]. Therefore, standardized driving cycles (DCs) mandated by legislation are employed for EC assessments of EVs.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Electric Vehicle Performance Characteristics for Adaptive Supervisory Self-Learning-Based SR Motor Energy Management Controller under Real-Time Driving Conditions

Saiteja,

Ashok,

Upadhyay

2024

Vehicles

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The performance of an electric vehicle (EV) notably depends on an energy management controller. This study developed several energy management controllers (EMCs) to optimize the efficiency of EVs in real-time driving conditions. Also, this study employed an innovative methodology to create EMCs, efficiency maps, and real-time driving cycles under actual driving conditions. The various EMCs such as PID, intelligent, hybrid, and supervisory controllers are designed using MATLAB/Simulink and examined under real-time conditions. In this instance, a mathematical model of an EV with a switched reluctance (SR) motor is developed to optimize energy consumption using different energy management controllers. Further, an inventive experimental approach is employed to generate efficiency maps for the SR motor and above-mentioned controllers. Then, the generated efficiency maps are integrated into a model-in-loop (MIL)-based EV test platform to analyze the performance under real-time conditions. Additionally, to verify EV model, a real-time driving cycle (DC) has been developed, encompassing various road conditions such as highway, urban, and rural. Subsequently, the developed models are included into an MIL-based EV test platform to optimize the performance of the electric motor and battery consumption in real-time conditions. The results indicate that the proposed supervisory controller (59.1%) has a lower EOT SOC drop compared to the PID (3.6%), intelligent (21.5%), and hybrid (44.9%) controllers. Also, the suggested controller achieves minimal energy consumption (44.67 Wh/km) and enhances energy recovery (−58.28 Wh) under different real-time conditions. Therefore, it will enhance the driving range and battery discharge characteristics of EVs across various real-time driving conditions.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Electric Vehicle Performance Characteristics for Adaptive Supervisory Self-Learning-Based SR Motor Energy Management Controller under Real-Time Driving Conditions

Saiteja,

Ashok,

Upadhyay

2024

Vehicles

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Comparison of SoC in Ni-MH and Lithium-Ion Battery for E-Vehicle

Alluraiah,

Nandagopal,

Veeramanikandan

et al. 2024

IJEER

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The current energy side of the battery is indicating in a percentage level is called state of charge (SOC). Nickel-metal hydride and lithium-ion batteries are a type of rechargeable battery. The chemical response at the positive electrode in nickel-metal hydride (NiMH) batteries is like that in nickel-cadmium (NiCd) batteries, as both use nickel oxide hydroxide NiO(OH). However, while NiCd cells use cadmium, NiMH batteries feature a hydrogen-absorbing alloy in their negative electrodes. NiMH batteries provide two to three times the capacity and a significantly higher energy density compared to NiCd batteries of the same size. The research was mainly focused on the aspect of SoC of Ni-MH and Lion batteries with operation of an electric vehicle with total weights of 600 kg was investigated using MATLAB/Simulink.

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Optimizing Solar PV Array Reconfiguration for Maximum Power Extraction Using Hippopotamus Algorithm under Partial Shading

Sai,

Kumar

2024

IJEER

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This research paper presents an innovative approach to maximizing power extraction from solar photovoltaic (PV) arrays under partial shading conditions by employing the Hippopotamus Optimization Algorithm (HOA). Partial shading is a common issue that significantly reduces the efficiency of PV systems by creating multiple local maxima on the P-V curve, thereby challenging conventional Maximum Power Point Tracking (MPPT) methods. To address this, we propose an adaptive reconfiguration strategy for the PV array, optimized using HOA, which successfully moderates the impacts of shading and enhances overall energy yield. The Hippopotamus Optimization Algorithm, inspired by the foraging behavior of hippopotamuses, is utilized for its robust global search capabilities and fast convergence. The algorithm dynamically adjusts the arrangement of the PV module to locate and maintain operation at the global maximum power point. Our methodology involves simulating various shading scenarios and evaluating the performance of HOA-based reconfiguration against traditional MPPT techniques. Simulation results demonstrate a significant improvement in power extraction efficiency, with the HOA-based reconfiguration strategy consistently achieving higher power output compared to conventional methods. Additionally, the proposed system exhibits enhanced adaptability to changing shading patterns, ensuring reliable performance in diverse environmental conditions. The findings highlight the potential of the Hippopotamus Optimization Algorithm as a powerful tool for optimizing PV schemes, particularly in scenarios where shading is inevitable. This study contributes to the advancement of renewable energy technologies by offering a novel solution for improving the efficiency and reliability of solar PV arrays.

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Dynamic Modelling and Current Control for Switched Reluctance Motor Drives in Electric Vehicles

Cited by 3 publications

References 15 publications

Evaluation of Electric Vehicle Performance Characteristics for Adaptive Supervisory Self-Learning-Based SR Motor Energy Management Controller under Real-Time Driving Conditions

Evaluation of Electric Vehicle Performance Characteristics for Adaptive Supervisory Self-Learning-Based SR Motor Energy Management Controller under Real-Time Driving Conditions

Comparison of SoC in Ni-MH and Lithium-Ion Battery for E-Vehicle

Optimizing Solar PV Array Reconfiguration for Maximum Power Extraction Using Hippopotamus Algorithm under Partial Shading

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