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This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.
This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.
The study examines the use of computer vision technologies into intelligent electric vehicle (EV) charging infrastructure. The objective is to increase station capabilities, maximize resource usage, and enhance user experiences. An examination of the data from charging stations indicates that there are differences in their capacities and capabilities. Certain stations can handle a greater number of cars at the same time because they have higher power outputs and numerous charging connections. The vehicle identification data illustrates the efficacy of computer vision in precisely recognizing various electric vehicle types, hence optimizing authentication procedures for efficient charging. An analysis of charging session data reveals variations in energy use and durations across sessions, underscoring the impact of charging practices on the utilization of charging stations. An examination of use reveals discrepancies in the number of sessions and energy usage among stations, highlighting the need for adaptive infrastructure. Percentage change study management solutions for demonstrates the fluctuating patterns of resource usage, emphasizing the need for flexible resource allocation techniques. The results emphasize the significant impact that computer vision may have on improving the efficiency and flexibility of electric vehicle charging infrastructure. The research highlights the significance of optimizing the allocation of resources, improving algorithms for various contexts, and applying adaptive solutions for optimal management of charging infrastructure. In essence, the study helps to further our knowledge of how computer vision contributes to the development of intelligent EV charging systems. It provides valuable insights into improving the efficiency of infrastructure and enriching user experiences in the field of electric mobility.
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