[Retracted] Conceptual Implementation of Artificial Intelligent based E‐Mobility Controller in smart city Environment

TLDR

Integrated intelligent transport systems for electric vehicles require high‑performance subsystems, yet current solutions suffer from limited features, range anxiety, charging delays, and communication problems that hinder smart‑grid penetration. The study proposes a concept of connected EVs using VANET communication, embedded sensor systems, and cloud‑based data analytics to address these limitations. Machine‑learning‑based control systems generate vehicle control information, while the architecture integrates sensors, battery performance optimization, VANET communication, and big‑data analytics to improve discharge time, cycle life, range, and safety. The approach enhances lithium‑ion battery discharge time and cycle life, extends vehicle range, improves battery safety, establishes reliable VANET communication, and enables advanced data analysis.

Abstract

Testing and implementation of integrated and intelligent transport systems (IITS) of an electrical vehicle need many high‐performance and high‐precision subsystems. The existing systems confine themselves with limited features and have driving range anxiety, charging and discharging time issues, and inter‐ and intravehicle communication problems. The above issues are the critical barriers to the penetration of EVs with a smart grid. This paper proposes the concepts which consist of connected vehicles that exploit vehicular ad hoc network (VANET) communication, embedded system integrated with sensors which acquire the static and dynamic parameter of the electrical vehicle, and cloud integration and dig data analytics tools. Vehicle control information is generated based on machine learning‐based control systems. This paper also focuses on improving the overall performance (discharge time and cycle life) of a lithium ion battery, increasing the range of the electric vehicle, enhancing the safety of the battery that acquires the static and dynamic parameter and driving pattern of the electrical vehicle, establishing vehicular ad hoc network (VANET) communication, and handling and analyzing the acquired data with the help of various artificial big data analytics techniques.