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.
Highlights
Testing and implementation of integrated and intelligent transport systems (IITS) of an electrical vehicle need many high-performance and high-precision subsystems
The proposed embedded controller will enhance the following: (i) It will improve the overall performance of the lithium ion battery (ii) It will increase the discharge time and cycle life of the lithium ion battery (iii) It optimizes the battery current according to battery parameters and driving patterns (iv) It increases the range of the electric vehicle
A new concept of monitoring and control of the intelligent transport system has been proposed with the help of the wireless sensor network and artificial intelligence in this paper
Summary
Testing and implementation of integrated and intelligent transport systems (IITS) of an electrical vehicle need many high-performance and high-precision subsystems. The above issues are the critical barriers to the penetration of EVs with a smart grid All the electric vehicles consist of battery units, and every unit has separate estimations of cell voltages and temperatures and state of charge Acquiring such estimations physically would require obtrusive, enormous, and complex circuits. A machine learning-based embedded controller is proposed This controller screens distinctive driving procedures, energy utilization versus load conditions, the energy required for existing and important routes for future use, and battery reenergized profiles. This paper describes the design and implementation of a data acquisition system for vehicles, with special emphasis on EVs
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