**9. Conclusions**

This manuscript proposes all of the cutting-edge discoveries related to electric vehicular technology and innovation. In addition to this is the brief study associated with the charging systems and their respective levels. The synergy between the shared autonomous electric vehicles and the current market is also described, and safety issues are addressed as well. This leads us to the conclusion that there is an immediate need to improvise the present advanced driving assistance systems, and this aspect is also covered in this review paper. Current developments in the battery technology and their system interfaces and cutting-edge solid-state battery evolution theory have been presented. Batteries will become more reliable and secure with the aid of this cutting-edge technology, self-healing batteries, and the integration of embedded sensors within the cell. The usage of a digital twin (DT) will allow for higher-reliability powertrain design, while also improving the economics and dependability of EVs. New trends and directions for innovative, practical, and reasonably priced powertrains are thus given.

Regarding autonomous driving systems, drivers will not have to manage such a complex chore any longer, preventing any potential harm when parking a car and reducing traffic congestion and fuel consumption. As was previously indicated, notions in the literature have been formed based on the full adoption of autonomous vehicles, which may not occur very soon. It has not been discussed how an autonomous vehicle should react to a careless driver. Tailgating, driving against traffic, speeding, neglecting to use turn signals, proceeding through red lights without stopping, and failing to surrender the right of way are all examples of reckless driving. Additionally, motorbike and autonomous vehicle interactions have not been taken into account by researchers, who have instead concentrated on four-wheel-drive interactions. It is difficult to determine how an autonomous vehicle should deal with a mode of transportation where there is a substantial danger of fatalities for motorcyclists. Modern technologies now have the ability to analyze driving behavior, which can help to prevent anomalous driving habits. The devices are able to control the lateral motion of an EV during unusual behavior. NVIDIA has established a new paradigm for autonomous driving software with the successful demonstration of neural-networkbased autonomous driving. Autonomous lateral control is self-driving automobiles' major problem. In terms of offering a full software stack for autonomous driving, an end-to-end model appears to be quite promising. This technology is one of several steps toward the realization of self-driving automobiles, even though it is not yet ready to be offered as a feature on the market. The work discussed in the paper [190] focuses on the application of an end-to-end paradigm. With the goal of illuminating deep learning and the software needed for neural network training, the subtleties of building an effective end-to-end model are highlighted. The model showed a 96.62% autonomy for a multilane track, like the one that was employed for training in this research contribution [190]. The model successfully maneuvered the car on single-lane, uncharted tracks 89.02% of the time. The findings show that end-to-end learning and behavioral cloning can be used with artificial intelligence to enable autonomous driving in novel and uncharted environments.

There are several reasons why EVs are attractive, among such reasons being the coordination between carbon footprints and the power grids employing various renewable sources. It is being investigated whether coordinated charging of electric vehicles has the potential to reduce the CO2 emissions associated with their charging by charging only when the grid's carbon intensity (gCO2/kWh) is low and absorbing excess wind generation during periods when it would otherwise be curtailed. A time-coupled linearized optimal

power flow formulation, based on plugging-in periods generated from a sizable travel dataset, is described as a way of scheduling charge events that seeks the lowest carbon intensity of charging while respecting EV and network restrictions [191,192]. Another reason is of course the efficiency of autonomous vehicles that has been increased significantly with the advent of artificial intelligence. Thus, an outline of autonomous vehicles is also included in this manuscript. The key components of an autonomous vehicle that enable data gathering and transmission are sensors. An improved system for lane keeping, lane change, and obstacle recognition is made possible by this information. However, various sensors have a number of limitations. Techniques for image processing could reduce costs; however, they are susceptible to climatic and environmental factors. Therefore, more work is required to either increase the reliability of inexpensive sensors or lower the cost of high-reliability sensors for mass production. In addition, the areas of research needed for autonomous intelligent vehicles were also noted. With the advent of technology day by day, road safety measures have been increased and still are top concerns while designing any advanced driver assistance product. The common issues on roads associated with society directly affect the driverless vehicles and their concept. This is the main reason that there is still no legislation on it. The future concern must be in this regard to set a main focus over the major penetration of autonomous shared electric vehicle and their co-existence with normal vehicles on the same roads.

**Author Contributions:** G.E.M.A. performed writing, studied the literature, and compiled all facts. N.E.O., M.A.M., S.A.B.M.Z. and V.S.A. reviewed the entire paper. K.K., V.S.A. and N.E.O. contributed the information about wide bandgap (WBG) power electronics and charging technology and drafted some of the sections on the battery technology. G.E.M.A., K.K., S.A.B.M.Z. and V.S.A. also wrote information on autonomous electric vehicles and shared autonomous electric vehicle and Li-Fi technology. All authors have equally contributed to this research review article. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Data sharing not applicable.

**Acknowledgments:** We thank the Centre for Automotive Research and Electric Mobility (CAREM) Universiti Teknologi PETRONAS, Malaysia, for supplying all the necessary data and resources for this project. Moreover, we also acknowledge the support of the co-authors from the Laboratory of Mechanical, Computer, Electronics and Telecommunications, Faculty of Sciences and Technology, Hassan First University, Morocco, and the Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**


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