# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 3 Digital Twin Technologies for Electromobility
### Digital Twin and Artificial Intelligence for Autonomous Vehicles  
The recent research on EVs focuses on AVs, also known as self-driving or driverless cars, i.e., vehicles driven without human intervention. Such vehicles are electric since electric propulsion is easier to be autonomously governed. With advanced technology, the vehicle will sense the surrounding environment, plan the route, and drive safely, thanks to AI and ML technology [41]. The AV is still under testing and has not yet become popular globally, but in the coming years, and due to its great benefits, the AV will occupy the global market and vehicle industry. Although the AV has been an active area of research and development in the last few decades, it still faces many challenges in developing a fully safe automated vehicle system. Considering road conditions, traffic conditions, weather conditions, and communication expansion have helped in the growth of the AV system. Car navigation systems assist in controlling and making decisions based on prior knowledge (sensors or road map) that feeds into the system. [PERSON] et al. [42] proposed an efficient approach for vehicle navigation systems based on the velocity optimization paradigm. The maximum speed is adjusted to the curve of the road, and the car follows a smooth path to the lane's center. The approach is integrated into car navigation architecture and was evaluated in two separate simulators before being tested in AV prototypes. The local route and road geometry are required for autonomous driving. Therefore, [PERSON] et al. [43] proposed a hybrid local route generation method. According to the history of the performance and the map availability, the algorithm can precisely choose the best route between the available options. The proposed method was validated and verified in real traffic conditions in an urban area in Korea. In fact, verification and validation are great challenges in AVs for safety assessment. The authors in [44] introduced a systematic review to investigate the current verification and validation software used in AVs. They discussed the simulation environments and more specific approaches, such as mutation testing, fault injection, techniques for cyber-physical systems, adversarial examples, corner cases, and formal methods. [PERSON] et al. [45] developed a framework that integrated the intelligent driving model with human factors, such as driving modes and their reactions and expectations on the road, to enhance autonomous driving performances. The proposed model helps to reinforce the efficiency and safety of AVs. Reinforcement learning (RL) is a widely used ML technique to train the agent on reward and punishment approaches. This approach effectively works with the AV industry as the RL algorithm learns from the driver's actions to increase a certain reward or take a decision. [PERSON] et al. [46] designed a framework for car-following based on video frame processing using RL algorithms. The framework is based on navigation decisions and automated object detection. The proposed model has achieved promising results and acceptable car-following behavior in AVs. Employing RL in the AV industry is innovative and will lead to some benefits. The more information the algorithm processes, the more efficient the algorithm becomes, and the best results can be obtained. Software providers that support the DT concept have begun integrating reinforcement learning into their tools. For example, FlexSim software [47] recently introduced the RL model and the possibility of connecting external systems with the FlexSim models. Such additions will allow the software to include a 3D design tool and a data analysis tool, enhancing the concept by [PERSON] et al. [3] to specify tasks required for a specialized unsupervised prognosis and control platform for the energy system performance estimation of the AV. They developed several test platforms using DT and ML algorithms to autonomously optimize electric propulsion drive systems of self-driving electric vehicles and monitoring sensors.