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The transition from combustion engines to electric propulsion is accelerating in every coordinate of the globe. The engineers had strived hard to augment the engine performance for more than eight decades, and a similar challenge had emerged again for electric vehicles. To analyze the performance of the engine, the vector engine operating point (EOP) is defined, which is common industry practice, and the performance vector electric vehicle motor operating point (EVMOP) is not explored in the existing literature. In an analogous sense, electric vehicles are embedded with three primary components, e.g., Battery, Inverter, Motor, and in this article, the EVMOP is defined using the parameters [motor torque, motor speed, motor current]. As a second aspect of this research, deep learning models are developed to predict the EVMOP by mapping the parameters representing the dynamic state of the system in real-time.

The automotive industry widely accepted the launch of electric vehicles in the global market, resulting in the emergence of many new areas, including battery health, inverter design, and motor dynamics. Maintaining the desired thermal stress is required to achieve augmented performance along with the optimal design of these components. The HVAC system controls the coolant and refrigerant fluid pressures to maintain the temperatures of [Battery, Inverter, Motor] in a definite range. However, identifying the prominent factors affecting the thermal stress of electric vehicle components and their effect on temperature variation was not investigated in real-time. Therefore, this article defines the vector electric vehicle thermal operating point (EVTHOP) as the first step with three elements [instantaneous battery temperature, instantaneous inverter temperature, instantaneous stator temperature].

Controller area network (CAN) is used as a legacy protocol for in-vehicle communication. However, it lacks basic security features such as message authentication, integrity, confidentiality, etc., because the sender information in the message is missing. Hence, it is prone to different attacks like spoofing attacks, denial of service attacks, man in the middle and masquerade attacks. Researchers have proposed various techniques to detect and prevent these attacks, which can be split into two classes: (a) MAC-based techniques and (b) intrusion detection-based techniques. Further, intrusion detection systems can be divided into four categories: (i) message parameter- based, (ii) entropy-based, (iii) machine Learning-based and (iv) fingerprinting-based. This paper details state-of- the-art survey of fingerprinting-based intrusion detection techniques. In addition, the advantages and limitations of different fingerprinting-based intrusion detection techniques methods will be discussed.

Technological advances in automotive industry have resulted in an increased number of Electronic Control Units (ECU)s. These ECUs are used for sensing and controlling actuators in the modern vehicles. Various network protocols have been proposed to achieve scalable and reliable communication amonglarge number ECUs in modern vehicles.Various network protocols have been proposed for invehicle communication,such as Controlled Area Network (CAN), Local Interconnected Network (LIN), Media Oriented System Transport (MOST), and FlexRay. This study compares latency and reliability of CAN-Bus and FlexRay communication protocols. The HSC12 microcontroller is used to implement these protocols, and for secure communication data is encrypted.Our experimental results indicate that the CAN-Bus communication protocol is a better option for hard real-time systems and FlexRay protocol is appropriate for deterministic data transmission, e.g., priority-less message communication.