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TopI. Introduction
Excess fuel use in conventional vehicles produces a rapid reduction in the amount of fuel available, resulting in an abrupt climate change and degradation of the environment. In this case, an electric vehicle is the best alternative energy source for transportation because it is a high-quality source that successfully addresses the issues mentioned above. Energy conservation is one of the most challenging impediments to long-distance transportation, and the development of affordable electric vehicles for everyone is one of the most difficult challenges. Energy management systems that is successful in increasing energy efficiency and, as a result, prolonging travel duration for vehicle impacts for battery life savings are being developed and implemented in this research. When it comes to energy savings, wireless vehicles waste a significant amount of energy from the capacitor, which is a significant downside for electric vehicle transportation.
Many articles have recently presented energy optimization strategies for electric vehicles based on an optimized ECMS (Equivalent Consumption Mitigation Strategy) (Zhi Sun & Akyildiz, 2010). The vehicle-to-vehicle communication model is simulated to compare the transmission efficiency of the baseline Adaptive ECMS and the enhanced Adaptive ECMS. Different vehicles’ driving patterns are updated to account for changes in the linked vehicle environment by applying equivalent factors to each vehicle (EFs). With each simulation run, the A-ECMS approach gains 1.63% inaccuracy. In addition, the stochastically generated approach cuts consumption by 13% and has a qualified effect on electric batteries (Aminit et al., 2007) & (Zhong et al., 2008). When the modern hybrid electric vehicle was modeled using Markova chain policy, which improves real-time control policy and results in an improvement of 6% in optimal fuel utilization, the results were impressive. The discharge energy of the battery is stable because the e-vehicle does not restrict connected power, allowing the battery to have a long service life (Sirkeci-Mergen & Scaglione, 2005)
In order to provide energy-aware cluster-based, short-range communication (Sendonaris et al., 2003) the IEEE802.15.4 protocol is employed, which activates chips in a sleeping mode after a period of inactivity since the passive signal was received. Cluster-based communication, an effective technique for optimizing transmission through energy-aware protocols, developed further in this suggested system as a follow-on development. The employment of cluster protocols to optimize energy consumption is typically required in dense networks (Kumar et al., 2019). According to the findings of this research, the source vehicle communicates with the destination vehicle or device via short-range communication. It requires more energy than an electric vehicle has data to transmit, and the condenser’s continual power leaking contributes to this rise in energy consumption. This clustering technology intelligently discovers and sends data from surrounding vehicles or devices at a greater data rate to reduce the amount of energy consumed. An electric vehicle can transfer useful information across short distances, and after the transmission is complete, the vehicle goes to sleep to conserve its battery power. Aside from that, when a vehicle requested to send data to a nearby e-vehicle from the cluster, the e-vehicle got wake-up signals, which determined whether the e-vehicles had enough energy to fulfill the request or not.