Multi-agent intelligent optimization approach for optimal wireless rapid charging and SOC estimation of hybrid electric vehicle

Abstract

This paper proposes a new Coyote Swarm Multi-Agent Intelligent Optimization (CSMAIO) Algorithm for a real-time battery management strategy through the wireless rapid-charging performance of hybrid electric vehicles (HEVs). Any electric vehicle can be charged by using any one of the modes among wired and wireless charging modes. The wireless charging system, which transfers electricity from a transmitter to a receiver without making contact, is the most important among them. It is obvious that this power varies with speed and is primarily used to recharge the battery. The proposed CSMAIO performance is analyzed with wired, wireless and proposed wireless rapid-charging methods. Comparative analysis has been done for SOC and torque at different speeds in case of wired, wireless and proposed wireless rapid-charging method. The effectiveness of wireless rapid-charging approach using the proposed CSMAIO at various speeds and torque levels has been evaluated by employing MATLAB Simulink and demonstrated that the slowest speed produces the high SOC and best results. High speed reduces the SOC. The SOC has been analyzed at different speeds, and it has been demonstrated that for speed of 10 km/h in case of wired charging method the SOC is 30%, in case of wireless charging method the SOC is 25% and in case of wireless rapid-charging method the SOC is 40% that is highest among all the three methods. In case of wired charging method torque is 450 Nm, and in case of wireless charging method the torque is 470 Nm. The proposed wireless rapid-charging approach is producing high torque of 500 Nm during a particular speed compared to other charging methods such as wired and wireless methodology. Thus, the efficient battery state-of-charge (SOC) performance is preserved, and lower fuel usage is also achieved with high torque by using the proposed CSMAIO approach.