IoT based energy management strategy for hybrid electric storage system in EV using SAGAN-COA approach

An effective way to increase the lifespan of electric vehicles'(EVs) batteries is through hybrid electric storage systems, (HESS). The strength and variation of the charging and discharging power profiles impact on battery longevity. Unexpectedly large power demands during vehicle operation might degrade batteries; these needs should be met by the super-capacitor (SC) in the HESS. This paper proposes anInternet of Things (IOT) based energy management strategy (EMS) for HESS in EV. The proposed hybrid method is the joint execution of both the Self-Attention Generative Adversarial Networks (SAGAN) and Coati Optimization Algorithm (COA). For this reason, SAGAN-COA is its name. The proposed SAGAN technique is utilized to predict the power demand of the system and the COA technique is used to optimize the weight parameter of the neural network to improve the SAGAN. By then, the MATLAB working platform has incorporated the proposed strategy, and the current process is utilized to determine the execution. The proposed technique shows superior outcomes than all existing methods like Convolutional Neural Networks (CNN), Genetic Algorithm (GA), and Seagull Optimization Algorithm (SOA).The existing method shows the battery peak discharging power of 23.98 kW, 24.07 kW, 27.11 kW and the proposed method shows the battery peak discharging power of 17.01 kW. The existing technique shows the power variations of 4.58 kW, 5.35 kW and 5.25 kW and the proposed technique shows the power variation of 4.35 kW, which is lower battery peak discharging power and eliminates power variation than other existing techniques.