IoT-Based Smart Energy Management in Hybrid Electric Vehicle Using Driving Pattern

Driving patterns involve multiple starts, stops, and goes, requiring both average and momentary demands on power. Batteries function better for the average power demand. An ultracapacitor is used as an auxiliary system with a longer life cycle and higher power density for the vehicle's momentary power needs. The challenging part of hybridization is smart energy management among the sources. An energy management control strategy decides the energy share among the ESSs. Most of the researchers used standard driving patterns for analyzing vehicle performance. However, a real-time driving pattern is needed to investigate the vehicle's performance. An IoT-based smart decision-making module can better regulate the energy across the multisource based on sensor unit data about vehicle driving speed. The experimental findings illustrate the effect of the suggested smart energy management control on the hybridization of ESSs to split the power among ESSs to meet the demand for power, maintain the charges in ESSs, and extend the lifespan of the battery. Based on the calculations, the LM approach has a minimal MAD of 0.046781 for training data and 0.043859 for testing data. It has been said that the LM algorithm has the lowest MSE, with values of 0.006397 and 0.005781 for training and testing, respectively. Even in RMSE, the training and testing values of 0.079355 and 0.075893 for the LM algorithm are the lowest. When the LM algorithm is being trained, its MAPE score is 0.022489, but when it is being tested, it is 0.004846.