Machine Learning‐Based Management of Hybrid Energy Storage Systems in e‐Vehicles

Abstract

In transportation systems based on e-vehicles, the energy demand is met with the integration of renewable energy sources while maintaining the voltage profile and mitigating the active and reactive power losses. Vehicle-to-grid optimization technique is used to ensure this integration. Minimum active and reactive power losses are achieved when e-vehicles are integrated with the renewable energy sources in a hybrid mode. A machine learning framework with nested learning is used to ensure optimal methodology to trigger vehicular movement and monitoring of the SoC battery level. When the HEV operates, there is a high possibility for battery degradation, leading to loss of its capacity. To determine the optimal policy, the TD( λ ) learning algorithm is incorporated. This algorithm is known to showcase high performance and a high convergence rate in a non-Markovian environment. The output is simulated to record the readings observed which is aimed at optimizing the total operation cost and reduction in battery replacement. The results show that for shorter drives, the battery replacement cost is more and it is optimally possible to increase the battery life by 21% using the proposed work. Similarly, the recordings indicate that the proposed work shows a significant reduction of about 8%–10% in the operating cost when compared with the RL and rule-based policy.

ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment

“…The CS energy distribution can be even more effective by integrating the above-mentioned forecasting model with the game theory for energy bidding. Then, the authors of [25] highlighted the management of hybrid energy storage systems for EVs.…”