Adaptive Energy Management by Ambient Intelligence Techniques

Abstract

The transportation industry is grappling with a significant surge in energy consumption, prompting a growing emphasis on sustainable and eco-friendly modes of travel. While batteries serve as a primary energy source for clean transportation, challenges arise from diverse driving profiles (such as NYCC, Artemis Urban, WLTP class-1) and increased C-rates, impacting battery performance and lifespan in Battery Electric Vehicles (BEVs). This underscores the importance of Hybrid-Source Electric Vehicles (HSEVs) as an effective solution. In the proposed Hybrid Electric Three-Wheeler (3W), supplementary power from supercapacitors (SC) and photovoltaic panels (PVs) complements the battery. However, addressing variations in power demands necessitates robust Energy Management Strategies (EMS). Introducing an innovative Adaptive Intelligent Hybrid Source Energy Management Strategy (IHSEMS) becomes crucial. IHSEMS optimizes power sources through an absolute energy-sharing algorithm, meeting motor power demands via a Fuzzy Logic Controller. A Techno-Economic Assessment validates the effectiveness of IHSEMS, revealing a 50.20% reduction in peak battery power compared to BEVs. Moreover, IHSEMS minimizes battery capacity loss by 48.1%, 44%, and 24%, respectively, and reduces total operational costs by 60%, 43.9%, and 23.68% when compared to standard BEVs, State Machine Control (SMC), and Frequency Decoupling Strategy (FDS).