Comprehensive Mathematical Framework for Hybrid Electric Vehicle Powertrain Using Blended Energy Management Techniques

The progressive depletion of petroleum-based energy reserves, coupled with the intensifying threat of global climate change, has catalyzed an urgent global imperative to explore alternative energy sources to conventional fossil fuels. While Electric Vehicles (EVs) offer a promising solution by eliminating tailpipe emissions, their widespread adoption is hindered by limitations such as range anxiety and inadequate charging infrastructure. In contrast, Hybrid Electric Vehicles (HEVs) have emerged as a pragmatic transitional technology, offering enhanced fuel efficiency and significantly reduced emissions by leveraging both internal combustion engines and electric propulsion systems. The operational efficiency, energy consumption, and emission profiles of HEVs are highly dependent on the specific vehicle architecture and its associated control strategy. Before physical prototyping and empirical testing, whether in laboratory conditions; controlled test tracks, or real-world environments; it is imperative to undertake comprehensive simulation-based modeling to validate design choices and optimize performance parameters. The development process of HEVs typically encompasses three hierarchical stages of computational modeling: Model-in-the-Loop (MiL), Software-in-the-Loop (SiL), and Hardware-in-the-Loop (HiL). Within the MiL framework, three principal modeling paradigms are employed: kinematic, quasi-static, and dynamic modeling. These approaches enable the development of accurate virtual digital twins and system behavior prediction under various driving scenarios. Furthermore, the construction of a robust simulation model is foundational for the formulation of an effective energy management strategy (EMS), facilitating optimal power distribution and load balancing between power sources. This study provides a concise review of these modeling methodologies and introduces a novel hybrid approach of integrating both forward and backward simulation techniques for a full parallel P3-type HEV powertrain architecture. The research further presents detailed analyses of fuel consumption metrics and energy management outcomes, underscoring the effectiveness of the proposed modeling and control strategies.