Optimization and Design of Hybrid Powertrain Architectures for Next-Generation Automobile Systems

Abstract

The drive for lower emissions, improved fuel economy, and high performance has propelled hybrid powertrain systems to the forefront of automotive research. This paper examines the optimization and design of hybrid powertrain architectures using multi-objective frameworks, modeling tools, and advanced optimization techniques—all from pre-2019 research. Core methodologies include exhaustive clutch-topology search in multi-planetary-gear (PG) power-split systems, genetic algorithm-based optimization of series hybrids, bi-level topology and control optimization, and simulation-based architecture comparison, supported by platforms such as Autonomie. We propose a generalizable methodology: (1) model candidate architectures (series, parallel, power-split, multi-mode); (2) employ component sizing and energy-management control optimization; (3) apply multi-objective optimization algorithms (e.g., NSGA-II, Pareto front selection); (4) evaluate performance metrics like fuel economy, acceleration, cost, and component count. Case studies reveal that optimized three-PG systems can outperform benchmark designs with fewer clutches; genetic algorithms enable holistic sizing and control co-optimization; and Autonomie permits rapid architecture evaluation across drive cycles. Multi-objective strategies allow trade-off exploration between fuel efficiency and performance. Advantages include systematic architecture exploration, performance transparency, and reduced prototyping cost. Challenges remain in computational demand, model fidelity, and real-world validation. We conclude that combining topology, component, and control optimization offers powerful pathways for hybrid powertrain innovation. Future work should integrate battery health modeling, real-time model predictive control, and digital twin frameworks for dynamic adaptation.