Optimized Ensemble Model to Predict the Compatibility of Automobiles With Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) represent a significant advancement in automotive technology, bridging the gap between conventional vehicles and pure electric vehicles by combining two distinct power sources. This integration enhances car efficiency and addresses concerns about high fuel consumption and emissions in traditional vehicles. The transition to hybrid models reduces harmful petrol emissions, contributing positively to environmental sustainability and mitigating the impacts of global warming. Machine learning (ML) algorithms play a crucial role in optimizing and assessing the compatibility of typical automobiles with HEVs. Through rigorous testing and refinement, the proposed model exhibits improved accuracy, elevating performance from 81.76% to 86.68%. The incorporation of W‐Saw score and L‐Saw score metrics in an interdisciplinary research methodology further strengthens the optimized model’s reliability and effectiveness. The model’s robustness is evidenced by the high W‐Saw score of 8.8, indicating its strength, and a low L‐Saw Score of 3.3, highlighting minimal errors. The optimized model’s significant W‐Saw score emphasizes its reliability and contribution to environmental sustainability.