Integrated Machine Learning Framework for Real-Time Hybrid Engine Optimization and Performance Prediction

Abstract: This paper presents a novel machine learning framework for optimizing hybrid engine performance across multiple competing objectives. We develop a comprehensive computational approach that combines physics-based modeling with advanced machine learning techniques to simultaneously optimize fuel efficiency, power output, and emissions in hybrid powertrains. The study employs a synthetic dataset representing 1,000 operating points with eight input parameters and three target variables. Our comparative analysis of Random Forest, Gradient Boosting, and Neural Network models reveals distinct performance patterns across the three target variables. Notably, all models achieved excellent performance in predicting fuel efficiency (R² > 0.98), with Gradient Boosting demonstrating superior overall performance across all metrics. For engine power prediction, Gradient Boosting again outperformed other models (R² ≈ 0.95), while Neural Networks showed significantly higher error rates. In emissions prediction, all models demonstrated lower accuracy (R² between 0.7-0.8), with Gradient Boosting maintaining a slight edge. Feature importance analysis identifies the most significant parameters affecting hybrid system performance, enabling us to establish a Pareto-optimal frontier of operating configurations through multi-objective optimization. Our approach visualizes the complex parameter space through interactive 3D representations, facilitating deeper understanding of the trade-offs between efficiency, power, and environmental impact. The proposed framework has potential applications in real-time hybrid engine control systems and can reduce development time in powertrain design, providing a foundation for similar multi-objective optimization problems in automotive engineering