Optimizing Combustion Efficiency in Cloud-Connected Smart Gasoline Engines using Gradient Boosting Machines

This research proposes using cloud-connected technologies and Gradient Boosting Machines (GBM) to improve smart Gasoline combustion efficiency. It uses real-time cloud data feeds to inform GBM decision-making to optimize fuel utilization and reduce emissions. A prediction model that adjusts to dynamic engine conditions was created using previous data and constant sensor inputs. It uses ensemble learning to optimize combustion parameters like spark timing and air-fuel ratio using GBM, improving engine performance. The experimental setup uses a cloud-connected smart petrol engine prototype with enhanced sensors and actuators. The engine and centralized computing system communicate seamlessly via the cloud architecture, allowing quick data analysis and model changes. Iterative model refinement and real-time modifications allow the system to adapt to changing operating conditions and maximize combustion efficiency. Compared to standard engine management systems, thorough calculations and practical testing reveal considerable fuel efficiency and pollution reductions. The cloud-connected GBM technique handles uncertainty and unexpected operating situations well, demonstrating its promise for smart engine systems. It advances intelligent engine control systems by using cloud connection and machine learning (ML) to optimize petrol engine combustion efficiency. The discoveries may help the car sector create environmentally friendly and high-performance engine solutions.