This study presents a novel methodology for optimizing energy management strategies in heavy-duty Fuel Cell Hybrid Electric Vehicles (FCHEVs) with pantograph charging systems. The approach integrates machine learning (ML) techniques to predict energy demand, optimize the power distribution between the battery and fuel cell, and enhance overall efficiency. The methodology involves clustering vehicle and road data, supervised ML classification, and zonification of routes for adaptive energy management. The proposed system was validated using real-world driving data from five different routes in Germany. The results indicate a significant improvement in hydrogen consumption and fuel cell degradation compared to conventional control strategies. This research establishes a framework for advanced energy management in heavy-duty hydrogen-powered electric vehicles.
The increased demand for electricity is inevitable due to transport sector electrification. A major part of this demand is from electric vehicle (EV) charging on a large scale, which is now a growing concern for the grid power distribution system. The lack of insight into grid en…
Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is lar…
Due to developments in vehicle engineering and communication technologies, vehicular networks have become an attractive and feasible solution for the future of electric, autonomous, and connected vehicles. Electric autonomous vehicles will require more data, computing resources,…
Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due to its characteristics, wh…
Electric vehicles (EVs) are becoming increasingly popular, due to their beneficial environmental effects and low operating costs. However, one of the main challenges with EVs is their short battery life. This study presents a comprehensive approach for predicting the Remaining Us…
Intelligent driving research has focused much attention on point cloud obstacles since they are a class of high-dimensional data that can adequately depict the shape and placement of obstacles, unlike picture data. Currently, deep learning technology is primarily employed for veh…