HORIZON-CL5-2026-05-D5-01 Large-scale demonstration of Heavy-Duty Battery Electric Vehicles towards long-haul logistics operations - Collaboration Offer

The adoption of heavy-duty electric vehicles (HD-BEVs) in logistics fleets and operations is becoming increasingly critical for sustainable transportation. One of the most important challenges in this domain is managing battery health and ensuring optimal charging strategies. These factors are essential for maintaining operational efficiency and reducing downtime.

For this call, Doğuş Teknoloji proposes leveraging advanced digital tools and AI-driven solutions to optimize charging and routing processes. By integrating real-time and historical data—such as current traffic conditions, Google Maps data, vehicle locations, battery state of charge, health status, charging station availability, and past driving and charging patterns—we can develop a system that dynamically recommends the most suitable charging station and route for each vehicle at any given moment. This approach will enable logistics operators to maximize fleet utilization, minimize delays, and support the growing number of HD-BEVs in their operations.

This contribution directly addresses the following expected outcomes of the HORIZON-CL5-2026-05-D5-01 call:

-            Develop and integrate in the demonstrations the use of digital tools with use of AI, access to data sets, operational and governance logistics models including charging opportunities and models to facilitate increasing numbers of HD-BEVs in logistics operations

o   Doğuş Teknoloji’s proposed solution directly addresses this requirement by introducing AI-driven digital tools that optimize charging and routing processes. By leveraging real-time and historical data—such as traffic conditions, vehicle locations, battery state of charge, and charging station availability—the system enables dynamic decision-making. This integration supports operational models that can handle growing HD-BEV fleets efficiently, reducing delays and improving overall logistics performance.