Use cases

Use case 1: Well tested and robust analytical tools and insights for optimal placement and roll out of charging infrastructure for heavy-duty trucks for a supply chain or retail customer

Use Case 1 Summary:

Heavy-duty vehicles account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO2 emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions, but they require charging infrastructure that supports long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. This deliverable reports a case study to estimate the charging infrastructure for BETs in long-haul operations in Europe by the year 2030. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operations (defined as travel times over 4.5 hours or distances over 360 km) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km square areas, where each square can include multiple charging stations and parking lots with multiple charging points) needs to have four to five times more overnight (CCS) than megawatt (MCS) charging points: We estimate that about 40,000 CCS (50-100 kW) and about 9,000 MCS (0.7 – 1.2 MW) points are required to support a BET share of long-haul operations of 15%. On average, eight CCS and two  MCS chargers are required per charging area. On average each CCS serves two, and MCS 11 BETs daily, respectively. The daily electricity demand for public charging of BET in each charging area would be around 110 GWh. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.

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Use Case 2: Innovative City Logistics mitigating the noise, pollution and CO2 emissions

The Innovative City Logistics Use Case is using digital tools to support the adoption of future logistics technologies. One of the aims is to estimate the Total Cost of Ownership (TCO) of the emerging delivery modes: delivery robots, drones, e-bicycles and e-vans in suburban setting (Helsinki suburb of Laajasalo) and urban settings (an area in Prague district No. 6). The use cases were developed using digital tools to support the adoption of future freight and logistics scenarios with zero-emission vehicles. Drones were not used in Prague and e-bicycles were not used in Laajasalo, Helsinki. Real operation data were used to estimate the logistics demand in Laajasalo, Helsinki, and a combination of real operation data and synthetic data were used to estimate the logistics demand in Prague. Simulations were run in Matsim for basic optimized scenario replacing an internal combustion engine (ICE) vans with electric vans (e-vans). By implementing full electrification, 10,5 tons of CO2 can be saved annually in Laajasalo, Helsinki and 8,5 tons of CO2 can be saved annually in Prague. The lowest TCO in Laajasalo, Helsinki was for drone operation, however, that was relatively closely followed by delivery robots. As for Prague, e-bicycles had the lowest TCO, again relatively closely followed by the delivery robots. For both locations, the human labor cost was the major component pointing out the potential of autonomous operation. Finally, an experimental augmented reality (AR) visualization of the advanced optimized state scenario was performed for the Prague location to determine the suitability of such visualization technology for decision making.

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Use Case 3: Innovative City Logistics mitigating the noise, pollution and CO

Use Case 3 Summary:

This deliverable describes the demonstration of analysis tool for EU policy to support digitalisation and GHG emissions reduction in the EU logistics industry.

The tool combines qualitative scenario analysis with a numerical simulation of change in the logistics system. The new simulation model MATISSE-LOGISTICS simulates transitions in the logistics system. It assesses the decisions of logistics companies in adopting new logistics systems. It specifically addresses the development and adoption of digital technologies in logistics systems, combined with the reduction of greenhouse gas emissions in logistics systems.

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