Assessment of the practical use of e-trucks

During the course of this project, over 530,000 kilometers were driven by e-trucks. In total, almost 490 tons of greenhouse gas emissions have been saved compared to operating these vehicles as diesel trucks. Additionally, further e-trucks are in use as a result of the project. Taking these e-trucks into account, the total emissions savings amount to over 500 tons.

The graphic below shows the total savings in greenhouse gas emissions from operating the e-trucks since the project started in October 2018.

Optimization of the connected load through simulation

As part of the project, a simulation tool was developed. As a result, three scenarios were defined for optimizing the required connected load: 

• full power: The truck is charged with full power until it is fully charged.
• full period: The power is divided until the truck has to start the next trip.
• opt char./fixed assign: The truck is charged with just enough power until enough energy is available for the next trip (to keep the total load at the site for charging the e-truck fleet as low as possible).

In a test of this tool with a simulated fleet of 50 e-trucks the following load profiles of the scenarios and the connected load required for them were determined. Significant saving potentials can be determined by the tool, as the following figure shows:

Energy consumption depending on use cases and season

The energy consumption of an e-truck changes depending on the time of year, as the heating/cooling requirements for the vehicle change due to the change in ambient temperature. The following figure shows the relationship between the outside temperature and the energy consumption of an e-truck.  Consumption is highest at -5°C, then decreases as the temperature rises and does not increase at higher temperatures. 

The figure below shows the energy consumption of different use cases (retail delivery with cooling and beverage delivery without cooling) depending on the seasons. The energy consumption patterns vary for the different use cases. However, generally speaking energy consumption per kilometre is significantly higher during winter.

 Feedback from dispatchers and logistics planners 

Within the practical use of the e-trucks, in addition to the technical parameters, the suitability for the respective logistical applications was recorded. The feedback from the dispatchers and logistics planners as well as the truck drivers are essential. The most important statements and findings are presented below.

Economic prospects and opportunities for e-trucks

The costs of the required components play a decisive role in establishing e-trucks in transport logistics. As part of the project, the cost development of the following technology components for future e-logistics were continuously evaluated:

• Charging infrastructure
• Stationary battery systems
• Battery packs
• e-trucks

The following figure shows a forecast of the cost development of charging infrastructure with different charging capacities in the period 2020-2030 (status 2020).

Development of battery system costs

The development of costs for battery systems also plays a crucial role. A cost forecast including fluctuation ranges up to 2050 can be seen in the figure below (status 2020).

Costs for vehicle batteries and vehicles

As part of the project, the costs for vehicle batteries were also collected and extrapolated until 2050. A considerable cost reduction potential of about 20% per doubling of global battery production is shown here, which could allow the economic operation of e-trucks in the future. The graph presents the development of passenger vehicle battery prices (status 2021):

However, the research project found that e-truck batteries are significantly more expensive due to their different requirement profile and have additional costs of about 274% compared to passenger vehicles. The cost reduction curve is about 3-5 years behind that of passenger vehicle batteries (status 2021):

The most important and largest cost factor is of course the e-trucks themselves. The following chart shows two scenarios for the cost development of e-trucks with a range of 400 km and 800 km respectively until 2030.

Optimization models for the transition to electric fleets

Two essential parameters have a significant impact on the implementation or conversion to e-truck fleets:

• The required energy and power consumption at the charging locations and the required technical framework
• The total costs (TCO - Total Cost of Ownership) of potential e-truck fleets

The following figure shows an exemplary load profile of an investigated logistics HUB. The current power and energy consumption is shown in green. The yellow lines show the power and energy consumption through a 100% conversion of the existing fleet to e-trucks. It was shown here that over 98% of the charging energy could be charged at the HUB.

Finally, the resulting power and energy consumption for this exemplary HUB is shown in gray. 

Total Cost of Ownership (TCO) comparison between diesel trucks and e-trucks 

Based on initial simulation results, a preliminary TCO [€/km] comparison between diesel trucks and e-trucks was determined. The following figure (status 2020) shows the TCO parity between diesel and e-trucks depending on the year of purchase. Each point stands for one truck, the different colours stand for the respective years of purchase. For points below the blue cost parity line, it would be cheaper to buy a new e-truck than a diesel truck.

Conversion tool for e-truck fleets

In the reporting period 03.2020 - 02.2021, a conversion tool was developed to facilitate investment planning for e-truck fleets in terms of battery sizing, infrastructure design and charging strategy. The aim was to significantly reduce the investment costs in a first step by optimising the infrastructure and vehicle configuration. This tool can be used to derive the most important key performance indicators, for example the number of e-trucks required, the number of charging points and the required grid connection power. The following table shows an illustrative result for the conversion of a logistics hub under two charging scenarios: