The Paris Climate Agreement requires drastic reductions of carbon dioxide emissions for industry and private sectors [1
]. The transportation sector is responsible for 27% of European greenhouse gas emissions (GHG), of which road transportation accounts for 71.7%. Despite international agreements, emissions from road transportation keep increasing, due to increasing transportation demand [2
]. For example, the German Ministry of Transport predicts an increase in road transportation of 30% by the year 2030 [3
]. Furthermore, globalization leads to stronger connected markets and consequently a higher transportation demand. Additionally, e-commerce has been growing rapidly, and is expected to further increase road transportation [4
]. Long-distance or heavy-duty trucks handle the largest share (68.2%) of transport performance [5
]. It is evident that heavy-duty trucks will remain an integral part of transportation in the future. Due to their high share (50%) of (fossil) energy consumption, vehicles with gross weights of more than 15 tons each have substantial leverage over lower anthropogenic GHG [7
]. Therefore, the European Union tightened limits for
emissions for passenger cars in 2018 and expanded these limits to include light and heavy-duty vehicles in 2019 [8
The reduction of emissions presents new challenges for the transportation and commercial vehicle sector. Therefore, the current trend of e-mobility for passenger cars continues in the commercial vehicle sector. To reduce emissions and avoid fines, original equipment manufacturers (OEMs) are exploring new powertrain technologies. In addition to battery electric vehicles (BEV), hybrid electric vehicles (HEV) offer energy and thus savings.
BEV have zero tailpipe (tank-to-wheel) emissions but require electric energy for charging. Thus, a holistic assessment must consider the production and transformation of electricity (well-to-tank). In addition to their energy supply, BEV components and their respective production processes change. The energy-intense production of lithium-ion batteries in particular has been the focus of recent discussions [10
]. Commercial vehicles require up to ten times larger batteries than passenger cars, rendering this topic even more important [13
Following the vehicle concept definition in Table 1
, configuration parameters for further analysis are defined. Table 6
summarizes the configuration parameters of the reference diesel vehicle, two battery electric vehicles (BEV1 and BEV2), one hybrid vehicle (HEV) and one plug-in hybrid vehicle (PHEV) respectively. The two BEVs have differently sized batteries in order to represent a shorter range of 450 km (BEV1) and a long range of approximately 600 km (BEV2). Mährle et al. (2017) collected data of ten vehicles used for long-haul transportation [68
]. They showed that on average, the vehicles traveled 400 km to 600 km per day. Consequently, the analyzed vehicles represent the lower and upper boundaries of the collected vehicle data. The HEV configuration is based on the results of Fries et al., who used an evolutionary algorithm to optimize costs and transport efficiency of a vehicle for long-haul applications [58
]. The same approached was used to obtain the PHEV configuration [20
]. Fries et al. limited the electric-only speed of both hybrid vehicles to 50 km/h, resulting in low electrical torque and power requirements [59
]. All concepts meet the required ranges and are therefore suitable for long-haul transportation. The mass models are implemented in the LOTUS simulation model [54
4.1. Inventory Analysis
shows the material mix of the concepts and highlights the change in material mix. The diesel and two hybrid concepts only show a small deviation regarding used materials. Steel and iron—used for the frame and ICE—are the most prominent materials. The total weight of the hybrid vehicles is 3% to 8% higher and thus comparable to the diesel vehicle: Down-sizing the internal combustion engine saves 229 kg to 310 kg and compensates the additional weight of the electrical components. This is in line with the findings of Fries et al. (2017), who observed net weight savings due to engine downsizing of approximately 220
]. Rupp et al. results confirm the additional weight of approximately 500 kg of the HEV [42
Due to the battery, the material mix of both battery electric vehicles is significantly different to that of the vehicles equipped with combustion engines. On a component scale, the differences in the material mix become evident. The mass of steel is mainly located in the glider, and consequently almost constant among the vehicles (relative standard deviation: 3.78%). Due to the omission of the combustion engine, the proportion of iron materials in the BEV is reduced by half, with the remaining iron being due to the suspension and transmission (431 ). The mass of aluminum increases the mass of the materials most of all. Compared to the ICE vehicle (197 ), the two BEV concepts show an increase of aluminum by a factor of 7–10 or 1525 kg and 2153 respectively. The increase is mainly due to the lithium-ion battery pack which is responsible for 82.3–87.5% of the BEV aluminum. With a total weight of 5265 kg and 7897 , the battery comprises 46–56% of the total tractor weight. This results in a net payload loss of the BEV of 3.998 t and 6.630 t compared to a conventional ICE vehicle.
4.2. Impact Assessment
Greenhouse gas emissions, foremost
emissions, are the main driver of global warming and climate change [70
]. Global primary energy consumption is largely responsible for rising
emissions. A consideration of the primary energy is therefore an important component in order to replace fossil energy sources with renewable ones [71
]. Because of this, we assess the environmental impact using primary energy, and global warming potential as an example of environmental impact. Other impacts categories, such as acidification or land use, are not regarded in this study but can be modeled with the presented LCI and any LCA software. It must be noted that primary energy demand includes the efficiency of electricity production. For this study we model the average 2019 European energy mix with GaBi
, resulting in 336 gCO2
] p. 93) and an average efficiency of 31%. The chassis is the main contributor of the vehicle glider, with approximately one third of the energy demand. Figure 7
shows the total cradle-to-gate primary energy demand for each vehicle concept divided into components. The strong increase in energy demand by a factor of 5 to 7 of the BEV compared to ICEV is mainly due to the lithium-ion battery, accounting for 84–89% of the total energy.
The global warming potential with a timescope of 100 years is determined following the recommendations of the International Reference Life Cycle Data System (ILCD) and obtained directly from GaBi
]. Thus, the results represent the midpoint (i.e., emissions released into the atmosphere) level and therefore do not directly indicate specific damage caused to the environment or human health, which would be the case for endpoint level. As Figure 8
shows, GWP develops analogously to primary energy demand. However, the increase caused by the lithium-ion battery by a factor of 4 to 5 is lower compared to the primary energy demand.
The results show that regardless of the component, the electricity consumption during production is the main contributor to primary energy demand. In the case of the lithium-ion battery, 85% of the GWP is due to electricity consumption. Under the Kyoto Protocol, these emissions are categorized as scope 2 [74
]. Scope 1 emissions, or direct air emissions, are of minor influence with regard to GWP.
5. Discussion and Conclusions
In order to perform an environmental impact assessment in the early concept development phase, it is necessary to estimate material composition and primary energy demand. While use phase or tank-to-wheel emissions are adequately represented by longitudinal dynamic simulations, there is a lack of data regarding the preceding processes. The presented life-cycle inventory provides generic mass compositions and primary energy demands for the assembly and manufacturing steps of a European heavy-duty tractor. The inventory is scalable and component based, so that different vehicle and powertrain concepts can be modeled. An OEM can apply the life-cycle inventory in the early concept phase to assess different vehicle concepts and powertrain configurations by scaling the components provided in the LCI. This way, vehicle KPIs such as maximum torque or battery capacity can easily be altered to optimize the concept vehicle. If the user has detailed information on particular materials, processes or components, for example, from an existing LCA model, those data can complement the generic LCI.
To set the GWP into context, we estimate the use-phase for an ICE and a BEV: On average, an European diesel trucks emits 850 tCO2
to 1161 tCO2
during their use-phase (tank-to-wheel) [6
]. Assuming a consumption of
kWh/km for the BEV and the European energy mix, the BEV emits 448 tCO2
to 700 tCO2
(well-to-wheel)—approximately half of the diesel truck—in the same time. This means that the battery production alone amounts to 13% to 30% of the well-to-wheel emissions, which highlights the importance of the battery production. Lithium-ion batteries in particular must be considered as critical components, rendering the other components almost obsolete regarding environmental impact. If the efficiency of the electricity production is neglected, the net process energy of the battery pack amounts to 470 kWh/kWhcapacity
. Emilsson and Dahllhöf [34
] estimate the net process energy with 313 kWh/kWhcapacity
. On the one hand, this deviation of 50 % can be explained with different background datasets. On the other hand, Emilsson and Dahllhöf point out that older results from their 2017 report estimated process heat and electricity for cell production on pack level approximately 30% to 60% higher. We assume that these older results are more comparable to the ecoinvent V3.3
dataset from the same year.
However, the energy use and emissions from the production of automotive traction batteries decreased in recent years and further optimization is expected [11
]. Furthermore, the high electricity consumption highlights the importance of renewable energy sources in order to reduce scope 2 emissions during component production, especially lithium-ion batteries. The percentage of renewable energy sources varies significantly from country to country, which means that cradle-to-grave assessments must consider varying amounts of electricity consumption for the manufacturing and use phase [76
Due to the lack of primary data, secondary data and expert verification had to be used. Thus, the valid range of the life-cycle inventory must be regarded critically. The results might not be applicable to vehicle concepts that strongly deviate from the N3 tractor that is used. Smaller tractors that are, for example, common in Japan, and rigid trucks that are often used in Europe, cannot be represented with this inventory. To verify the model, we compared the results to published data at the vehicle level. The percentage of vehicle weight of the studies by Gaines et al. [41
] and Altenburg et al. [77
] and the standard deviation of the two and the presented study are shown in Figure 9
. Since the studies have different categorizations of materials, we adapted all results to match the categories with the smallest divisions.
The relatively high deviations in steel, iron and polymer of the presented LCI compared to Gaines et al. can be explained by the different geographical locations: Gaines et al. examined US class 8 trucks. The tighter weight restrictions of approximately 36 t GVW (80,000 lbs) in the US [78
] might have led to a greater use of lightweight design and materials (light-metal) than in Europe. Adapted to the categorization of Altenburg et al. [77
], the presented model shows good agreement with an absolute deviation of less than 5% for all categories. However, the production’s global warming potential of 43
estimated by Altenburg et al. is 41% higher compared to our results. On the one hand, this could be attributed of the higher amount of aluminum and consequently greater energy consumption. On the other hand, the background database used for our results is most likely different from the one used by Altenburg et al. Furthermore, Altenburg et al. might have detailed information about the manufacturing processes, allowing for more precise modeling.
Given our generic approach in order to represent a variety of vehicle and powertrain concepts, the comparison with existing data shows a sufficient agreement to apply the LCI to the early concept phase. Because this contribution adds detailed information on component level to previous studies, the purpose of assessing environmental impact during concept development is sufficiently fulfilled. Nevertheless, additional data from primary data sources—especially truck OEMs—could further increase the data quality and the reliability of the results.
The presented life-cycle inventory and results extend the knowledge necessary for evaluating new, emerging powertrain technologies. Further studies could advance the presented approach by combining environmental impact with economic or social perspectives and including fuel-cell powered vehicles. These studies should include further impact categories such as emissions to water and air. Only the combined consideration of the three dimensions of sustainability will make an efficient transition to green freight transport possible.