The feasibility of heavy battery electric trucks

Abstract

•Fast charging can improve competitiveness for heavier battery electric trucks•Efficiency, load capacity, and fuel savings scale effects benefit electrification•Current technology close to a threshold where electric trucks become feasible•Earlier conclusions on heavy electric trucks are sensitive to battery improvements Heavy trucks are very difficult to make battery electric if the range needs to match that of diesel trucks. Diesel is a very energy-dense fuel, and for long-range transport, the load capacity of a battery truck would be limited. If electric trucks can be fast-charged in the same way as personal electric vehicles, the required range is dramatically reduced and electrification becomes much more realistic. We show that if fast charging is available, the competitiveness of battery electric trucks compared with diesel trucks can actually improve with larger trucks. We also argue that previous findings pointing out that heavy trucks are harder to electrify than lighter trucks are very sensitive to assumptions about the battery cost and battery lifetime. Because battery technology is improving fast, the feasibility of heavy battery electric trucks is also changing fast. Future research and policy making on how to reduce carbon emission from trucks need to consider battery electric options closer. Research on the decarbonization of transport often concludes that heavy battery electric trucks are infeasible due to the incompatibility of long driving distance with high energy use and low specific energy and high costs of batteries. However, emphasis is often placed on battery electric range matching that of diesel trucks, instead of overall competitiveness. We model battery electric trucks that use high-power fast charging, enabling smaller batteries and showing that the economics of battery electric trucks per ton-kilometer improves with greater weight, driven by increasing load capacity as well as increased energy savings as a function of weight. Furthermore, we show that previous findings that the competitiveness per kilometer is worse for heavy trucks than for lighter trucks are very sensitive to assumptions about the battery cost per kWh and lifetime of the battery pack. Given the rapid development of batteries, we conclude that the economic feasibility of heavy battery electric trucks might have been generally underestimated. Research on the decarbonization of transport often concludes that heavy battery electric trucks are infeasible due to the incompatibility of long driving distance with high energy use and low specific energy and high costs of batteries. However, emphasis is often placed on battery electric range matching that of diesel trucks, instead of overall competitiveness. We model battery electric trucks that use high-power fast charging, enabling smaller batteries and showing that the economics of battery electric trucks per ton-kilometer improves with greater weight, driven by increasing load capacity as well as increased energy savings as a function of weight. Furthermore, we show that previous findings that the competitiveness per kilometer is worse for heavy trucks than for lighter trucks are very sensitive to assumptions about the battery cost per kWh and lifetime of the battery pack. Given the rapid development of batteries, we conclude that the economic feasibility of heavy battery electric trucks might have been generally underestimated. Decarbonization of road freight by using heavy battery electric freight trucks is generally found to be of limited feasibility as a means of combating climate change, with high costs and low gravimetric-specific energy of batteries typically found to be the limiting factors.1Çabukoglu E. Georges G. Küng L. Pareschi G. Boulouchos K. Battery electric propulsion: an option for heavy-duty vehicles? Results from a Swiss case-study.Transp. Res. C. 2018; 88: 107-123Crossref Scopus (51) Google Scholar, 2Forrest K. Mac Kinnon M. Tarroja B. Samuelsen S. Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sectors in California.Appl. Energy. 2020; 276: 115439Crossref Scopus (27) Google Scholar, 3Liimatainen H. van Vliet O. Aplyn D. The potential of electric trucks – an international commodity-level analysis.Appl. Energy. 2019; 236: 804-814Crossref Scopus (65) Google Scholar, 4Sripad S. Viswanathan V. 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C. 2018; 88: 107-123Crossref Scopus (51) Google Scholar, 2Forrest K. Mac Kinnon M. Tarroja B. Samuelsen S. Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sectors in California.Appl. Energy. 2020; 276: 115439Crossref Scopus (27) Google Scholar, 3Liimatainen H. van Vliet O. Aplyn D. The potential of electric trucks – an international commodity-level analysis.Appl. Energy. 2019; 236: 804-814Crossref Scopus (65) Google Scholar,9Davis S.J. Lewis N.S. Shaner M. Aggarwal S. Arent D. Azevedo I.L. Benson S.M. Bradley T. Brouwer J. Chiang Y.-M. et al.Net-zero emissions energy systems.Science. 2018; 360: eaas9793Crossref PubMed Scopus (560) Google Scholar, 10IPCCTransport.in: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 2014Google Scholar, 11Laser M. Lynd L.R. Comparative efficiency and driving range of light- and heavy-duty vehicles powered with biomass energy stored in liquid fuels or batteries.Proc. Natl. Acad. Sci. USA. 2014; 111: 3360-3364Crossref PubMed Scopus (9) Google Scholar, 12Tanco M. Cat L. Garat S. A break-even analysis for battery electric trucks in Latin America.J. Cleaner Prod. 2019; 228: 1354-1367Crossref Scopus (18) Google Scholar, 13Gao Z. Lin Z. Davis S.C. Birky A.K. Quantitative evaluation of MD/HD vehicle electrification using statistical data.Transport. Res. Rec. 2018; 2672: 109-121Crossref Scopus (6) Google Scholar Reviews on decarbonization strategies, thus, generally concludes that heavy battery electric freight trucks are likely to play a rather small role due to these technical limitations.9Davis S.J. Lewis N.S. Shaner M. Aggarwal S. Arent D. Azevedo I.L. Benson S.M. Bradley T. Brouwer J. Chiang Y.-M. et al.Net-zero emissions energy systems.Science. 2018; 360: eaas9793Crossref PubMed Scopus (560) Google Scholar,10IPCCTransport.in: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 2014Google Scholar In this paper, we present results that goes against this assertion based on an analysis of heavy BEV truck competitiveness that (1) includes high-power fast charging and (2) explicitly analyses both costs per distance (km) and transported weight per distance (ton-km) and how these costs depends on gross vehicle weight (GVW). We show that the cost competitiveness of heavy battery electric trucks compared with traditional diesel-powered trucks is highly sensitive to underlying assumptions, especially pertaining to costs and performance of batteries. Furthermore, our results indicate that the cost competitiveness of heavy BEV trucks can improve with greater truck weights, as a result of a set of different scaling effects that jointly work in favor of battery electric trucks as battery technology improves. Recent years have seen a number of studies that evaluate the competitiveness of BEV trucks, measured as the total cost of ownership (TCO) in terms of total life time cost and total cost per km.6Mareev I. Becker J. Sauer D. Battery dimensioning and life cycle costs analysis for a heavy-duty truck considering the requirements of long-haul transportation.Energies. 2018; 11: 55Crossref Scopus (41) Google Scholar,11Laser M. Lynd L.R. Comparative efficiency and driving range of light- and heavy-duty vehicles powered with biomass energy stored in liquid fuels or batteries.Proc. Natl. Acad. Sci. USA. 2014; 111: 3360-3364Crossref PubMed Scopus (9) Google Scholar,12Tanco M. Cat L. Garat S. A break-even analysis for battery electric trucks in Latin America.J. Cleaner Prod. 2019; 228: 1354-1367Crossref Scopus (18) Google Scholar Research also tends to focus on the weight of the battery pack and how this negatively affects load capacity.1Çabukoglu E. Georges G. Küng L. Pareschi G. Boulouchos K. Battery electric propulsion: an option for heavy-duty vehicles? Results from a Swiss case-study.Transp. Res. C. 2018; 88: 107-123Crossref Scopus (51) Google Scholar, 2Forrest K. Mac Kinnon M. Tarroja B. Samuelsen S. Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sectors in California.Appl. Energy. 2020; 276: 115439Crossref Scopus (27) Google Scholar, 3Liimatainen H. van Vliet O. Aplyn D. The potential of electric trucks – an international commodity-level analysis.Appl. Energy. 2019; 236: 804-814Crossref Scopus (65) Google Scholar, 4Sripad S. Viswanathan V. Performance metrics required of next-generation batteries to make a practical electric semi truck.ACS Energy Lett. 2017; 2: 1669-1673Crossref Scopus (80) Google Scholar An associated strand of research assesses the share of transport demand that can be fulfilled with battery electric trucks. Scholars in this case focus on analyzing current road freight transport patterns in terms of traveled distances and transported loads in different truck segments and comparing this with modeled capability of battery electric trucks.1Çabukoglu E. Georges G. Küng L. Pareschi G. Boulouchos K. Battery electric propulsion: an option for heavy-duty vehicles? Results from a Swiss case-study.Transp. Res. C. 2018; 88: 107-123Crossref Scopus (51) Google Scholar, 2Forrest K. Mac Kinnon M. Tarroja B. Samuelsen S. Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sectors in California.Appl. Energy. 2020; 276: 115439Crossref Scopus (27) Google Scholar, 3Liimatainen H. van Vliet O. Aplyn D. The potential of electric trucks – an international commodity-level analysis.Appl. Energy. 2019; 236: 804-814Crossref Scopus (65) Google Scholar,13Gao Z. Lin Z. Davis S.C. Birky A.K. Quantitative evaluation of MD/HD vehicle electrification using statistical data.Transport. Res. Rec. 2018; 2672: 109-121Crossref Scopus (6) Google Scholar We build on the above research, observing that if battery weight is the limiting factor, it is crucial to analyse competitiveness not just per distance traveled, but also include transported loads, i.e., cost per ton-km in addition to the cost per km. We also add to the literature in that our analysis include high-power fast charging (on the order of 1 MW), a factor that is crucial for understanding our analysis and results. If fast charging is available, this eases the constraint that a BEV truck has to be able to run for a high number of hours or very long distances to be competitive with conventional trucks. Our analysis assume that trucks operate half a shift modeled as 4.5 h, charge during a 40 min session, and we use a simplified drive cycle with 75% free flow and 25% congested conditions with velocities of 80 and 20 km/h respectively. This results in an average velocity of 65 km/h and the needed range of 290 km aiming to be representative of an average use case. That is, instead of focusing on technical parity, we turn the attention to cost competitiveness, aiming to include all major costs resulting from more frequent charging sessions. Our results are not applicable to use cases where this charging pattern is not logistically possible, even if infrastructure becomes broadly available, and use cases with higher average velocity are more challenging for BEV than our modeled generic average case. To explore how BEV trucks with fast charging perform economically compared with diesel trucks, we develop a model that aims to capture average global conditions. We not only include charging equipment and energy costs but also additional driver and capital costs incurred during the break necessary for charging. In some markets, such a break is already mandatory (e.g., EU), but for other markets, BEV trucks in our analysis have an on average ca 15% increased delivery times compared with diesel trucks due to the 40 min charge time added every 4.5 h. Our study is limited in that we do not include any value of time associated with this change, which is important but beyond the scope of our analysis. As the inclusion of high-power fast charging is central for our analysis, it is important to note that there is currently no such system in commercial operation. However, we argue that technological obstacles to their deployment can be overcome and note that there are new MW-charging standards being developed by CHAdeMO (ChaoJi)14Yoshida M. Charging Standard - Future Direction - February 17th, 2020 CHAdeMO Association Secretary General Makoto YOSHIDA (CHAdeMO Association).2020Google Scholar and CCS (HPCCV).15Bracklo Claas Mapping standards for low- and zero-emission electric heavy duty vehicles. Presentation by CharIn at International Transport Forum Expert Worshop. Perspective on standardisation development. 17-18 2020 Paris, France. https://www.itf-oecd.org/mapping-standards-low-and-zero-emission-electric-heavy-duty-vehicles-expert-workshop. INInternational Transport Forum; 2020.Google Scholar The shortest possible charging time of Li-ion batteries is not limited by the size of the battery pack per se but by the battery cell technology that determines the possible C-rate.16Ecker M. Nieto N. Käbitz S. Schmalstieg J. Blanke H. Warnecke A. 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This means that in order for BEV trucks to mimic charging patterns from personal vehicles, the limiting factors are the high power levels and development and deployment of such chargers and not Li-ion battery technology per se. Finally, it can be noted that fast charging times similar to personal vehicles is emerging for lighter trucks19Scania Scania’s commitment to battery electric vehicles.https://www.scania.com/group/en/home/newsroom/news/2021/Scanias-commitment-to-battery-electric-vehicles.htmlDate: 2021Google Scholar and that battery electric cars fast charge at ca 150 kW20Gnann T. Funke S. Jakobsson N. Plötz P. Sprei F. Bennehag A. Fast charging infrastructure for electric vehicles: today’s situation and future needs.Transp. Res. D. 2018; 62: 314-329Crossref Scopus (134) Google Scholar today, but chargers up to 350 kW are available.21ABBElectric Vehicle Infrastructure Terra HP high power charging UL.https://library.e.abb.com/public/ffebef28c136483990435f79fb17d67b/ABB_Terra-HP_UL_G2_Data-SheetR5.pdfDate: 2020Google Scholar,22Liang X. Srdic S. Won J. Aponte E. Booth K. Lukic S. A 12.47 kV medium voltage input 350 kW EV fast charger using 10 kV SiC MOSFET.IEEE Applied Power Electronics Conference and Exposition (APEC). 2019; : 581-587Crossref Scopus (16) Google Scholar Conceptually, a straightforward method to achieve high-power charging for heavy trucks (i.e., a peak of one MW or more per truck) could be to use multiple parallel 150 or 350 kW chargers. In other words, although the deployment of high-power charging could be complicated by other factors, not least limits in the local power grid, we conclude that MW chargers charging Li-ion batteries at a peak rate of 1.5 C is conceptually feasible and likely technologically feasible as well. This warrants a more careful evaluation of how this affects the viability of BEV trucks. Using a model with various sets of assumptions about battery technology cost and capabilities, our analysis is focused on the economic competitiveness of electric trucks when high-power charging is available. Our model explores variations across a set of three central battery pack parameters: battery cost, cycle life, and specific energy. Parameter set 1 (PS1) is broadly representative of conservative and slightly older assumptions used in the literature analyzing electric trucks by using Li-ion batteries (300 USD/kWh, 1,000 cycles, 125 Wh/kg). Parameter set 3 (PS3) is broadly representative of an optimistic outlook that takes into account recent literature on Li-ion batteries (100 USD/kWh, 5,000 cycles, 175 Wh/kg). Finally, parameter set 2 (PS2) represents the arithmetic mean for each parameter between PS1 and PS3 (200 USD/kWh, 3,000 cycles, 150 Wh/kg). Details and sources are found in experimental procedures and Table S1. We first show that with conservative battery specifications (PS1), battery electric truck competitiveness clearly worsens with greater GVW (Figure 1A). This is in line with previous analyses in literature, i.e., that lighter electric trucks are more feasible than heavy electric trucks (Figure 1A, PS1 top most curve with increasing and convex shape). However, using the more optimistic set of assumptions (PS3) reverses the result: BEV truck competitiveness now improves with GVW. Finally, the midpoint values (PS2) fall around a threshold where BEV truck competitiveness has no significant trend as a function of GVW (Figure 1A). In other words: how the modeled cost per km depends on GVW and, therefore, truck segment is very sensitive to battery specifications. If we instead model costs per ton-km, the result is that the competitiveness of electric trucks improves with GVW, independent of assumptions made about the battery pack in PS1 through PS3 (Figure 1B). There are two main reasons for this: (1) the overall efficiency improvements in terms of cost per ton-km that come with larger trucks, and (2) the relation between load capacity and battery pack weight. In general, it is beneficial to increase the weight of a truck because load capacity grows faster than energy consumption and vehicle costs.23Liimatainen H. Pöllänen M. Nykänen L. Impacts of increasing maximum truck weight – case Finland.Eur. Transp. Res. Rev. 2020; 12: 14Crossref Scopus (12) Google Scholar For heavier BEV trucks in particular, this also means that the costs of electrification are distributed over a larger load (on the per ton-km metric) and (2) that load capacity in our model increases faster than battery weight. The second effect is conceptually important: for a given needed range of the electric truck between charges, the share of the load capacity taken up by the battery pack weight decreases with weight of the truck (Figure S1). This contributes to battery electric trucks becoming more competitive, the heavier the vehicle is on a cost per ton-km basis. However, the condition for cost competitiveness is then the same for cost per km and cost per ton-km: improvements from lower energy and maintenance costs have to outweigh battery costs and cost-related charging. Our model indicates that if the performance of Li-ion batteries PS3 is reached (Figures 1A and 1B, PS3), heavy BEV trucks can be competitive in absolute terms both per ton-km and per km. In other words, contrary to the conclusions previously drawn in the literature, these results suggest that if high-power fast charging is available, thereby allowing for smaller batteries to be used, BEV trucks can be feasible in the heaviest categories. Assuming no or limited fast charging necessitates significantly longer range and, thus, larger batteries. Cost savings are primarily driven by the change from internal combustion engines to electric motors, which lowers energy costs, not because the electricity costs less (global average diesel and electricity costs are approximately the same per kWh) but because of the higher efficiency of the electric powertrain. In free flow, we assume that the energy efficiency ratio is 2.5 times and under congested conditions (lower average speed) the energy efficiency ratio is higher at 4.2 (see Table S2). Our model uses a very simplified drive cycle with 75% of the drive time in free flow (see Table S2) and the free flow energy efficiency ratio, thus, dominates. Note that recent empirical studies could warrant values as high as 3 in free flow and 5 in urban congested conditions (see Table S2), which reflect the assumptions recently used by Tanco et al.12Tanco M. Cat L. Garat S. A break-even analysis for battery electric trucks in Latin America.J. Cleaner Prod. 2019; 228: 1354-1367Crossref Scopus (18) Google Scholar Added costs of electrification are primarily determined by the assumptions about battery capital cost per km—itself a function of battery costs per kWh and cycle life. When battery capital costs are low, the gains from the higher energy efficiency of heavy battery electric trucks tend to increase faster with weight than does the capital cost of batteries. This further strengthens BEV truck competitiveness and can potentially enable competitiveness both per km and per ton-km. Together this difference between energy savings and added costs is the main driver behind the change in model behavior in Figure 1A, which shows a shift from worse competitiveness to improved competitiveness as a function of GVW as battery specifications improve (see also Figures S2 and S3). In other words, below a certain threshold for the joint battery parameter values, fuel cost savings more than offset the costs of vehicle electrification. For battery costs of 300 USD kWh−1, this holds for a cycle life of approximately 2,000 (see Section S1). However, the cost of charging equipment together with the modeled higher capital, insurance, and salary costs—as well as the costs related to idle time during charging—are substantial (see Figure S3). This means that the threshold to reach competitiveness is rather on the order of 200 USD kWh−1 and a cycle life of 3,000 (Figure 1A, PS2). It is, however, important to note that these values could be optimistic as frequent fast charging causes high degradation. On the other hand, assumptions can be conservative in the light of recent progress toward faster charging times, but especially rapid reductions in battery costs18Nykvist B. Sprei F. Nilsson M. Assessing the progress toward lower priced long range battery electric vehicles.Energy Policy. 2019; 124: 144-155Crossref Scopus (102) Google Scholar,24Henze V. Battery Pack Prices Fall as Market Ramps Up With Market Average at $156/kWh in 2019.https://about.bnef.com/blog/battery-pack-prices-fall-as-market-ramps-up-with-market-average-at-156-kwh-in-2019/Date: 2019Google Scholar,25Kittner N. Lill F. Kammen D.M. Energy storage deployment and innovation for the clean energy transition.Nat. Energy. 2017; 2: 17125Crossref Scopus (378) Google Scholar as well as research on how to create longer cycle life in NMC chemistry by using new additives26Harlow J.E. Ma X. Li J. Logan E. Liu Y. Zhang N. Ma L. Glazier S.L. Cormier M.M.E. Genovese M. et al.A wide range of testing results on an excellent lithium-ion cell chemistry to be used as benchmarks for new battery technologies.J. Electrochem. Soc. 2019; 166: A3031-A3044Crossref Scopus (148) Google Scholar and single-crystal technology.27Liu Y. Harlow J. Dahn J. Microstructural observations of “single crystal” positive electrode materials Before and After long term cycling by cross-section scanning electron microscopy.J. Electrochem. Soc. 2020; 167: 020512Crossref Scopus (68) Google Scholar To assess our main findings in further detail, three sets of sensitivity analyses are conducted. First, by using battery parameter specifications PS2 as a baseline, a Monte Carlo simulation is conducted wherein each parameter is randomly changed by ±33%, thereby covering a large range of variation in assumptions of model parameter values, as well as uncertainty in technical parameters (see Table S2). It is important to acknowledge that for some parameters, a 33% change is clearly not realistic, e.g., a 33% increase in the load capacity parameter would make load 95% of the GVW and the sensitivity analysis gives rise to a number of unrealistic extreme cases. However, a change on the order of 33% is needed to cover a wide uncertainty in some parameters and in order to simplify the model evaluation, the same sensitivity is applied for all parameters. For costs per km, this sensitivity analysis clearly visualizes how sensitive general conclusions on lighter and heavier battery electric trucks are to assumptions in parameter specifications (Figure 2A). Again, the battery specifications in PS2 appear to be close to the threshold where this dependency changes, as seen in (Figure 1A). For costs per ton-km, Figure 2B shows that the main finding, i.e., that increasing the GVW improves the outlook for BEV holds also under this large variation of model assumptions. For some combinations of parameter values the result can be that a vehicle requires battery pack weights that exceed the load capacity. In these cases, increasing the vehicle weight enables a larger cargo capacity and reduces the constraint. Second, a sensitivity analysis for a typical heavy-duty truck with a GVW of 40 ton is conducted. In this case, all parameters are increased 33% compared with PS2 and otherwise default values. The resulting changes in terms of the altered difference between the cost of diesel and battery electric (in US cent tkm−1) are shown in Figure 3. As can be expected, the model is most sensitive to the parameters determining fuel savings. These include the relative energy efficiency of battery electric drivetrains and the price of diesel (Figure 3), but especially the average velocity at highway speed, where a higher value lowers the energy consumption ratio in our model (see Table S2). The combined sensitivity to changes in battery specifications (specific energy, cost, and life cycles) is high, as these determine the capital cost of the battery pack. It is noteworthy that a range of parameters beyond battery pack specifications, such as energy efficiency and average velocity, can offer substantial opportunities to improve the overall competitiveness of battery electric trucks. Third, as fuel cost savings between electricity and diesel are critically important, and these vary considerably globally and more than our generic sensitivity analysis of 33%, we run the model with 50% lower diesel fuel price (ca 0.6 USD liter−1). This is representative for countries with much lower fuel costs than the global average, e.g., current costs in the US. As expected, this reduces competitiveness considerably, both for cost per km and per ton-km but does not alter the general finding that competitiveness per ton-km improves with weight (see, Figure S4). Additional sensitivity analysis could be conducted, such as higher electricity prices, but as Figure 3 show, the model is more sensitive to changed diesel fuel than electricity costs, stemming from the fact that BEV vehicles has a larger share of total cost in capital cost and less in fuel costs. It is important to note that the capital cost of the battery is not only determined by the battery cost per kWh but also by the lifetime of batteries. The 200% increase between 1,000 cycles and 3,000 cycles in PS1 and PS2 is very pronounced and results in a very sharp cost reduction in Figure 1A that is more important than the 300 to 200 US