Electrifying Long-Haul Freight—Part II: Assessment of the Battery Capacity

Depcik, Christopher; Gaire, Anmesh; Gray, Jamee; Hall, Zachary M.; Maharjan, Anjana; Pinto, Darren; Prinsloo, Arno

doi:10.4271/02-12-02-0007

Cited by 9 publications

(5 citation statements)

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“…As the electrification of truck traffic progresses, new trends in battery storage technology for the individual use cases of truck applications should be investigated in addition to the cell chemistries already on the market. Examples include sodium-ion cells [46], solid-state lithium-ion cells [24], or hybrid battery storage systems consisting of lithium-ion batteries with varying characteristics [47].…”

Section: Discussionmentioning

confidence: 99%

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Spoilt for Choice: User-Centric Choice of Battery Size and Chemistry for Battery-Electric Long-Haul Trucks

Schneider,

Teichert,

Zähringer

et al. 2023

Energies

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With growing demands to save greenhouse gases, the rapid market introduction of BET will become increasingly important, with truck manufacturers announcing various models entering the market in the near future. Soon, truck operators will be faced with deciding which battery capacity and cell chemistry to choose in their next purchase. In this study, we evaluate the choice of battery capacity, regarding feasibility and cost-effectiveness, for trucks using NMC and LFP cell chemistry. Our results show that higher energy density allows larger NMC batteries to be installed, resulting in the ability to transport higher payloads at low charging powers. The LFP chemistry has to rely on higher charging powers of up to 700kW to transport the same payloads. When asked to choose a battery capacity for the individual use case, the smallest battery size should always be selected when only charging powers up to 300kW are available. However, the reduction in publicly charged energy can lead to cost advantages of larger battery capacities at higher charging powers. When deciding between the two cell chemistries, the LFP chemistry shows advantages in most cases. Only at high payloads and low charging powers the NMC chemistry shows cost advantages.

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Section: Discussionmentioning

confidence: 99%

“…Depcik et al [24] studied the impact of different cell chemistries and future improvements on the BET use case. Results show that LFP cells hold an advantage over NMC cells, based predominantly on the cycle stability.…”

Section: Introductionmentioning

confidence: 99%

Spoilt for Choice: User-Centric Choice of Battery Size and Chemistry for Battery-Electric Long-Haul Trucks

Schneider,

Teichert,

Zähringer

et al. 2023

Energies

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show abstract

“…A previous effort by one of the authors included the estimation of battery electric heavy-duty tractor trailers using a model based on the conservation of momentum [24]. This work used a similar model including additions to handle wind direction based on travel direction and electrical power requirements.…”

Section: Modelingmentioning

confidence: 99%

“…This model includes the effects of battery efficiency and cabin conditioning as a function of ambient conditions while isolating location-specific influences, such as driving conditions, from the effects of temperature. Similar to a prior effort [24], E aux was multiplied by the velocity to obtain the power draw from the battery pack due to the HVAC system operating. However, analogous to Equation (34), a second-order polynomial was used for the velocity:…”

Section: Auxiliary Powermentioning

confidence: 99%

Electric Vehicle Simulations Based on Kansas-Centric Conditions

Simpson

Bousfield

Wohleb

et al. 2022

WEVJ

Self Cite

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Range anxiety is a significant contributor to consumer reticence when purchasing electric vehicles (EVs). To alleviate this concern, new commercial EVs readily achieve over 200 miles of range, as found by the United States Environmental Protection Agency (EPA). However, this range, measured under idealized conditions, is often not encountered in real-world conditions. As a result, this effort describes the simplest model that incorporates all key factors that affect the range of an EV. Calibration of the model to EPA tests found an average deviation of 0.45 and 0.57 miles for highway and city ranges, respectively, among seven commercial EVs. Subsequent predictions found significant losses based on the impact of road grade, wind, and vehicle speed over a Kansas interstate highway. For cabin conditioning, up to 57.8% and 37.5% losses in range were found when simulating vehicles at 20 °F and 95 °F, respectively. Simulated aging of the vehicle battery pack showed range losses up to 53.1% at 100,000 miles. Model extensions to rain and snow illustrated corresponding losses based on the level of precipitation on the road. All model outcomes were translated into an Excel spreadsheet that can be used to predict the range of a generic EV over Kansas-centric roads.

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“…Several research studies show BETs' feasibility [13][14][15][16] and economic competitiveness [17][18][19] compared to today's diesel trucks. Previous work in the BET context deals with the topics of battery dimensioning [17,20,21] and cell selection [18,20,22]. However, the BET range that can be achieved while meeting payload and installation space requirements 2 of 25 is currently insufficient for long-haul transport with daily ranges up to 700 km without intermediate on-route charging [20].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Journey: Dynamic Charging Strategies for Battery Electric Trucks in Long-Haul Transport

Zähringer,

Teichert,

Balke

et al. 2024

Energies

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Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies’ results’ plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications.

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Electrifying Long-Haul Freight—Part II: Assessment of the Battery Capacity

Cited by 9 publications

References 0 publications

Spoilt for Choice: User-Centric Choice of Battery Size and Chemistry for Battery-Electric Long-Haul Trucks

Spoilt for Choice: User-Centric Choice of Battery Size and Chemistry for Battery-Electric Long-Haul Trucks

Electric Vehicle Simulations Based on Kansas-Centric Conditions

Optimizing the Journey: Dynamic Charging Strategies for Battery Electric Trucks in Long-Haul Transport

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