With the rapid expansion of the electric vehicle (EV) market and the growing demand for extended all-electric range, reducing the carbon footprint of lithium-ion batteries (LIBs) has become increasingly important. While recent studies have primarily focused on the cradle-to-gate stage of LIBs, understanding of the energy consumption and carbon footprint during the use phase of EV batteries remains limited, and there is no consensus on the evaluation approach for this stage. This study characterizes the impacts of the use phase and identifies key parameters using a physics-based model. We applied this method to existing LIB life cycle assessment (LCA) studies to estimate the use phase emissions and examine the effects of crucial factors such as battery mass and charging-discharging losses.

Building on our previous work, which introduced a physics-based hierarchical model for evaluating energy consumption during the battery use phase, this study incorporates the impact of battery degradation on charging-discharging energy losses. Our model demonstrates that internal energy losses during charge-discharge cycles are determined from an efficiency perspective, while the energy consumption induced by battery mass is assessed through the perspective of vehicle load dynamics, specifically the power demand required to complete a drive cycle. Literature LCAs often employ these two different viewpoints simultaneously, resulting in double counting. Instead, we provide two separate models for evaluating the use phase carbon footprint of various LIB designs under different conditions, including grid mix and EV lifetime.

A model application shows that the carbon footprint from energy losses during charge-discharge cycles, e.g. ~5% of energy stored in the battery, is rather homogeneous across battery designs in the initial stage of battery life, although it is a function of EV fuel economy. As battery degradation gradually increases energy losses over EV lifetime, smaller batteries may lose capacity faster than larger batteries due to higher number of charge-discharge cycles compared to larger batteries for a given EV fuel economy and driving distance. On the other hand, the carbon footprint induced by battery mass, unsurprisingly, varies depending on battery size (kWh) and energy density (Wh/kg). Our findings reveal that the use phase carbon footprint constitutes a significant portion, exceeding 50% of the cradle-to-gate emissions in all modeling viewpoints and scenarios analyzed. These results underscore the necessity of considering both production and use phases in EV battery LCA and highlight potential trade-offs between these phases based on design choices such as battery chemistry and size that determine EV range.

Reducing GHG emissions from vehicle production is critical for decarbonizing the automotive sector. This study quantifies emissions from U.S. auto-body aluminum and steel sheet component production (2025–2050) and evaluates circular economy (CE) strategies, such as enhanced post-consumer and closed-loop scrap recycling rates and improved manufacturing process yields, under reference, moderate, and aggressive process and grid decarbonization scenarios. Emissions estimates are derived by integrating dynamic material flow analyses (2025–2050) of auto-body sheet metal usage and scrap availability with cradle-to-gate process impact models for component manufacturing.

Currently, emissions intensities for auto-body aluminum and steel sheet components are estimated at 12.3 kgCO2eq/kg and 4.31 kgCO2eq/kg, respectively. In the reference scenario, in which process and grid emissions remain unchanged, annual emissions from auto-body aluminum and steel sheet component manufacturing are projected to increase by 54% (reaching 7 MtCO2eq) and 18% (reaching 108 MtCO2eq), respectively, by 2050. Implementing CE strategies, such as improving manufacturing process yields to 70% from 44% for aluminum and 47% for steel, maximizing post-consumer scrap recycling rates from 0% to 80% for aluminum and 30% to 80% for steel, and increasing closed-loop scrap recycling rates to 100% from 91% for aluminum and 31% for steel could reduce these projected emissions by up to 52% for aluminum and 53% for steel. For aluminum, the most impactful emission reduction measure is enhancing post-consumer scrap recycling (reducing emissions by 2.4 MtCO2eq), followed by improving manufacturing process yields (1.1 MtCO2eq) and increasing closed-loop recycling (0.5 MtCO2eq). In contrast, for steel, the most significant reductions come from increasing closed-loop recycling (42 MtCO2eq), followed by boosting manufacturing process yields (25 MtCO2eq) and post-consumer scrap recycling (16 MtCO2eq). While the relative effectiveness of these CE strategies diminishes as processes and grid decarbonize, they remain crucial, delivering emissions reductions of 23%–54% by 2050 under aggressive decarbonization scenarios. This study reveals end-of-life management opportunities and challenges that must be overcome to transition toward a low-carbon automotive industry.

The heavy-duty vehicle (HDV) sector is a key decarbonization candidate, contributing 6% of Canada’s GHG emissions. Advances in battery and charging technology have made heavy-duty battery-electric vehicles (HDBEVs) a viable road freight decarbonization pathway. To support the operational efficiency and reliability of Canadian energy systems as electrification accelerates, accurately predicting electrical charging profiles for HDBEV fleets is paramount. Charging profiles report the total hourly electrical power required for charging operational HDBEV fleets, which must be satisfied by local transmission and distribution. While many studies use American HDV GPS data to forecast charging profiles, research using Canadian HDV data is scarce. This study leverages high-resolution GPS data from the American Transportation Research Institute (ATRI) for HDVs operating in Ontario, Canada in 2019 to produce annual HDBEV fleet charging profiles under different technology advancement scenarios and operator charging strategies.

The project method is composed of two phases: processing of HDV GPS data and modelling of charging demand. Processing entails converting timeseries data for real-world HDV travel data into travel operation schedules, which distinguish between driving periods and charging opportunities. These schedules detail driving start and end times, distance travelled, geographical location of stops, and estimates of cargo weights and ambient temperature data. Phase two applies a linear approach to predict the charging demand for randomly sampled HDBEV fleets travelling according to operation schedules. The model uses government reported data for technological parameters such as HDBEV battery size and charging rates. A key model input is the choice of fleet charging strategy, chosen from immediate (instantly charge once stationary), delayed (fully charge just before departing), or minimum power (lower constant power throughout the entire stationary period).

Preliminary findings use data from August 8 to 14, 2019, producing schedules for 1960 HDVs, covering a total travel distance of 4.4 million kilometres. Results indicate the charging strategy directly impacts the fleet demand. Applying immediate charging increases demand during afternoon peak times by over 30 percent compared to delayed and minimum power charging. Charging at minimum power results in lowest peak power demand of all three strategies, while increasing off-peak demand, serving as a load-levelling approach. These results indicate that in place of rapid technology advancement, operator behaviour has the highest potential to reduce HDBEV fleet charging demand. These findings provide energy system operators and energy resource policymakers valuable insights into HDBEV load profiles and power demand variations across operator charging behaviours. Ongoing work is focused on improving accuracy of scheduling, implementing non-linear HDBEV charging, and processing full year 2019 data.

The Coordinating Research Council (CRC), which was formed in 1919, is a non-profit association that acts as a forum for cooperative, pre-competitive, publicly available mobility-related research. In 2021, CRC added the Sustainable Mobility Committee (SMC) to its technical research portfolio in response to member interest. The SMC has four technical focus areas, called Working Groups, in Fuels, Electrification, Life Cycle Analysis, and Hydrogen & Carbon Reduction.

Since CRC’s Executive Director Chris Tennant’s 2024 ISSST keynote presentation, the SMC has completed and published a wide range of technical research, found at crcao.org/published-reports-full. This presentation would review this research, as well as discuss upcoming research foci within each of the SMC Working Groups, with specific emphasis on Life Cycle Analysis.

While CRC’s Members are generally in industry, technical research contracts are frequently awarded to government partners (often National Labs) and academia. This presents a unique perspective for ISSST participants to understand how collaborative, consensus-based research between industry, government, and academic institutions works in practice.

Potential Thematic Areas: Sustainability and resilience of energy systems, Other creative sustainability-related topics, Business & Industry Practices or Case Studies, Open Science and Communication of Sustainability Science

Preference: ORAL, 30 min (also could be ORAL, 15 min with poster)

• If provided a 45 min oral slot, the presentation would provide a quick background on CRC, review each of the 8+ technical research publications and proceedings published in the past year, and review in some detail prospective upcoming research projects.

• If provided a 30 min oral slot, the presentation would provide a quick background on CRC review each of the 8+ technical research publications and proceedings published in the past year, and quickly highlight 8 upcoming research projects.

• If provided a 15 min oral slot with a poster, the presentation would show information about CRC, quickly highlight some key takeaways from technical research over the past year, show a couple upcoming research projects, and invite folks to speak with the presenter at her poster.

The frequency of unplanned power outages has increased with recurrent extreme weather conditions and natural hazards. During and in the aftermath of such events, a reliable backup power supply is essential. Electric vehicles (EVs) could serve as a solution for backup power supply in emergencies due to their potential use as mobile energy resources when equipped with bi-directional energy exchange technology, allowing them to supply power to external loads by discharging their batteries.

This research proposes energy supply logistics using electric uncrewed aerial and ground vehicles (UAVs and UGVs) within the framework of a two-echelon electric location routing problem. In our model, vehicle batteries are recharged at satellite locations while they discharge both when traversing the transportation network and to serve energy demand, acting as portable battery storage resources. The network consists of two echelons: the first echelon operates between the depot starting point of travel, housing all UAVs and UGVs, and open satellites equipped with EV recharging infrastructure using only UGVs. The second echelon operates between open satellites and demand nodes requiring energy to be served using both UAVs and UGVs. The objective is to determine the route of each vehicle and the location of open satellites to minimize the total distance for both echelons while satisfying all energy demands.

We solve the problem with an exact method developed based on the Held-Karp algorithm using dynamic programming to find the minimum travel distance across both echelons. We introduce a penalty term to the link distance by adding it to the physical distance. The penalty is applied if the current battery state of charge (SOC) is insufficient to return to the originating satellite to recharge or complete the travel, indicating that the current sequence of nodes will be infeasible in the next step. With the penalty term, we can discard infeasible sequences of nodes and guarantee optimality. However, the exact method is time-consuming for large networks. Therefore, a heuristic method was developed, which clusters the demand nodes into groups first and then solves the problem within each group. To evaluate the performance of the heuristic method, we compared the solutions from the two methods for randomly generated networks.

Lastly, the model was applied to a real network in four counties in California corresponding to a Public Safety Power Shutoff (PSPS) scenario conducted by PG&E in 2020. Our model is applied to provide backup power to resilience hubs (demand nodes) that require necessary power for medical equipment and care for the elderly and young children. We found that UAVs are preferred due to their shorter travel distances compared to UGVs traversing the road network. However, due to the smaller capacity of UAVs, higher energy demand or long-distance demand nodes are served by UGVs. Additionally, the model determines the number of open satellites that can minimize the total travel distance and provides insights into preparedness for recharging the energy supply logistics fleet in California post-wildfire recovery scenarios.