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1. Joint Design and Control of Electric Vehicle Propulsion Systems

   https://doi.org/10.23919/ECC51009.2020.9143869  

   Verbruggen, Frans; Salazar, Mauro; Pavone, Marco; Hofman, Theo (May 2020, 2020 European Control Conference (ECC))

   null (Ed.)

   This paper presents models and optimization methods for the design of electric vehicle propulsion systems. Specifically, we first derive a bi-convex model of a battery electric powertrain including the transmission and explicitly accounting for the impact of its components' size on the energy consumption of the vehicle. Second, we formulate the energy-optimal sizing and control problem for a given driving cycle and solve it as a sequence of second-order conic programs. Finally, we present a real-world case study for heavy-duty electric trucks, comparing a single-gear transmission with a continuously variable transmission (CVT), and validate our approach with respect to state-of-the-art particle swarm optimization algorithms. Our results show that, depending on the electric motor technology, CVTs can reduce the energy consumption and the battery size of electric trucks between up to 10%, and shrink the electric motor up to 50%. 

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2. Comparative Assessment of Machine Learning Techniques for Modeling Energy Consumption of Heavy-Duty Battery Electric Trucks

   https://doi.org/10.1109/FISTS60717.2024.10485539  

   Gonzalez, Emmanuel Hidalgo; Garrido, Jacqueline; Barth, Matthew; Boriboonsomsin, Kanok (February 2024, IEEE)

   Efforts to decarbonize the heavy-duty vehicle sector have generated vast interest in transitioning from conventional diesel trucks to battery electric trucks (BETs). As a result, understanding energy consumption characteristics of BETs has become important for a variety of applications, for instance, assessing the feasibility of deploying BETs in place of conventional diesel trucks, predicting the state-of-charge (SOC) of BETs after specific duty cycles, and managing BET charging needs at the home base or en-route. For these applications, mesoscopic energy consumption models offer a good balance between the amount and fidelity of the input data needed, such as average traffic speed and road grade on a link-by-link basis, and the model performance. As a common intelligent transportation system (ITS) application, this paper presents a comparative assessment of mesoscopic energy consumption models for BETs developed using three different machine learning techniques. The results show that the random forest (RF) regression outperforms the extreme gradient boosting (XGBoost), the light gradient boosting machine (LightGBM), as well as the conventional linear regression as evidenced by the resulting model having a higher coefficient of determination (R2) value than that of its counterparts. When applied to the simulated dataset, the RF regression can capture the behaviors of BET energy consumption well where the R2 value of the resulting model is 0.94. 

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3. Addressing the Robust Battery Electric Truck Dispatching Problem with Backhauls and Time Windows Under Travel Time Uncertainty

   https://doi.org/10.1109/ITSC58415.2024.10919697  

   Peng, Dongbo; Barth, Matthew; Boriboonsomsin, Kanok (September 2024, IEEE)

   The emergence of battery electric trucks (BETs) in recent years has shown great promise in reducing greenhouse gas (GHG) emissions in urban freight logistics. However, designing a customer-oriented dispatching strategy for a BET fleet is more complex than traditional vehicle routing problems (VRP) due to several constraints, such as limited driving range, potential need for en route recharging, and long recharging times. Also, in practice, the uncertain travel times in urban transportation network may lead to the violation of scheduled customer time windows and impact overall energy consumption. To better utilize the BET fleet, this paper introduces a robust BET dispatching problem with backhauls and time windows under travel time uncertainty, which aims to minimize the overall fleet energy consumption while also minimizing the risk of violating customer time window. A mathematical optimization model based on novel route-related sets is developed, and an adaptive large neighborhood search (ALNS) metaheuristic algorithm is used to find robust dispatching solutions. Based on real-world data from a truck fleet in San Bernardino County, California, a simulation study is conducted to demonstrate the robustness of the solutions obtained by the proposed method. Moreover, a sensitivity analysis with respect to uncertainty parameters is performed to assess the trade-off between the overall fleet energy consumption and the robustness of the solutions. 

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4. Economic Viability Assessment of Repurposed EV Batteries Participating in Frequency Regulation and Energy Markets

   Robson, Sarah; Alharbi, A.; Gao, W; Khodaei, A; Alsaidan, I. (April 2021, 2021 IEEE Green Technologies Conference)

   null (Ed.)

   The high cost and growing environmental concerns surrounding lithium-ion batteries have motivated research into extending the life of electric vehicle(EV) batteries by repurposing them for second life grid applications. The incorporation of repurposed electric vehicle batteries (REVBs) has the potential to decrease the overall cost of new battery energy storage systems (BESS) and extend the useful life of the materials. This paper focuses on maximizing daily profit that can be made from REVBs by stacking two grid services such as frequency regulation and energy arbitrage while minimizing battery capital cost by using second life EV batteries. A model for battery management with stacked frequency regulation and energy arbitrage is developed and tested using PJM market data. A mixed integer linear programming (MILP) is used to solve the optimization problem. It is found that REVBs can generate higher net profits than a new BESS. 

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5. Comparative Analysis of Energy Use and Greenhouse Gas Emission of Diesel and Electric Trucks for Food Distribution in Gowanus District of New York City

   https://doi.org/10.3389/fdata.2021.693820  

   Elangovan, Raghul; Kanwhen, Ondrea; Dong, Ziqian; Mohamed, Ahmed; Rojas-Cessa, Roberto (July 2021, Frontiers in Big Data)

   New York City’s food distribution system is among the largest in the United States. Food is transported by trucks from twelve major distribution centers to the city’s point-of-sale locations. Trucks consume large amounts of energy and contribute to large amounts of greenhouse gas emissions. Therefore, there is interest to increase the efficiency of New York City’s food distribution system. The Gowanus district in New York City is undergoing rezoning from an industrial zone to a mix residential and industrial zone. It serves as a living lab to test new initiatives, policies, and new infrastructure for electric vehicles. We analyze the impact of electrification of food-distribution trucks on greenhouse gas emissions and electricity demand in this paper. However, such analysis faces the challenges of accessing available and granular data, modeling of demands and deliveries that incorporate logistics and inventory management of different types of food retail stores, delivery route selection, and delivery schedule to optimize food distribution. We propose a framework to estimate truck routes for food delivery at a district level. We model the schedule of food delivery from a distribution center to retail stores as a vehicle routing problem using an optimization solver. Our case study shows that diesel trucks consume 300% more energy than electric trucks and generate 40% more greenhouse gases than diesel trucks for food distribution in the Gowanus district. 

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