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1. Joint planning for battery swap and supercharging networks with priority service queues

   Zhang, J. ; Bai, L. ; Jin, T. ( March 2021 , International journal of production economics)

   Existing network planning models for electric vehicle (EV) services usually treat the battery swap and the on-board supercharging as two independent processes. This study makes an early attempt to design an EV charging network where battery swap and supercharging are jointly coordinated. The swap and supercharge processes are characterized by Erlang B and Erlang C priority queues, respectively. A strategic location-allocation model is formulated to optimize the station sites, battery stock level, and the number of superchargers at chosen sites. Three design criteria, namely, battery state-of-charge, maximum service time, and power grid constraint, are simultaneously taken into account. Meta-heuristics algorithmsmore »incorporating Tabu search are developed to tackle the proposed non-linear mixed integer optimization model. Computational results on randomly generated instances show that the priority battery service scheme outperforms the pure battery swap station in terms of spare battery investment cost and charging flexibility. The case study on a real-world traffic network comprised of 0.714 million households further shows the efficacy and advantage of the dual battery charging process for ensuring state-of-charge, service time commitment, and network-wide grid stability.« less
2. Greening Electric Bike Sharing Using Solar Charging Stations

   https://doi.org/10.1145/3408308.3427621  

   Wamburu, John ; Raff, Christopher ; Irwin, David ; Shenoy, Prashant ( November 2020 , Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys))

   Electric bikes have emerged as a popular form of transportation for short trips in dense urban areas and are being increasingly adopted by bike share programs for easy accessibility to riders. Motivated by the rising popularity of electric bikes, a form of an electric vehicle, we study the research question of how to design a zero-carbon electric bike share system. Specifically we study the challenges in designing solar charging stations for electric bike systems that enable either net-zero or a fully zero-carbon operation. We design a prototype two bike solar charging station to demonstrate the feasibility of our approach. Usingmore »insights and data from our prototype solar charging station, we then conduct a data driven analysis of the costs and benefits of converting an entire bike system into one powered using solar charging stations. Using empirical analysis, we determine the panel and battery capacity for each station, and perform a feasibility evaluation of the system using 8 months of ridership data. Our results show that equipping each bike station with a single grid-tied solar panel is adequate to meet the annual charging demand from electric bikes and achieve net-zero operation using net-metering. For an off-grid setup, our analysis shows that a bike station needs twice as many solar panels, on average, along with a 1.8kWh battery, with the busiest bike station needing 6× more solar capacity than in the net-metering case. Our analysis also reveals a tradeoff between the array size and the battery size needed to achieve true-zero carbon operation for the electric bike share system.« less
3. An Electric Vehicle Battery Modular Balancing System Based on Solar Energy Harvesting

   Duan, Chen ; Tao, Hongjun ; Wang, Caisheng ; Chen, Jianfei ; Zhao, Xinru ; Zhou, Xuan ( June 2019 , IEEE Transportation Electrification Conference and Expo)

   This paper proposes a solar energy harvesting based modular battery balance system for electric vehicles. The proposed system is designed to charge the battery module with minimum SOC/voltage by solar power during charging and discharging. With the solar power input, the useful energy of the battery can be improved while vehicle driving. For vehicle charging, the charging energy from grid and total charging time can be reduced as well. Simulation analysis shows that for a 50Ah rated battery pack, the overall pure electric drive mileage can be improved by 22.9%, while consumed grid energy and total charging time can bemore »reduced by 9.6% and 9.3% respectively. In addition, the battery life can be improved around 10%~11%. The prototype design and test of a 48V battery pack vehicle consisting of four 12V battery modules are carried out. The experimental results validate that the system has good modular balance performance for the 100Ah battery modules with 5~7A charging current from solar power, and the overall usable battery energy has been increased.« less
4. UAV Assisted Cellular Networks With Renewable Energy Charging Infrastructure: A Reinforcement Learning Approach

   https://doi.org/10.1109/MILCOM52596.2021.9653034  

   Sherman, Michelle ; Shao, Sihua ; Sun, Xiang ; Zheng, Jun ( November 2021 , MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM))

   Deploying unmanned aerial vehicle (UAV) mounted base stations with a renewable energy charging infrastructure in a temporary event (e.g., sporadic hotspots for light reconnaissance mission or disaster-struck areas where regular power-grid is unavailable) provides a responsive and cost-effective solution for cellular networks. Nevertheless, the energy constraint incurred by renewable energy (e.g., solar panel) imposes new challenges on the recharging coordination. The amount of available energy at a charging station (CS) at any given time is variable depending on: the time of day, the location, sunlight availability, size and quality factor of the solar panels used, etc. Uncoordinated UAVs make redundantmore »recharging attempts and result in severe quality of service (QoS) degradation. The system stability and lifetime depend on the coordination between the UAVs and available CSs. In this paper, we develop a reinforcement learning time-step based algorithm for the UAV recharging scheduling and coordination using a Q-Learning approach. The agent is considered a central controller of the UAVs in the system, which uses the ϵ -greedy based action selection. The goal of the algorithm is to maximize the average achieved throughput, reduce the number of recharging occurrences, and increase the life-span of the network. Extensive simulations based on experimentally validated UAV and charging energy models reveal that our approach exceeds the benchmark strategies by 381% in system duration, 47% reduction in the number of recharging occurrences, and achieved 66% of the performance in average throughput compared to a power-grid based infrastructure where there are no energy limitations on the CSs.« less
5. Electric vehicle charging stations in the workplace with high-resolution data from casual and habitual users

   https://doi.org/10.1038/s41597-021-00956-1  

   Asensio, Omar Isaac ; Lawson, M. Cade ; Apablaza, Camila Z. ( July 2021 , Scientific Data)

   Problems of poor network interoperability in electric vehicle (EV) infrastructure, where data about real-time usage or consumption is not easily shared across service providers, has plagued the widespread analysis of energy used for transportation. In this article, we present a high-resolution dataset of real-time EV charging transactions resolved to the nearest second over a one-year period at a multi-site corporate campus. This includes 105 charging stations across 25 different facilities operated by a single firm in the U.S. Department of Energy Workplace Charging Challenge. The high-resolution data has 3,395 real-time transactions and 85 users with both paid and freemore »sessions. The data has been expanded for re-use such as identifying charging behaviour and segmenting user groups by frequency of usage, stage of adoption, and employee type. Potential applications include but are not limited to simulating and parameterizing energy demand models; investigating flexible charge scheduling and optimal power flow problems; characterizing transportation emissions and electric mobility patterns at high temporal resolution; and evaluating characteristics of early adopters and lead user innovation.

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