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Energy Demand Analysis and Powertrain Design of a High-Speed Delivery Robot Using Synthetic Driving Cycles

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  • Jari Vepsäläinen

    (Department of Mechanical Engineering, School of Engineering, Aalto University, Otakaari 4, 02150 Espoo, Finland)

Abstract

Last mile is known as the last leg of the delivery process, which is the most expensive and time-consuming part per kilometer. The most common last mile deliveries are postal packages, groceries and take away meals. Recently, there has been a growing interest and implementation of sidewalk autonomous delivery robots. These robots travel roughly at the speed of pedestrians (6 km/h). A high-speed (15 km/h) delivery robot design is proposed for reducing the time, energy and carbon footprint of deliveries. The preliminary design of the delivery robot powertrain is based on worst-case scenario analysis and Monte Carlo simulations with synthetic driving cycles. Synthetic driving cycles were used because there are no open data available of delivery robots. Thus, the procedure presented in this paper is a general approach for cases where there is no precedent application and/or no data are available. The synthetic driving cycles are based on start and end location of food-delivery services in Helsinki, Finland. Based on the simulations, the crucial factors contributing to energy demand and its variation were analyzed. Carbon footprint of the delivered package over distance of the design is compared to existing wheeled delivery robots and quadrupeds. The motivation of the work is that showcasing the energy savings of higher speed aids government officials in their decision-making regarding delivery robot regulations. As a result of the simulations, higher operation speed lowered the energy consumption by over 40%.

Suggested Citation

  • Jari Vepsäläinen, 2022. "Energy Demand Analysis and Powertrain Design of a High-Speed Delivery Robot Using Synthetic Driving Cycles," Energies, MDPI, vol. 15(6), pages 1-21, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2198-:d:773345
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    References listed on IDEAS

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    1. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    2. Lei, Fei & Bai, Yingchun & Zhu, Wenhao & Liu, Jinhong, 2019. "A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort," Energy, Elsevier, vol. 167(C), pages 1040-1050.
    3. Ivan EVTIMOV & Rosen IVANOV & Gergana STANEVA & Georgi KADIKYANOV, 2015. "A study on electric bicycle energy efficiency," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 10(3), pages 131-140, September.
    4. Xu, Chonggang & Gertner, George Zdzislaw, 2008. "Uncertainty and sensitivity analysis for models with correlated parameters," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1563-1573.
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