Trucking Data

Using big data to plan for the future of electric trucks

Heavy-duty vehicles account for approximately 30% of transport C02 emissions globally.

We explore the challenges of heavy-duty truck electrification in efforts to achieve zero-emission transport.

Truck Analysis



Truck Diagram

We analyzed more than 61,000 electric trucks in China and California, categorized by their vocation and gross vehicle weight. We compare key differences between the usage, total cost of ownership (TCO), and (CO2) emissions between electric and diesel trucks.




Normalization & Optimization



Truck Diagram

The low usage intensity was identified as a significant operational challenge for deployed electric trucks. If ET drivers keep current usage patterns, on average 3.8 electric delivery trucks and 3.6 electric semi-trailers are needed to replace one curent diesel counterpart.


We consider future electrification optimizations and their effects on the TCO and CO2 emissions of electric trucks, compared to diesel, in order to improve electric truck usage and reduce life-cycle emissions.

LD

HD

MD

Trucking Data

This study gathers year-long data from 61,598 electric trucks in China and California in 2020-2021, evaluating their real-world performance, cost, and decarbonization effects. The dataset reveals significant underusage of current (~2020) electric trucks when compared to their diesel counterparts.

The findings display the significance of optimizing usage patterns, greener electricity, and battery improvement for electric trucks. Also highlighting the importance of large fine-grained datasets to support decision-making in global sustainable energy transition.

Team

MIT Senseable City Lab

  • Carlo Ratti Director
  • Pei Zhao Research Lead
  • Paolo Santi Research
  • Fábio Duarte Design Manager
  • Elizabeth McCaffrey Visualization & Web

Tsinghua University

  • Shaojun Zhang Research
  • Fang Wang Research
  • Ye Wu Research

    • Publications

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    The material on this website can be used freely in any publication provided that:

    • it is duly credited as a project by the MIT Senseable City Lab
    • a PDF copy of the publication is sent to senseable-press@mit.edu

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