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Challenges and opportunities in truck electrification revealed by big operational data

Nature Energy volume 9, pages 1427–1437 (2024)Cite this article

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Abstract

The electrification of trucks is a major challenge in achieving zero-emission transportation. Here we gathered year-long records from 61,598 electric trucks in China. Current electric trucks were found to be significantly underutilized compared with their diesel counterparts. Twenty-three per cent of electric delivery trucks and 30% of semi-trailers could achieve one-on-one replacement with diesel counterparts, while on average 3.8 electric delivery trucks and 3.6 electric semi-trailers are required to match the transportation demand that is served by one diesel truck separately. For diesel trucks that are capable of one-on-one replacement, electric trucks have 15–54% and 1–49% reductions in cost and life-cycle CO2 emissions, respectively. Enhancements in usage patterns, vehicle technologies and charging infrastructure can improve electrification feasibility, yielding cost and decarbonization benefits. Increased battery energy densities with optimized usage can make one-on-one electrification feasible for more than 85% of diesel semi-trailers. In addition, with cleaner electricity, most Chinese electric trucks in 2030 will have lower expected life-cycle CO2 emissions than diesel trucks.

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Fig. 1: Vehicle stock and usage patterns of ET fleets.
Fig. 2: Life-cycle CO2 emissions and TCO of individual ETs and DTs.
Fig. 3: Replacement rates after fleet usage normalization.
Fig. 4: Future electrification effects under different scenarios for ETs.

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Data availability

Material CO2 emission factors in China, life-cycle CO2 emission data and TCO results are available via Figshare at https://doi.org/10.6084/m9.figshare.24421210 (ref. 46). More specific datasets or materials are available from S.Z. or Y.W. upon reasonable request. Source data are provided with this paper.

Code availability

The codes that support the findings of this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

We are grateful to the National Key Research and Development Program of China (Grant No. 2022YFC3703600, Y.W.), the National Natural Science Foundation of China (52170111, S.Z.) and Energy Foundation China (Grant No. G-2310-35151, S.Z.). We thank M. Wang from Argonne National Laboratory for useful discussion. We thank A. Wang and all members from MIT Senseable City Lab for their generous help. We thank the North American Council for Freight Efficiency (NACFE) for the data and visualized results for the Run on Less – Electric Depot program.

Author information

Author notes

  1. These authors contributed equally: Pei Zhao, Shaojun Zhang.

Authors and Affiliations

  1. School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, China

    Pei Zhao, Shaojun Zhang, Fang Wang & Ye Wu

  2. Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Pei Zhao, Paolo Santi & Carlo Ratti

  3. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China

    Shaojun Zhang & Ye Wu

  4. Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing, China

    Shaojun Zhang & Ye Wu

  5. Instituto di Informatica e Telematica del CNR, Pisa, Italy

    Paolo Santi

  6. National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

    Dingsong Cui, Peng Liu, Zhaosheng Zhang & Zhenpo Wang

  7. Collaborative Innovation Center for Electric Vehicles in Beijing & National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, China

    Peng Liu & Zhenpo Wang

  8. School of Humanities and Social Sciences, Beijing Institute of Technology, Beijing, China

    Jin Liu

  9. Department ABC, Politecnico di Milano, Milan, Italy

    Carlo Ratti

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Contributions

P.Z., S.Z., P.S., C.R., Z.W. and Y.W. designed the research; D.C., P.L., Z.Z., J.L. and Z.W. prepared and pre-processed the data; P.Z., S.Z. and P.S. developed the assessment methods and performed the research; P.Z., S.Z., P.S. and Y.W. analysed the data; P.S., F.W., C.R. and Y.W. provided valuable discussions and edited the manuscript; P.Z., S.Z., P.S., C.R. and Y.W. wrote and revised the paper.

Corresponding authors

Correspondence to Shaojun Zhang, Zhenpo Wang or Ye Wu.

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Nature Energy thanks Kelly Fleming, Felipe Rodriguez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work

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Supplementary information

Supplementary Information

Supplementary Figs. 1–15, Tables 1–10 and Notes 1–4.

Source data

Source Data Fig. 1

Data on the daily mileage percentile of DTs and ETs by fleet and the annual trip number percentile of DTs and ETs by fleet.

Source Data Fig. 2

Data on life-cycle CO2 emission results for Fig. 2a and TCO results for Fig. 2b.

Source Data Fig. 3

Replacement rate modelling result under different trip mileages and increased active trips (VKT).

Source Data Fig. 4

CO2 emissions and TCO gaps between DT and ET fleets under different scenarios.

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Zhao, P., Zhang, S., Santi, P. et al. Challenges and opportunities in truck electrification revealed by big operational data. Nat Energy 9, 1427–1437 (2024). https://doi.org/10.1038/s41560-024-01602-x

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