Trade-offs between automation and light vehicle electrification

Abstract

Weight, computing load, sensor load and possibly higher drag may increase the energy use of automated electric vehicles relative to human-driven electric vehicles, although this increase may be offset by smoother driving. Here, we use a vehicle dynamics model to evaluate the trade-off between automation and electric vehicle range and battery longevity. We find that automation will likely reduce electric vehicle range by 5–10% for suburban driving and by 10–15% for city driving. The effect on range is strongly influenced by sensor drag for suburban driving and computing loads for city driving. The impact of automation on battery longevity is negligible. While some commentators have suggested that the power and energy requirements of automation mean that the first automated vehicles will be gas–electric hybrids, our results suggest that this need not be the case if automakers can implement energy-efficient computing and aerodynamic sensor stacks.

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Fig. 1: EV and AEV velocity and power profiles.
Fig. 2: Histogram of range results for composite and city drive profiles for a Tesla Model 3 with an 80 kW h battery pack.
Fig. 3: Results of the MC simulation of AEV range for different EV models.
Fig. 4: Battery degradation results for our base EV and AEV for the composite drive cycle.

Data availability

All underlying data are publicly available at https://github.com/battmodels/Automation-EV-Range. Source data are provided with this paper.

Code availability

The custom code for the MC simulation of the physics-based model presented in the paper is publicly available on GitHub at https://github.com/battmodels/Automation-EV-Range.

To allow readers to engage with our research, we have created a web applet (available at https://tinyurl.com/avrange) that allows users to select different combinations of radar, computing and LiDAR load, cameras, energy savings and EVs, to assess the effect of different assumptions about automation on vehicle range. The input files for the battery degradation simulations on AutoLion-ST v6.3, Build 2 will be provided on request. Source data are provided with this paper.

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Acknowledgements

This work was supported by the Carnegie Mellon University (CMU) College of Engineering, Department of Engineering and Public Policy; Scott Institute for Energy Innovation; Center for Climate and Energy Decision Making (SES-1463492; through a cooperative agreement between the National Science Foundation and CMU); the Block Center for Technology and Society at CMU; Technologies for Safe and Efficient Transportation University Transportation Center; and Mobility21, A United States Department of Transportation National University Transportation Center. A.M. thanks A. Dongare and A. Bhat at CMU for their helpful comments on the power demands of AV computing platforms.

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A.M., S.S., P.V. and V.V. designed the research and conceived the paper; A.M., S.S., P.V. and V.V. developed the physics-based model for vehicle energy use; S.S. and V.V. developed the battery degradation model; A.M., S.S., V.V. and P.V. performed the analysis and created the figures; and A.M., S.S., P.V. and V.V. wrote the paper.

Corresponding author

Correspondence to Venkatasubramanian Viswanathan.

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Supplementary Notes 1–4, Figs. 1–10 and Table 1.

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Mohan, A., Sripad, S., Vaishnav, P. et al. Trade-offs between automation and light vehicle electrification. Nat Energy (2020). https://doi.org/10.1038/s41560-020-0644-3

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