Trade-offs between automation and light vehicle electrification


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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 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

To allow readers to engage with our research, we have created a web applet (available at 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.


  1. 1.

    Taiebat, M., Brown, A. L., Safford, H. R., Qu, S. & Xu, M. A review on energy, environmental, and sustainability implications of connected and automated vehicles. Environ. Sci. Technol. 52, 11449–11465 (2018).

    Google Scholar 

  2. 2.

    Stephens, T. et al. Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles (National Renewable Energy Laboratory, 2016).

  3. 3.

    Study of the Potential Energy Consumption Impacts of Connected and Automated Vehicles (US Energy Information Administration, US Department of Energy, 2017).

  4. 4.

    Offer, G. J. Automated vehicles and electrification of transport. Environ. Sci. Technol. 8, 26–30 (2015).

    Google Scholar 

  5. 5.

    Hawkins, A. J. Not all of our self-driving cars will be electrically powered—here’s why. The Verge (2017).

  6. 6.

    Sripad, S. & Viswanathan, V. Evaluation of current, future, and beyond Li-ion batteries for the electrification of light commercial vehicles: challenges and opportunities. J. Electrochem. Soc. 164, E3635–E3646 (2017).

    Article  Google Scholar 

  7. 7.

    Ouster. OS1 Datasheet (2019).

  8. 8.

    Chassis Systems Control: Mid-range Radar Sensor (MRR) for Front and Rear Applications (Robert Bosch, 2015).

  9. 9.

    Point Grey Dragonfly2 0.8 MP Mono FireWire 1394a Board Level (Sony ICX204) (Point Grey, 2018).

  10. 10.

    Velodyne HDL-64E (Velodyne, 2019).

  11. 11.

    Carroll, A. et al. An analysis of power consumption in a smartphone. In Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference Vol. 14, 21–21 (USENIX Association, 2010).

  12. 12.

    Sepulcre, M., Gozalvez, J. & Hernandez, J. Cooperative vehicle-to-vehicle active safety testingunder challenging conditions. Transp. Res. Part C Emerg. Technol. 26, 233–255 (2013).

    Article  Google Scholar 

  13. 13.

    Gawron, J. H., Keoleian, G. A., de Kleine, R. D., Wallington, T. J. & Kim, H. C. Life cycle assessment of connected and automated vehicles: sensing and computing subsystem and vehicle level effects. Environ. Sci. Technol. 52, 3249–3256 (2018).

    Article  Google Scholar 

  14. 14.

    Liu, S., Tang, J., Zhang, Z. & Gaudiot, J.-L. Computer architecture for autonomous driving. Computer 50, 18–25 (2017).

    Article  Google Scholar 

  15. 15.

    Lin, S.-C. et al. The architectural implications of autonomous driving: constraints and acceleration. In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems 751–766 (ACM, 2018).

  16. 16.

    Nvidia Drive AGX Pegasus (Nvidia, 2018).

  17. 17.

    Walz, E. Tesla shares details of its new self-driving chipset at ‘Autonomy Investor Day’. FutureCar (2019).

  18. 18.

    Chowdhury, H., Alam, F., Khan, I., Djamovski, V. & Watkins, S. Impact of vehicle add-ons on energy consumption and greenhouse gas emissions. Procedia Eng. 49, 294–302 (2012).

    Article  Google Scholar 

  19. 19.

    Chen, Y. & Meier, A. Fuel consumption impacts of auto roof racks. Energy Policy 92, 325–333 (2016).

    Article  Google Scholar 

  20. 20.

    Liu, J., Kockelman, K. M. & Nichols, A. Anticipating the emissions impacts of smoother driving by connected and autonomous vehicles, using the MOVES model. In Transportation Research Board 96th Annual Meeting (2016).

  21. 21.

    Prakash, N., Cimini, G., Stefanopoulou, A. G. & Brusstar, M. J. Assessing fuel economy from automated driving: influence of preview and velocity constraints. In ASME 2016 Dynamic Systems and Control Conference (American Society of Mechanical Engineers, 2016).

  22. 22.

    Mersky, A. C. & Samaras, C. Fuel economy testing of autonomous vehicles. Transp. Res. Part C Emerg. Technol. 65, 31–48 (2016).

    Article  Google Scholar 

  23. 23.

    Gao, Z. et al. Evaluation of electric vehicle component performance over eco-driving cycles. Energy 172, 823–839 (2019).

    Article  Google Scholar 

  24. 24.

    Certificate Summary Information Report, Tesla Motors: Model 3 Long Range, 02/28/2019 (United States Environmental Protection Agency, 2019).

  25. 25.

    Templeton, B. Elon Musk’s war on LIDAR: who is right and why do they think that? Forbes (2019).

  26. 26.

    Morgan, M. G., Henrion, M. & Small, M. Uncertainty: a Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis (Cambridge University Press, 1992).

  27. 27.

    Harlow, J. E. et al. A wide range of testing results on an excellent lithium-ion cell chemistry to be used as benchmarks for new battery technologies. J. Electrochem. Soc. 166, A3031–A3044 (2019).

    Article  Google Scholar 

  28. 28.

    Srinivasan, V. & Wang, C.-Y. Analysis of electrochemical and thermal behavior of Li-ion cells. J. Electrochem. Soc. 150, A98–A106 (2003).

    Article  Google Scholar 

  29. 29.

    Fang, W., Kwon, O. J. & Wang, C.-Y. Electrochemical–thermal modeling of automotive Li ion batteries and experimental validation using a three-electrode cell. Int. J. Energy Res. 34, 107–115 (2010).

    Article  Google Scholar 

  30. 30.

    Smith, K. & Wang, C.-Y. Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles. J. Power Sources 160, 662–673 (2006).

    Article  Google Scholar 

  31. 31.

    Anderson, J. M. et al. Autonomous Vehicle Technology: a Guide for Policymakers (Rand, 2014).

  32. 32.

    Tesla Model 3. Tesla (2019).

  33. 33.

    Yang, X.-G., Leng, Y., Zhang, G., Ge, S. & Wang, C.-Y. Modeling of lithium plating induced aging of lithium-ion batteries: transition from linear to nonlinear aging. J. Power Sources 360, 28–40 (2017).

    Article  Google Scholar 

  34. 34.

    AAA Foundation for Traffic Safety. American driving survey: methodology and year 1 results, May 2013–May 2014. AAA (2015).

  35. 35.

    Annual vehicle distance traveled in miles and related data – 2017(1). Federal Highway Administration (2017).

Download references


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.

Author information




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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

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

Source data

Source Data Fig. 1

Data to generate Fig. 1a–d

Source Data Fig. 2

Data to generate Fig. 2a,b

Source Data Fig. 3

Data to generate Fig. 3a,b

Source Data Fig. 4

Data to generate Fig. 4a,b

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mohan, A., Sripad, S., Vaishnav, P. et al. Trade-offs between automation and light vehicle electrification. Nat Energy (2020).

Download citation