Autonomous Electric Vehicles as Mobile Data Centers: Energy, Emissions, and Mitigation Across Deployment Eras and Grid Scenarios

The rapid advancement of autonomous electric vehicles toward Level 4 full self-driving capability introduces a new and growing energy burden: onboard artificial intelligence computers and sensors require 450 to 1200 W of continuous system power during automated operation. This paper uses a 750 W reference that spans the full Level 4 system boundary—sensing, computing, networking, power conversion, and thermal management—based on the publicly available commercial hardware specifications. The autonomy load reduces electric vehicle range by 4 to 14% depending on vehicle speed, with the largest penalty in low-speed urban service where Level 4 is deployed first. For fleets of 50, 300, and 500 million vehicles operating 3 h/day (near-term ~2030, mid-term ~2035, long-term ~2040), onboard compute energy reaches 41, 246, and 411 TWh/yr, respectively. The long-term figure approximately matches the 2024 global data centerelectricity benchmark (~415 TWh). Across five grid carbon intensities, fleet CO2 outcomes span two orders of magnitude: compute-only emissions range from ~4.5 Mt CO2e/yr (wind) to ~337 Mt CO2e/yr (coal) for the 500-million-vehicle case. Total fleet energy including propulsion reaches 1911 TWh/yr for 500 million vehicles—a load relevant to grid infrastructure planning, since unlike data centers, the EV fleet imposes a geographically distributed demand across the utility network. An integrated efficiency roadmap shows that advanced chip design, solid-state sensing, and adaptive power management can reduce per-vehicle compute power to approximately 218 W full system (71% reduction from 750 W) by 2035–40; advanced chip designs alone could target 100–130 W for the compute component.

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Publication Details

Journal
World Electric Vehicle Journal
Published
2026-11-02
DOI
https://doi.org/10.3390/wevj17110576
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
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article

Autonomous Electric Vehicles as Mobile Data Centers: Energy, Emissions, and Mitigation Across Deployment Eras and Grid Scenarios

Kaushik Rajashekara
World Electric Vehicle Journal
Electric Vehicles and Infrastructure
article

Autonomous Electric Vehicles as Mobile Data Centers: Energy, Emissions, and Mitigation Across Deployment Eras and Grid Scenarios

Kaushik Rajashekara
article en

Abstract

The rapid advancement of autonomous electric vehicles toward Level 4 full self-driving capability introduces a new and growing energy burden: onboard artificial intelligence computers and sensors require 450 to 1200 W of continuous system power during automated operation. This paper uses a 750 W reference that spans the full Level 4 system boundary—sensing, computing, networking, power conversion, and thermal management—based on the publicly available commercial hardware specifications. The autonomy load reduces electric vehicle range by 4 to 14% depending on vehicle speed, with the largest penalty in low-speed urban service where Level 4 is deployed first. For fleets of 50, 300, and 500 million vehicles operating 3 h/day (near-term ~2030, mid-term ~2035, long-term ~2040), onboard compute energy reaches 41, 246, and 411 TWh/yr, respectively. The long-term figure approximately matches the 2024 global data centerelectricity benchmark (~415 TWh). Across five grid carbon intensities, fleet CO2 outcomes span two orders of magnitude: compute-only emissions range from ~4.5 Mt CO2e/yr (wind) to ~337 Mt CO2e/yr (coal) for the 500-million-vehicle case. Total fleet energy including propulsion reaches 1911 TWh/yr for 500 million vehicles—a load relevant to grid infrastructure planning, since unlike data centers, the EV fleet imposes a geographically distributed demand across the utility network. An integrated efficiency roadmap shows that advanced chip design, solid-state sensing, and adaptive power management can reduce per-vehicle compute power to approximately 218 W full system (71% reduction from 750 W) by 2035–40; advanced chip designs alone could target 100–130 W for the compute component.

World Electric Vehicle JournalVol. 17(11)
University of Houston (US)
Openalex Percentile: Top 25%
Electric Vehicles and Infrastructure
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