A team of researchers at Carnegie Mellon University (CMU) used a vehicle dynamics model to evaluate the effects of automated driving on EV range and battery longevity. They compared autonomous vehicles (SAE levels 4 and 5) with human-driven EVs by calculating the additional energy required to power the automated driving components, as well as the potential increase in drag from LiDAR.
They found 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. They also found that the impact of automation on battery longevity is negligible. Their paper is published in Nature Energy.
Two of the researchers had developed a physics-based vehicle dynamics model to estimate the energy demands of an EV, given a realistic driving profile. Using a realistic velocity profile with a 1-seconds temporal resolution, the model calculates the instantaneous power needed each second to overcome vehicle inertia, aerodynamic drag and road friction.
The CMU team extended the model for autonomous electric vehicles by adding the weight of the different components to the mass of the vehicle and battery pack, and increasing the drag coefficient for automated solutions with a roof-based spinning LiDAR.
If no LiDAR is used, or if solid-state LiDAR that is incorporated into the aerodynamic profile of the vehicle is used, the increase in drag is zero. They also modified the velocity profile to account for potentially smoother driving and add the computing and sensor loads at each second.
Keeping track of the total energy used, they repeated the driving profile until the battery was fully depleted. They then compared the resulting range estimates for a given battery capacity to conventional EVs.