Simulating EVE-Ai™ Velocity Recommendations Module: Tesla Model S P70D in Urban/Highway Drive Cycle
In the continued development of Electra’s EVE-Ai™ 360 Adaptive Controls for battery packs, the Electra Team completed a simulated case study to test the EVE-Ai: Velocity Recommendations module on a Tesla Model S. Compared to the original, real-world driving data, the Velocity Recommendations extended range by 28% using the same vehicle along the same driving route, showing a way forward for battery pack performance improvement using intelligent software systems.
The original driving data was collected from a mixed urban/highway drive in Phoenix, AZ, USA with a total driving distance of 92.69 miles (149.17 kilometers) and totaled 92 minutes in drive time. In simulating the same drive cycle with Electra’s EVE-Ai: Velocity Recommendations control software, the goal was to maximize efficiency without increasing the drive time over the same route. Thus, the simulated drive remained within 20% of the speed limit at all times while holding the drive time constant and keeping acceleration at less than 1.5G.
The results of this simulation significantly increased efficiency by prioritizing battery pack discharge strategies while still keeping the aggressive drive time preferred by the driver on this particular route. The EVE-Ai simulated drive had a total trip efficiency of 391 Wh/mile compared to the 502 Wh/mile of the original data. This converts to 28% range extension using EVE-Ai: Velocity Recommendations on this simulated trip comparison alone.
It should be noted that the user in the original driving data routinely traveled up to 90 mph (144.84 km/hr) with sharp acceleration and deceleration, causing a poor Wh/mile trip efficiency. The same Tesla model averages 310 Wh/mile on a standard EPA urban/highway drive cycle. Even with this aggressive original data, Electra was able to simulate a trip that reached the destination within the original drive time while significantly improving drive efficiency. With the success of this simulation, applying the same Velocity Recommendations module to an average driver and drive cycle - or another real-world, imperfect driving data set - can maximize trip efficiency well below average.
Adaptive battery pack controls are essential additions to the growing software infrastructure of electric vehicles. When multiplied over a vehicle’s lifetime of trips, Velocity Recommendations extend range in the thousands of miles. EVE-Ai 360 Adaptive Controls offer 6 individual modules, including Velocity Recommendations. When all 6 are installed simultaneously, the software solution can improve efficiency in range, lifetime, and charging strategy using 360° of data inputs for intelligently enhanced battery pack performance in every drive cycle.
Ready to model efficiency improvements for your driving data with EVE-Ai™: Velocity Recommendations? Contact us here to speak with an Electra representative, or read more about EVE-Ai™ 360 Adaptive Controls and Electra’s technology.