It's a more precise version of those qualitative ratings. Instead of "Excellent" being everything above 90%, additional information is available to buyers in the form of a 100-point rating. Here’s a helpful way to think of it.
It's not uncommon for us to see newer used cars with max ranges that meet or exceed 100% of the original range for the make, model, and battery size. This is mostly due to variations in battery pack and vehicle configuration. For the sake of simplicity, in the badge we cap these range scores at "100 Plus" to signify an outstanding range expectation. In the reports, we do display the uncapped value, which can exceed 100.
Auto manufacturers provide different levels of information about range on their dashboards based on recent driving habits. This is not a good indicator of future range, especially in different climates, which have a measurable impact on range. In a Recurrent Report, the vehicle range will reflect the common range at temperatures experienced in your climate and how that could change as the vehicle ages.
Another unique value is that Recurrent’s data spans OEMs to recognize details that a single manufacturer may not see or share. For Recurrent, our only objective is transparency for vehicle buyers and sellers.
Recurrent Reports include the information that buyers need to feel confident in their purchase. A buyer could be a Used Car Manager at auction or a consumer purchasing an EV.
Here are some examples of critical used EV information found in the Recurrent Report:
Recurrent currently supports 70% of all used EVs that transact on the used market in the US. As new models are released, we begin to monitor their battery degradation then release reports when we have a high degree of confidence in our range degradation modeling.
Specific models today include:
* Tesla Model S 60/60D not scored due to the limited unit production, Tesla Model Y Standard Range not scored due to a limited production - only 2.2% of models.
No. After capturing millions of data points on EV range degradation, we rely on that data to produce a statistically probable range estimate. How that vehicle is ultimately used, where it resides, and how it’s charged may all contribute to ranges that varies from our prediction.
This is the same as how a ‘lead foot’ driver in a combustion engine (ICE) vehicle returns lower MPG or how altitude impacts a naturally aspirated vehicle more than its forced induction counterpart.