In a recent study published by J.D. Power, U.S. Electric Vehicle Experience (EVX) Ownership Study, range anxiety, still plays a significant role in the car buying decision process.
Interestingly, the research reveals that driver range anxiety is not necessarily based on reality. “Even though most owners drive less than the stated range of their vehicle’s battery, they still want to know that the actual battery range is close to the stated battery range,” Brent Gruber, senior director of global automotive at J.D. Power said. “It’s still about peace of mind.”
In fact, bigger and more efficient batteries have partially addressed this issue: in 2020 EV (Electric Vehicle) ranges covered between 200–500 km. Proof is also in the sales figures. Electric vehicle sales in 2020 broke all records, with a global sales increase of around 40%, and notwithstanding the crisis caused by the pandemic.
The traditional approach to range estimation has been done heuristically using the vehicle’s consumption history over a given period.
The next accuracy level is provided by enhancing the consumption history with a combination of sensor inputs and consumption curve data. Sensor inputs include temperature, number of passengers, and energy consumers in the vehicle (AC, heating, cruise control, etc.). Added to this are vehicle properties like weight, friction quotients and surface. It is all used to calculate the required kinetic energy for specific road segments.
Unfortunately, this approach has some limitations:
Consumption history does not consider where the vehicle is traveling or other important map data. If the trip does not match an existing pattern, the estimation is off.
Consumption curve data (which may be used to improve the range projection estimation by considering the speed variation given by traffic or maneuvers), requires a significant amount of time and effort to compile. Even with all that work, accuracy is typically 70% for 90% of the trips; in some cases, accuracy is less than 50% because no map data is used.
Some solutions use map data, such as elevation and road geometry, for range estimation. These yield a more accurate range projection. However, the requirements for sensor inputs and energy consumption curves increase complexity and maintenance costs and only address the needs of a specific model. Deep-level customization becomes a requirement and decreases the reusability of the solution.
The same algorithms and physics formulas are applied irrespective of driver behavior, vehicle characteristics or environment. The inputs are the only variants, and predictability varies.
These traditional models are not dynamic or self-improving. They do not learn or adapt to the driver’s behavior and calculations are based on “lab-like” conditions. These fixed models perpetuate the fears drivers have about “real range” and power surprises.
Using big data to train neural networks and provide more accurate results, the system actually learns. Training can be done using generic models based on vehicle types (including specific OEM models), and driving-style personalization.
In addition to an exceptionally reliable range estimation, there are additional benefits:
The most important use case for EV (Electric Vehicle) navigation is that of not leaving a car stranded on the road while attempting to help drivers get from A to B. Hence, all the features delivered as part of the EV (Electric Vehicle) navigation solutions depend on accurate range estimation.
Knowing how far one can go on a specific road in a specific context is critical for the success of search and navigation features. What good is having comprehensive search capabilities, if one is not sure whether it is possible to get there on the current charge? What good is it to have charging stations suggestions if one can’t reach them to charge a vehicle?
Range estimation that learns and improves over time ensures the driver is not stranded on the road unexpectedly. It also delivers increased customer satisfaction because the rest of the navigation features are working optimally.
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It delivers extensive charging station filters and coverage near the points of interest (POI’s) in the driver’s search results. This includes EV charging stations near the driver’s current position, at “safe charge level,” along route or nearby POI (Points of Interest).
This critical piece of information can help the driver to decide which POI to visit. Additional information includes the presence of a fast charger, compatibility, and EV connector dynamic data. Coverage information is provided about the surrounding POI amenities within walking distance, like rest areas, public restrooms, restaurants, malls, hotels, and more.
A comprehensive trip planner returning a navigable route(s) that best fits with the vehicle and selected trips including recommended speed for maximum battery efficiency.
Depending on destination and current charge level, single or multiple charge stops are advised as waypoints while providing detailed information such as ETA (Estimated Time of Arrival), charging-time –per-charge stop, charge-level-at-charge stop, destination, and minimum charge level required at each charge stop (if multiple charge stops are needed), to complete the trip.
It provides regular updates on range during navigation. The driver gets notifications when the range has changed due to evolving driving conditions (e.g., traffic jams, detours, change in weather conditions), which might cause a deviation from the original route. Of course, each changing variable results in additional advice on (additional) charge stops.
This delivers continuous range monitoring and battery-range visual display, along with EV-specific dynamic route guidance. Driver alerts are triggered when conditions change both externally (traffic, detours, etc.) and internally (things that draw on car power).