Extending EV Range with AV Programming
Intelligent programming of autonomous electric vehicles offers potentially big energy savings, according to a study by IAV.
Increasing the range of electric vehicles (EV) in a practical cost-effective way has become a primary challenge for the industry. However, current efforts to increase EVs’ market acceptance, via advertising, typically focus on the exhilaration that comes from mashing the accelerator and enjoying the launch torque compared with a gasoline engine. An alternative approach, with customer appeal of its own, would be to increase the vehicle’s autonomous driving capability.
This was proposed by Jonas Gutsche, an advanced-development engineer with IAV, at the recent SAE Thermal Management Systems Symposium. He said the vehicle could produce real-world gains in range, perhaps 50% in urban driving and 13% in highway use. Most battery electric vehicles (BEV) provide a choice of driving styles (economy, normal and sportive), but the mode selected is still up to the driver.
Three aspects of an AV trip
The IAV study outlined by Gutsche looked at the three aspects of a trip for a “highly automated vehicle” (HAV)—essentially SAE autonomous levels 4 to 5. The first, pre-processing, uses the map data to determine the route. The second aspect, “Macro” factors, must be considered along with the pre-processing. They include integration of the details from the route itself—speed limits, required turns, intersections and time factors.
The pre-programming also must consider the driving dynamics and driving/rolling resistance (drag) of the vehicle, and the changes in the battery pack state of charge. As an example, this last item is affected by and itself affects everything from route selection to availability and usability of regenerative braking and any other recuperative system in the car.
The third aspect is how to adjust for the micro factors – “while driving” inputs that often change instantly – not precisely predictable during route planning. They include any car-to-infrastructure transmissions (including from traffic control devices), sensor data and camera readings from on-board devices and weather changes.
Gutsche pointed out the most efficient deceleration is coasting – no active reduction in speed and no waste of energy. But unless a battery pack’s state of charge (SoC) is below 90%, there won’t be energy recovery from regeneration, and in-city driving presents fewer coasting opportunities. In his SAE presentation, Gutsche offered an energy-efficient urban acceleration-and-braking approach, noting that if conditions don’t permit the calculated drive, the vehicle should be operated for maximum efficiency (slow, light acceleration) until opportunities for the anticipated strategies emerge.
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