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Adaptive Learning

Adaptive Learning is a strategy used by O.E.M. automotive manufacturers to maintain long term tuning accuracy.  This strategy allows for continuously changing climate conditions to be constantly compensated for by the ECU. This strategy is vastly different from closed loop type control systems. While Closed Loop and Adaptive Learning work together, with Closed loop systems, the system is always starting from the same point, so if you are 10% off on your base, then your closed loop system will always have to trim that 10% out every time it hits that point in the mapping. With adaptive learning, the ECU learns this 10% error, and changes the value in the map at that point. This allows for the closed loop system to work more effectively, keeping the Closed Loop system to minimal changes.
There are several strategies with the ProEFI ECU that incorporate Adaptive learning.
Idle Control – Obtaining a proper Idle control on a warmed up engine is the easy part. The challenge arrives when that given amount of air required to obtain the desired Idle RPM changes. When the engine gets cold, the oil viscosity changes and the engine becomes harder to turn over, requiring more air to get the same idle RPM. So you now have varying base idle settings to obtain the same idle rpm under different conditions. While closed loop systems help with this condition, the errors vary greatly under the different conditions, making the closed loop system fluctuate more while trying to eliminate the error against the target. Incorporating Adaptive learning to the Idle control strategy   allows for constant adjustments to the base idle tables. This means less idle fluctuation with changing temperatures, and less setup to “dial” in the idle control.


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