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This first production application of closed loop “Adaptive Learning” fuel control software, further improved catalytic converter operating efficiency, by allowing three-way converters to operate with precise stoichiometric air/fuel ratio feed streams. This also eliminated the need for secondary air injection reactor (AIR) systems on those vehicles, reducing hardware complexity and cost.

Electronic fuel injection (EFI) had tighter air/fuel control than carburetion. However, injectors, sensors and the engine’s air pumping capacity (volumetric efficiency across speed and load conditions) still had individual engine calibration differences. This meant that there were slight variations in each EFI system’s ability to program the precise ideal (stoichiometric) air/fuel ratio for optimum catalytic converter operation.

Thus, the concept of adaptive learning control software was developed to provide a means to self-compensate each EFI engine control system for the small calibration variations of its individual elements. This technology dramatically reduced error in the engine’s air/fuel ratio open loop control.

Adaptive learning control used the input from the oxygen sensor, along with an appropriate transport delay time through the engine, to create a small correction factor for each operating point of the engine. It then stored each specific operating point correction factor in a speed-load table. The next time that operating point was encountered, the adjustment was included in the fuel control calculation, thereby ensuring a closer match to the ideal or stoichiometry air/fuel ratio. Every time another refinement was needed, it was added to the table until a full adjustment had been determined for each operating point. As components changed over the life of the vehicle, the factors continued to self correct to keep the precise delivery of fuel for optimum operation of the three way converter.

Following this initial introduction, fuel controllers for future generations continued to build upon and refine this self-calibrating technology. Adaptive learning later spread into transmission adaptive shift calibration to eliminate part-to-part differences that allowed even further refined automatic transmission shift feel.

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