Adaptive air-fuel ratio control for combustion engines

University essay from Linköpings universitet/Institutionen för systemteknik

Author: Eva Lundin; [2010]

Keywords: ;


Around the world, vehicle emission regulations become stricter, increasing exhaust emission demands. To manage these rules and regulations, vehicle manufacturers put a lot of effort into minimizing the exhaust emissions. The three-way catalytic converter was developed, and today it is the most commonly used device to control the exhaust emissions.

To work properly the catalytic converter needs to control the air-fuel mixture with great precision. This then increases the demands on the engine management systems, causing them to become more complex. With increased complexity, the time effort of optimizing parameters has grown drastically, hence increasing development costs. In addition to this, operating conditions change due to vehicles age, requiring further optimization of the parameters while running.

To minimize development cost and to control the air-fuel mixture with great precision during an engines full life span, this master thesis proposes a self-optimized system, i.e. an adaptive system, to control the air-fuel mixture.

In the suggested method, the fuel injection to the engine is controlled with help of a linear lambda sensor, which measures the air-fuel mixture. The mapping from injection to measured air-fuel mixture forms a nonlinear system. It can be approximated as a linear function at static engine operating points, allowing the system at each static point to be modelled as a first order system with long time delay. To enable utilization over full operating area, and not only in static point, the controller uses large maps, so called gain-scheduling maps, to change control parameters.

The tested controller is model based. It uses an Otto-Smith Predictor and a feed forward connection of target air-fuel. The model parameters in the controller are updated while driving and the adaptation method used is based on a least squares algorithm.

The performance of the adapted controller and the adaptation method is tested in both simulation environment and in vehicle, showing good potential.

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