Current calibration methods generate a static tabular relationship between the optimal values of the controllable variables and steady-state operating points or specific driving conditions (e.g., vehicle speed profiles for highway and city driving). This relationship is incorporated into the electronic control unit (ECU) of the engine to control engine operation. While the engine is running, values in the tabular relationships are interpolated to provide the values of the controllable variables for each engine operating point. These calibration methods, however, seldom guarantee optimal engine operation for common driving habits (e.g., stop and go driving, rapid acceleration, or rapid braking). Each individual driving style is different and rarely meets those driving conditions of testing for which the engine has been calibrated to operate optimally. Consumers often complain that their new cars simply cannot achieve the gas mileage estimate displayed on the window sticker or featured in advertisements.
University of Michigan researchers have developed a system for making the engine of a vehicle an autonomous intelligent system capable of learning the optimal values of the controllable variables in real time while the driver drives the vehicle based on individual operating style.
Applications and Advantages
- Automobile design
- Achieves the minimum possible fuel consumption and pollutant emissions