Kalaivanan, A. and Sakthivel, G., "Self Tuning Genetic Algorithm to Achieve Maximum Thermal Efficiency by Monitoring Combustion Characteristics with Vibration/Acoustic Sensors," SAE Technical Paper 2017-01-2283, 2017.
Electronic Fuel Injection Systems have revolutionised Fuel Delivery and Ignition timing in the past two decades and have reduced the Fuel Consumption and Exhaust Emissions, ultimately enhancing the Economy and Ecological awareness of the engines. But the ignition/injection timing that commands the combustion is mapped to a fixed predefined table which is best suited during the stock test conditions. However continuous real time adjustments by monitoring the combustion characteristics prove to be highly efficient and be immune to varying fuel quality, lack of transient performance and wear related compression losses.For developing countries, Automotive Manufacturers have been Tuning the Ignition/Injection timing Map assuming the worst possible fuel quality. Conventional knock control system focus on engine protection only and doesn't contribute much in improving thermal efficiency. Predefined Ignition tables fail to produce good thermal efficiency during hard acceleration due to their inability to advance for the acceleration. IC Engines wear over time leading to compression losses and a reduced cranking pressure. This reduction in the effective compression ratio raises the need for adjusting the Ignition/Injection Timing.A closed loop ignition/injection timing by monitoring combustion characteristics can compensate for such issues and maintain a consistent thermal efficiency in all operating conditions. The algorithm implements genetic mutation approach to create a correction factor for ignition timing by analysing signals from a knock sensor or an acoustic sensor. It also takes into account, the Acceleration Gradient, MAP, RPM and Air-Fuel Ratio in computing the correction factor. The computation is also optimised to execute with in an engine cycle even at high RPM.