Cognitive Model of the Internal Combustion Engine 2018-01-1738

This paper describes research focused upon improved the quality of automobile engine quality. Methods and models were developed for estimating and predicting the technical condition of internal combustion engine (ICE), which provides usage of the decision support making in the search for minimum fuel consumption regimes. We developed models of multi-criterion, multiparametric optimization of energy and material-material characteristics of ICE according to the system approach.

The developed methods and models for estimating and predicting the technical state of the functionally interconnected and interacting ICE components are performed taking into account their hierarchy and topologies, energy resource used and the fuel. The cognitive methodology has been used to make engine models researches and analysis. The paper focuses on the fuzzy logic approach applying, considering the indeterminacy, incompleteness and unclear information in the engines operation processes. Cognitive, imitation and fuzzy models for estimating and predicting the technical state of ICE have been developed by the authors of this paper, which allowed to identify ICE’s most vulnerable components, set weight values, influence on fuel consumption according to the quantitative and qualitative energy interchange between ICE components.

The received results provide quality enlargement of the ICE operation and their functional components, based on the developed estimation and prediction methods of their technical condition.

The paper describes results the cross-platform software application which was implemented using the high-level Java programming language and XML markup language. Developed software allows us to provide user’s flexible interaction process with the module of the decision support system, which is based on implementation of the developed methods and models for the ICE technical condition estimating and predicting.

Usage of the developed software helped to obtain optimization results of the energy and material characteristics of the explored ICE, which allows us to find several Pareto-optimal solutions for quality criteria that affect fuel consumption for each single model. This has led to a reduction of components wear, which leads to reducing fuel consumption during ICE operation.