An SVM-based approach to develop a new control module for H2-powered engine using driver behaviour classification

In the next years, the global hydrogen vehicle market is expected to grow at a very high rate. Consequently, it is necessary for scholars and professionals to study and test specific components in order to rise motor efficiency leveraging the new features of connectivity available in smart roads. In particular, our research is focused on the developement of an engine control module driven by evaluation of usage characteristics (e.g., driving style) and "connected-to-x" scenarios using the standard engine control approach. Moreover, the module proposed enables the implementation of "fast running" models to improve the response of vehicle and make the best possible use of H2-powered engine characteristics. That said, in this paper is proposed a new approach to implement the control module, using Support Vector Machine (SVM) as the machine learning algorithm to detect driving style, and consequently modify the parameters of the engine. We choose SVM because i) it is less prone to overfitting; and ii) SVM memory efficiency enables the design of a low-cost, compact size controller board. The first step of our research, described in this paper, is to test the algorithm proposed and verify its performance using the usual machilne learning metrics. An open source dataset has been used for training and testing of our SVM-based algorithm and the promising results achieved are shown. Thìs experimental control module will be installed on an H2-powered motor on test bench to test functionality and tuning.