Intelligent vehicle drive mode which predicts the driver behavior vector to augment the engine performance in real-time

In this article, a novel drive mode, intelligent vehicle drive mode (IVDM), was proposed, which augments the vehicle engine performance in real-time. This drive mode predicts the driver behavior vector (DBV), which optimizes the vehicle engine performance, and the metric of optimal vehicle engine performance was defined using the elements of engine operating point (EOP) and heating ventilation and air conditioning system (HVAC). Deep learning (DL) models were developed by mapping the vehicle level vectors (VLV) with EOP and HVAC parameters, and the trained functions were utilized to predict the future states of DBV reflecting augmented vehicle engine performance. The iterative analysis was performed by empirically estimating the future states of VLVin the allowable range of DBV and was fed into the DL model to predict the performance vectors. The defined vehicle engine performance metric was applied to the predicted vectors, and thus optimal DBV is the instantaneous output of the IVDM. The analytical and validation techniques were developed using field data obtained from General Motors Inc., Warren, Michigan. Finally, the proposed concept was quantified by analyzing the instantaneous engine efficiency (IEE) and smoothness measure of the instantaneous engine map (IEM).

Automated summary

In this article, a novel drive mode called Intelligent Vehicle Drive Mode (IVDM) is proposed to enhance vehicle engine performance in real-time. The drive mode predicts driver behavior vectors and optimizes engine performance based on engine operating point and HVAC parameters. Deep learning models are used to map vehicle level vectors to engine and HVAC parameters, allowing for the prediction of future driver behavior. The proposed concept is quantified using field data and analyzed based on engine efficiency and smoothness of the engine map.