Online Identification of Vehicle Driving Conditions Using Machine-Learned Clusters

Abstract

<div class="section abstract"><div class="htmlview paragraph">Modern electrified vehicles rely on drivers to manually adjust control parameters to modify the vehicle's powertrain, such as regenerative braking strength selection or drive mode selection. However, this reliance on infrequent driver input may lead to a mismatch between the selected powertrain control modifiers and the true driving environment. It is therefore advantageous for an electric vehicle's powertrain controller to make online identifications of the current driving conditions. This paper proposes an online driving condition identification scheme that labels drive cycle intervals collected in real-time based on a clustering model, with the objective of informing adaptive powertrain control strategies. HDBSCAN and K-means clustering models are fitted to a data set of drive cycle intervals representing a full range of characteristic driving conditions. The cluster centroids are recorded and used in a vehicle controller to assign driving condition identification labels to the most recently recorded interval of vehicle data. The accuracy of the driving condition identifications of each model is compared by deploying the online identification scheme on the powertrain controller of an electrified vehicle and performing a real-world drive cycle of known driving conditions. The HDBSCAN clusters resulted in superior online driving condition identifications compared to alternative schemes. The main contribution of this paper is the novel application of clustering in an online identification scheme for use in a real-world embedded vehicle controller. By enabling accurate online identification of driving conditions, this approach can improve the powertrain control strategies of electrified vehicles and enhance the driving experience. Future research can leverage the online identification of driving conditions and explore the use of subsequent adaptive control schemes for reducing energy consumption, enhancing safety, and advancing the development of intelligent transportation systems.</div></div>