Abstract
This paper presents a novel framework to enable automatic re-training of the supervisory powertrain control strategy for hybrid electric vehicles using supervised machine learning. The aim of re-training is to customize the control strategy to a user-specific driving behavior without human intervention. The framework is designed to update the control strategy at the end of a driving task. A combination of dynamic programming and supervised machine learning is used to train the control strategy. The trained control strategy denoted as SML is compared to an online-implementable strategy based on the combination of the optimal operation line and Pontryagin’s minimum principle denoted as OOL-PMP, on the basis of fuel consumption. SML consistently performed better than OOL-PMP, evaluated over five standard drive cycles. The EUDC performance was almost identical while on FTP75 the OOL-PMP consumed 14.7% more fuel than SML. Moreover, the deviation from the global benchmark obtained from dynamic programming was between 1.8% and 5.4% for SML and between 5.8% and 16.8% for OOL-PMP. Furthermore, a test-case was conducted to emulate a real-world driving scenario wherein a trained controller is exposed to a new drive cycle. It is found that the performance on the new drive cycle deviates significantly from the optimal policy; however, this performance gap is bridged with a single re-training episode for the respective test-case.
Highlights
In the late 1970s, the European Union (EU) established the link between air quality and automotive emissions, thereby setting in motion policies to reduce air pollution
It is evident from this comparison that the supervised machine learning (SML) controller almost perfectly tracks the dynamic programming (DP) control strategy, resulting in a 2.6% loss in optimality for the NEDC
The results show that the learned control strategy (SML) outperforms the conventional online implementable strategy (OOL-Pontryagin’s minimum principle (PMP)), tested over several standard drivecycles in terms of fuel economy
Summary
In the late 1970s, the European Union (EU) established the link between air quality and automotive emissions, thereby setting in motion policies to reduce air pollution. In 1992, the Euro norm for passenger cars was introduced that set a ceiling for concentration of pollutants [1]. These norms are made more stringent with time [2] and enforces companies to adopt more efficient automotive powertrains. This can be illustrated with the growth in hybrid electric vehicle (HEV) market share and the estimated increase in sales over the decade [3]. In line with the efforts to improve overall powertrain efficiency, significant strides have been made in transmission development. The continuously variable transmission (CVT) with a steel pushbelt is predicted to achieve an efficiency of 97% [4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
