Predictive kinetic energy management for an add‐on driver assistance eco‐driving of heavy vehicles

Autor: Beshah Ayalew, Keith Loiselle, Andrej Ivanco, DoHyun Daniel Yoon
Rok vydání: 2020
Předmět:
Zdroj: IET Intelligent Transport Systems. 14:1824-1834
ISSN: 1751-9578
1751-956X
Popis: This study presents a radar-based predictive kinetic energy management (PKEM) framework that is applicable as an add-on driver assistance module for a heavy vehicle with an internal combustion engine powertrain. The proposed framework attempts to minimise fuel consumption by estimating the motion of the leading vehicle from radar information and optimising the inputs to the ego vehicle in a predictive manner. The PKEM framework consists of a driver-pedal pre-filter, an interacting multiple model radar-based filter and predictor of traffic object states, and a non-linear model predictive controller. The framework is integrated with established human-driver car-following models representing various driving styles and evaluated over a set of standardised driving cycles. The authors found that the energy-saving benefits can be as much as 23% over the baseline driver-only case with minimal compromises on travel time in urban environments, while the benefits are nearly negligible on the highway cycle. The results included also show the potential trade-offs in accommodating driver-desired inputs.
Databáze: OpenAIRE
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