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Predictive kinetic energy management for an add‐on driver assistance eco‐driving of heavy vehicles
Author(s) -
Yoon DoHyun Daniel,
Ayalew Beshah,
Ivanco Andrej,
Loiselle Keith
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2020.0380
Subject(s) - powertrain , driving cycle , advanced driver assistance systems , automotive engineering , model predictive control , energy consumption , radar , fuel efficiency , controller (irrigation) , computer science , engineering , simulation , control (management) , torque , artificial intelligence , electric vehicle , physics , quantum mechanics , telecommunications , agronomy , power (physics) , electrical engineering , biology , thermodynamics
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.

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