Emission constrained fuel optimal vehicle control with route lookahead

Polterauer, Philipp; Re, Luigi del

doi:10.23919/acc.2018.8431896

Cited by 8 publications

(7 citation statements)

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“…Through discretizing also cost function and constraints one obtains a form of the ecodriving problem perfectly fitting (DPOCP). Thus an optimal solution can be computed through applying the DP algorithm, as in [1]. From here on the solutions obtained using this approach will be refereed to as (EcoDPc).…”

Section: Recasting the Ecodriving Problem Into The Nmpc Frameworkmentioning

confidence: 99%

“…The rise of electrified vehicles is a clear sign for the interest in cleaner transportation, however many transportation task are currently and will still be carried out by conventionally powered vehicles. Therefore, the efficient operation, often referred to as ecodriving, for conventional vehicles is an important topic, as also seen by the numerous publications, e.g., [1], [2] and [3] to name just a few of them.…”

Section: Introductionmentioning

confidence: 99%

“…Since fuel efficiency is especially important for heavy duty vehicles,in [7] the authors study fuel optimal control using road grade look ahead for that type. Therein special attention is paid to the gear shift modeling and the differences between energy 1 Philipp Polterauer and Luigi del Re are with the Institute for Design and Control of Mechatronical Systems, Johannes Kepler University Linz, 4040 Linz, Austria (philipp.polterauer@jku.at, luigi.delre@jku.at) 2 Gian Paolo Incremona and Patrizio Colaneri are with Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy (gianpaolo.incremona@polimi.it,patrizio.colaneri@polimi.it) based optimal control formulation and classic dynamical one are investigated.…”

Section: Introductionmentioning

confidence: 99%

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A Switching Nonlinear MPC Approach for Ecodriving

Polterauer

Incremona

Colancri

et al. 2019

2019 American Control Conference (ACC)

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In recent years many works focusing on improved vehicle fuel efficiency through advanced control have been carried out, reflecting the high interest in ecodriving of vehicles. Although many studies have shown the potential that optimal control based ecodriving can offer, these solution are often difficult to be translated into online control strategies, one of the reasons being the complexity of the optimal control problem and therefore the computational burden.To cope with this a novel online approach, based on switching Nonlinear Model Predictive Control (NMPC), is proposed. The NMPC strategy is developed for the case of conventional vehicles, where gear shifting and longitudinal dynamics are controlled. It is shown that our proposal can operate in real time, while recovering most of the performance achievable by an offline optimal solution.The development of the method is described in detail and its performance is analyzed. The results show that the proposed NMPC can successfully solve the ecodriving task and seems a good compromise between computational burden and performance suitable for field implementation.

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Section: Recasting the Ecodriving Problem Into The Nmpc Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Switching Nonlinear MPC Approach for Ecodriving

Polterauer

Incremona

Colancri

et al. 2019

2019 American Control Conference (ACC)

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“…Discrepancies between results in simulation and real conditions as observed in Polterauer and Del Re 30 motivate experimental validation. However, in the studies mentioned above, experimental analysis is provided only in Refs.…”

Section: Introductionmentioning

confidence: 88%

Hardware-in-the-loop exploration of energy versus emissions trade-off in eco-following scenarios for connected automated vehicles

Huang

Salehi

Stefanopoulou

et al. 2022

International Journal of Engine Research

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Studies on eco-driving have mostly taken an energy-centric view and considered driving performance, while less attention has been paid on emissions behavior. This work extends in an experimentally verified way our understanding of the trade-offs among fuel economy, driving aggressiveness, and, especially, emissions in connected automated diesel-powered vehicles. Experiments are performed with a 6.7-L Ford Powerstroke diesel engine, a urea-SCR based NOx aftertreatment system, and a full model for a Ford F250 medium-duty truck in the loop to provide realistic assessment of fuel consumption, tailpipe emissions, and driving style performances. An energy and emissions conscious speed planner is leveraged to follow the traffic. This planner offers flexibility in prioritizing energy or emissions while satisfying user-defined headway constraints, and thus allows exploration of different calibrations in a unified way. Results show how various calibrations of the flexible leader following policy yield 8%–14% decrease in total fuel consumption and 64%–70% decrease in tailpipe emissions compared with a strictly constrained following policy.

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“…On a country road (L1501 between Linz and Altenberg, AT) various drives were performed to measure trajectories of the vehicle. The initial purpose of these test drives was to create models for eco-driving control [31]. The drivers were not aware that their behavior would be analyzed also in terms of safety, leading to non-biased and natural behavior.…”

Section: Data Collectionmentioning

confidence: 99%

Probabilistic Prediction of Driving Behavior on Country Roads

Sovtic

Adelberger

Wang

2022

2022 European Control Conference (ECC)

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Advanced Driver Assistance Systems (ADAS) are already an important contributor to safety in conventional vehicles and thus in road traffic. This is obviously necessary, as in Austria in 2020, almost 99% of all accidents with personal injury were presumably caused by misconduct of involved persons or impairments [1]. Furthermore, country roads seem to be a safety-relevant environment, as they account for more than 50% of the fatalities in road traffic in Austria.Im nächsten Schritt wird der Einfluss der Identifikationsstrategie auf die Prädiktionsergebnisse betrachtet, wobei für die Identifikation ein Maß für die "Reichhaltigkeit" eines Trainingsdatensatzes eingeführt wird. Es zeigt sich, dass die "Reichhaltigkeit" als auch der Umfang des Identifikationsdatensatzes die Qualität der Prädiktionsmodelle beeinflusst.Schließlich wird ein ausgewähltes Bayes'sches Netzwerk in Simulation für einen Abstandsregeltempomat (ACC) verwendet, um das Verhalten des vorausfahrenden Fahrzeugs vorherzusagen. Der ACC wird als optimal-control Problem formuliert und im Rahmen eines MPC gelöst, während die Validierung auf einem Maß für den Kraftstoffverbrauch und Fahrkomfort basiert. Es zeigt sich bei der Prädiktion mit dem Bayes'schen Netz eine Verbesserung des Kraftstoffverbrauchs und Fahrkomforts im Vergleich zu anderen Prädiktionsmethoden, die keine topologischen Eigenschaften berücksichtigen.

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Emission constrained fuel optimal vehicle control with route lookahead

Cited by 8 publications

References 10 publications

A Switching Nonlinear MPC Approach for Ecodriving

A Switching Nonlinear MPC Approach for Ecodriving

Hardware-in-the-loop exploration of energy versus emissions trade-off in eco-following scenarios for connected automated vehicles

Probabilistic Prediction of Driving Behavior on Country Roads

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