Adaptive Control of a Hybrid Powertrain with Map-based ECMS

Sivertsson, Martin; Sundström, Christofer; Eriksson, Lars

doi:10.3182/20110828-6-it-1002.02091

Cited by 36 publications

(38 citation statements)

References 5 publications

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“…Here, for robustness reasons, another approach is used. The strategy used in [4] is extended to fit the PHEV problem. The strategy is to adapt the equivalence factor according to a tangent function in SOC.…”

Section: Equivalence Factor Adaptationmentioning

confidence: 99%

“…The idea is that as long as the SOC is near the desired SOC the control should remain rather constant; but when the SOC approaches the limits the control needs to adapt. In [4], this is used in a HEV where the aim is to maintain the SOC around a constant level.…”

Section: Equivalence Factor Adaptationmentioning

confidence: 99%

“…This paper is an extension of [3] where the control strategy was outlined. The control is an extension of the adaptive map-based Equivalent Consumption Minimization Strategy (ECMS) developed in [4] for the PHEV problem and it is implemented for the simulator made available in the PHEV Benchmark.…”

Section: Introductionmentioning

confidence: 99%

“…This is since it is desirable to make use of all the stored energy in the battery, the engine should only be started if the driving mission exceeds the electric range of the vehicle. Therefore the control strategy in [4] is extended to also handle a time-varying SOC reference as well as estimation of initial control values from driving cycle data provided in the simulator.…”

Section: Introductionmentioning

confidence: 99%

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Design and Evaluation of Energy Management using Map-Based ECMS for the PHEV Benchmark

Sivertsson

Eriksson

2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-Plug-in Hybrid Electric Vehicles (PHEV) provide a promising way of achieving the benefits of the electric vehicle without being limited by the electric range, but they increase the importance of the supervisory control to fully utilize the potential of the powertrain. The winning contribution in the PHEV Benchmark organized by IFP Energies nouvelles is described and evaluated. The control is an adaptive strategy based on a map-based Equivalent Consumption Minimization Strategy (ECMS) approach, developed and implemented in the simulator provided for the PHEV Benchmark. The implemented control strives to be as blended as possible, whilst still ensuring that all electric energy is used in the driving mission. The controller is adaptive to reduce the importance of correct initial values, but since the initial values affect the consumption, a method is developed to estimate the optimal initial value for the controller based on driving cycle information. This works well for most driving cycles with promising consumption results. The controller performs well in the benchmark; however, the driving cycles used show potential for improvement. A robustness built into the controller affects the consumption more than necessary, and in the case of altitude variations the control does not make use of all the energy available. The control is therefore extended to also make use of topography information that could be provided by a GPS which shows a potential further decrease in fuel consumption.Re´sume´-Conception et e´valuation de la gestion de l'e´nergie en utilisant l'ECMS (strate´gie de minimisation de la consommation e´quivalente) base´e sur des cartes, afin de tester les ve´hicules hybrides e´lectriques rechargeables -Les ve´hicules e´lectriques hybrides rechargeables (PHEV, Plug-in Hybrid Electric Vehicles) repre´sentent un moyen prometteur d'obtenir les avantages des ve´hicules e´lectriques, sans eˆtre limite´s par les proble`mes d'autonomie, mais ils augmentent l'importance de la supervision e´nerge´tique, afin d'exploiter pleinement le potentiel du groupe motopropulseur. La contribution gagnante au benchmark PHEV, organise´par IFP Energies nouvelles, est de´crite et e´value´e ici. Le controˆle est re´alise´par le biais d'une strate´gie adaptative base´e sur une approche ECMS (Equivalent Consumption Minimization Strategy, strate´gie de minimisation de la consommation e´quivalente) a`base de cartographies, de´veloppe´e et mise en application dans le simulateur fourni pour le benchmark PHEV. Le controˆle mis en oeuvre s'efforce d'eˆtre aussi proche que possible d'une strate´gie « blended », tout en s'assurant que toute l'e´nergie e´lectrique est utilise´e dans une mission de conduite. Le controˆleur est adaptatif, afin de re´duire l'importance de valeurs initiales correctes, mais les valeurs initiales affectant la consommation, une me´thode est de´veloppe´e afin d'estimer la valeur initiale optimale pour le controˆleur, sur la base d'informations du cycle de conduite.Oil & Gas Science and Technology -Rev. IFP En...

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Section: Equivalence Factor Adaptationmentioning

confidence: 99%

Section: Equivalence Factor Adaptationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and Evaluation of Energy Management using Map-Based ECMS for the PHEV Benchmark

Sivertsson

Eriksson

2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…In particular, charge-sustaining or autonomous HEV implies that the battery State Of Charge (SOC) at the end of a vehicle mission is required to be as close as possible to its initial value. A mathematical formulation of such a control problem has been posed in terms of optimal control (Ambühl et al, 2007;Hofman, Steinbuch, Serrarens, & van Druten, 2008;Kim, Cha, & Peng, 2011;Serrao, Onori, & Rizzoni, 2009;van Berkel, Hofman, Vroemen, & Steinbuch, 2012) and numerous practical implementations for various architectures such as parallel (Lin et al, 2001;Musardo & Rizzoni, 2005;Pisu & Rizzoni, 2007;Salman et al, 2000;Sciarretta et al, 2004;Sivertsson, Sundström, & Eriksson, 2011), series (Anatone, Cipollone, Donati, & Sciarretta, 2005;Pisu & Rizzoni, 2005), and combined HEV (Borhan & Vahidi, 2010;Cipollone & Sciarretta, 2006;Hofman et al, 2008;Liu & Peng, 2006) have been presented.…”

Section: Introductionmentioning

confidence: 99%

A control benchmark on the energy management of a plug-in hybrid electric vehicle

Sciarretta

Serrao²,

Dewangan

et al. 2014

Control Engineering Practice

119

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a b s t r a c tA benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting-charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagin's minimum principlederived optimization tool HOT.

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