Supervisory control of hybrid powertrains: An experimental benchmark of offline optimization and online energy management

Chasse, Alexandre; Sciarretta, Antonio

doi:10.1016/j.conengprac.2011.04.008

Cited by 71 publications

(37 citation statements)

References 17 publications

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“…The results for the benchmark tests are shown in Table 4. For both tested cycles the controller performs relatively well and is close to the solution predicted by HOT, described in [5]. The results are within 3.6% in fuel consumption.…”

Section: Tablesupporting

confidence: 73%

“…A common strategy when using ECMS is to adapt the equivalence factor according to an affine function of the SOC error [2,5]. Here, for robustness reasons, another approach is used.…”

Section: Equivalence Factor Adaptationmentioning

confidence: 99%

“…To achieve optimal results using ECMS the optimal value of an equivalence factor needs to be found [5]. However, this optimal value is driving cycle-specific and has to be approximated online.…”

Section: Introductionmentioning

confidence: 99%

“…Both approaches use a cycle independent-equivalence factor and a correction based on the deviation of SOC from its reference value, denoted SOC error. In [5], the correction is a linear function in SOC error. In [6], the equivalence factor is corrected with a product of two terms, a cubic function of the SOC error and tanh of the low-pass filtered SOC error.…”

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: Tablesupporting

confidence: 73%

“…A common strategy when using ECMS is to adapt the equivalence factor according to an affine function of the SOC error [2,5]. Here, for robustness reasons, another approach is used.…”

Section: Equivalence Factor Adaptationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…The control of the power flows among these devices is commonly referred to as the energy management or supervisory control [1]. There are many approaches [2] to design an energy management strategy, covering heuristic approaches [3][4][5][6] as well as optimization-based approaches, such as deterministic dynamic programming (DP) [7][8][9][10][11][12][13], Pontryagin's minimum principle (PMP) [14][15][16][17][18][19][20][21] and stochastic dynamic programming (SDP) [22][23][24][25][26]. Recently, convex optimization [27] has attracted attention in the research field of energy management for HEVs.…”

Section: Introductionmentioning

confidence: 99%

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

et al. 2014

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This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs) with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP) and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC) reaches its boundaries.

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MPC-based energy management with adaptive Markov-chain prediction for a dual-mode hybrid electric vehicle

Chen

Ding

Wang

et al. 2017

Sci. China Technol. Sci.

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Supervisory control of hybrid powertrains: An experimental benchmark of offline optimization and online energy management

Cited by 71 publications

References 17 publications

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

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

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

MPC-based energy management with adaptive Markov-chain prediction for a dual-mode hybrid electric vehicle

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