A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management

Musardo, Cristian; Rizzoni, Giorgio; Guezennec, Yann; Staccia, Benedetto

doi:10.3166/ejc.11.509-524

Cited by 634 publications

(306 citation statements)

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“…This problem is well accommodated by the ECMS, where the sum of fuel and battery power is minimized. However, battery and fuel power are not directly comparable and therefore an equivalence factor k relating the two is needed; for more information on ECMS [7][8][9]. The problem is formulated as:…”

Section: Problem Formulationmentioning

confidence: 99%

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: Problem Formulationmentioning

confidence: 99%

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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“…2R in Q max (18) where Q max is the maximum charging capability, T m is the motor torque, and w m is the motor speed. Equation (18) can be rewritten in discretization form, as shown in Equation (19), to calculate the SoC at instant k…”

Section: Battery Modelmentioning

confidence: 99%

“…(1) When the clutch is engaged or disengaged, the clutch model can be represented as Equations (11) and (12) …”

Section: Clutch Modelmentioning

confidence: 99%

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A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI

Zhang

Liu

et al. 2016

Energies

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This paper presents a new energy management system based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to enhance fuel economy and impose state of charge (SoC) charge-sustainability. First, the relationship between the equivalent factor (EF) of ECMS and the co-state of pontryagin's minimum principle (PMP) is derived. Second, a new method of implementing the adaptation law using fuzzy proportional plus integral (PI) controller is developed to adjust EF for ECMS in real-time. This adaptation law is more robust than one with constant EF due to the variation of EF as well as driving cycle. Finally, simulations for two driving cycles using ECMS are conducted as opposed to the commonly used rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results confirm that ECMS with Fuzzy PI adaptation law is more robust than ECMS with constant EF as well as PI adaptation law and it achieves significant improvements compared with RB in terms of fuel economy, which is enhanced by 4.44% and 14.7% for china city bus cycle and economic commission of Europe (ECE) cycle, respectively.

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“…This energy management problem has been studied extensively in academic circles [7], [9], [11], [13]. There are many proposed methods available for both the non-causal (cycle known in advance) and causal (cycle unknown in advance) cases.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of hybrid vehicle energy management controllers on real-world drive cycle data

Opila

Wang

McGee

et al. 2009

2009 American Control Conference

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Abstract-Hybrid Vehicle fuel economy and drivability performance are very sensitive to the "Energy Management" controller that regulates power flow among the various energy sources and sinks. Many methods have been proposed for designing such controllers. Most analytical studies evaluate closed-loop performance on government test cycles. Moreover, there are few results that compare stochastic optimal control algorithms to the controllers employed in today's production hybrids. This paper studies controllers designed using Shortest Path Stochastic Dynamic Programming (SPSDP). The controllers are evaluated on Ford Motor Company's highly accurate proprietary vehicle model over large numbers of realworld drive cycles, and compared to a controller developed by Ford for a prototype vehicle. Results show the SPSDPbased controllers yield 2-3% better performance than the Ford controller on real-world driving data, with even more improvement on a government test cycle. In addition, the SPSDP-based controllers can directly quantify tradeoffs between fuel economy and drivability.

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A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management

Cited by 634 publications

References 1 publication

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

A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI

Performance comparison of hybrid vehicle energy management controllers on real-world drive cycle data

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