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

Abstract

-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...

Integrated Energy and Thermal Management for Electrified Powertrains

“…The reference value can be chosen as the final SOC, which is equal to the initial SOC. More advanced adaptation methods can be found in [57,58]. The co-state of the engine temperature can be retrieved from the pre-stored tables, as mentioned above.…”

Optimal Control of Electrified Powertrains

“…Paper 11 [84] presents the design, implementation, and analysis of the best performing controller for the benchmark in Paper 10. The contribution of the method proposed in the paper is an efficient way of solving and implementing the ECMS control strategy for a PHEV that is also self-contained, using driving distance and average speed to estimate the initial equivalence factor and then adapting it continuously throughout the driving mission to ensure that it is robust to unknown driving missions and that the desired discharge profile is followed.…”

“…Publications G, H, and J are preliminary to Paper 1 in the dissertation [89]. In publication I, describing the controller for the PHEV benchmark in Paper 10 [72], the author did all the work, a publication preliminary to Paper 11 in the dissertation [84]. Publication K is the author's masters thesis that contains work preliminary to Paper 9 in the dissertation [91].…”

An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles

“…The ECMS instantaneously minimizes the equivalent energy consumption cost, with the objective function described as [22,33]:…”

“…Moreover, taking into account different energy conversion efficiencies during driving and braking of HEVs, two constants, rather than a single one, have been used to define the EF [5]. A map-based ECMS for PHEVs has been proposed as well, in which the optimal energy distribution is performed offline and stored in tables so that the EF can be determined by searching the tables online [22]. Furthermore, an improved adaptive equivalent consumption minimization strategy (IA-ECMS) has been designed to instantaneously adjust the EF based on the identified driving behavior and predicted real-time traffic information [27].…”