Optimization of the powerflow control of a hybrid electric powertrain including load profile prediction

Marx, Matthias; Söffker, Dirk

doi:10.1109/vppc.2012.6422666

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…Unlike numerical optimization methods, analytical optimization methods based on minimum principals such as Pontryagins minimum principle reduce the computational load substantially [61]. Nevertheless, formulation of a complex nonlinear MIMO hybrid power management for application of analytical methods requires model simplification.…”

Section: Discussion and Evaluationmentioning

confidence: 99%

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Review and Comparison of Power Management Approaches for Hybrid Vehicles with Focus on Hydraulic Drives

Karbaschian

Söffker

2014

Energies

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Abstract:The main advantage of hybrid powertrains is based on the efficient transfer of power and torque from power sources to the powertrain as well as recapturing of reversible energies without effecting the vehicle performance. The benefits of hybrid hydraulic powertrains can be better utilized with an appropriate power management. In this paper, different types of power management algorithms like off-line and on-line methods are briefly reviewed and classified. Finally, the algorithms are evaluated and compared. Therefore, different related criteria are evaluated and applied.

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Section: Discussion and Evaluationmentioning

confidence: 99%

“…The performance of nonlinear MPC-based power management in association with an adaptive prediction time horizon is presented in [61]. If the predictive velocity matched well with the reference velocity, time horizon is increased and vice versa.…”

Section: Model Predictive Approachmentioning

confidence: 99%

Review and Comparison of Power Management Approaches for Hybrid Vehicles with Focus on Hydraulic Drives

Karbaschian

Söffker

2014

Energies

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“…Simulation results of the nonlinear MPC show a noticeable improvement in the fuel economy with respect to linear time-varying MPC. In [152], the power management based on nonlinear MPC with an adaptive prediction time horizon is proposed. An MPC-based control algorithm based on load profile prediction is proposed.…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

et al. 2020

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Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

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“…Without the information available from telemetry, prediction strategies based on neural networks and stochastic Markov chain have been considered in [49] within an MPC framework. Past trajectories have been used for prediction, as considered in [48,50,51]. Here, a prediction algorithm considers certain features of the past trajectory measured over predefined time horizons.…”

Section: Introductionmentioning

confidence: 99%

“…The adaption of horizon length (the number of past measurements) depending on the prediction performance is a key aspect here. The task of control optimization in [50] is carried out using MPC, and the results are compared to globally-optimal solutions determined by dynamic programming. In [52], an intelligent control, both with and without knowledge of the unknown, is considered; whereas in [53], control based on both the driving cycle, as well as driving style is elaborated.…”

Section: Introductionmentioning

confidence: 99%

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Moulik

Söffker

2015

Energies

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The field of hybrid vehicles has undergone intensive research and development, primarily due to the increasing concern of depleting resources and increasing pollution. In order to investigate further options to optimize the performance of hybrid vehicles with regards to different criteria, such as fuel economy, battery aging, etc., a detailed state-of-the-art review is presented in this contribution. Different power management and optimization techniques are discussed focusing on rule-based power management and multi-objective optimization techniques. The extent of rule-based power management and optimization in solving battery aging issues is investigated along with an implementation in real-time driving scenarios where no pre-defined drive cycle is followed. The goal of this paper is to illustrate the significance and applications of rule-based power management optimization based on previous contributions.

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Optimization of the powerflow control of a hybrid electric powertrain including load profile prediction

Cited by 9 publications

References 18 publications

Review and Comparison of Power Management Approaches for Hybrid Vehicles with Focus on Hydraulic Drives

Review and Comparison of Power Management Approaches for Hybrid Vehicles with Focus on Hydraulic Drives

Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

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