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Power Management for Hybrids and Vehicle Drivetrains

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (1 May 2016) | Viewed by 35492

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Prof. Dr. Dirk Söffker

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Chair of Dynamics and Control, University of Duisburg-Essen, Forsthausweg 2, 47057 Duisburg, Germany
Interests: control of energy flows—hybrid powertrains and wind turbine control; diagnostics and prognostics of technical systems; modeling, diagnosis, and control of elastic mechanical structures; control theory: robust observers and nonlinear control; cognitive technical systems: automata and assistance
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Dear Colleagues,

The hybridization of vehicle drive trains helps to overcome several conflicting requirements in the field of individual mobility. Overall fuel efficiency, exhaust behavior, drivability aspects, as well as design conflicts of the primary energy source will be strongly effected, introducing further energy storages between the primary source (ICE, FC, etc.) and the unknown highly dynamic power requirements realizing vehicle motion. Beside the characteristic properties of the hard components (motor, generator, fuel cell, storages, etc.), the soft components of drive trains, to control the energy flow in all directions based on measured, estimated, or assumed variables, are of importance. The focus of this Special Issue should be only on the soft components here denoted as power management. Power management includes controls, look-up-tables, adaption and learning strategies, sensor fusion, estimation and filtering approaches, optimization techniques, and is designed and applied to various drive train topologies and constellations. Common scientific tasks should be addressed in the Special Issue, addressing mechanical and electrical engineering aspects, as well as information science-oriented approaches.

To perfect the Special Issue “Power Management for Hybrids and Vehicle Drivetrains”, contributions should be clearly focused on the addressed research areas. Contributions should not be focused on hardware sizing aspects, pure numerical simulations studies, electronic circuits and related realizations, application reports, battery charging strategies, and should not only repeat known results (from previous works or the work of others). Prospective authors should provide original work with significant and novel contributions, providing new facts, ideas, insights, and results.

Prof. Dr. Dirk Söffker
Guest Editor

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Keywords

Power management strategies for hybrid vehicles
Power management strategies for vehicle drivetrains including motor management
Optimization of power management (online or offline), combined approaches
Optimization of architectures, algorithms, parameters
Optimization strategies related to power management design conflicts (fuel efficiency, drivability, aging, exhaust emission characteristics, etc.)
Aspects of integration of sources and storages with complex dynamical behavior on different scales
Consideration of individual driver/driving behavior
Drive cycles vs. real driving
Theory and practice/validation aspects of power management strategies
Adaptive, iterative, or learning behavior of power management considering variable conditions
Relations between standards, rules, or further requirements and realization of power management

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Published Papers (5 papers)

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Research

8014 KiB

Open AccessArticle

by Di Guo, Changqing Du and Fuwu Yan

Energies 2016, 9(9), 740; https://doi.org/10.3390/en9090740 - 13 Sep 2016

Cited by 6 | Viewed by 5742

Abstract

During the mode transition from the pure electric propulsion mode to the hybrid propulsion mode, clutch-based pre-transmission parallel hybrid electric vehicles are subject to drivability issues. These issues originate from the fact that in the clutch-based pre-transmission parallel hybrid powertrain (CPPHP) configuration, the clutch connects the engine and the motor. Without a carefully designed mode transition control that coordinates the engine torque, clutch torque and motor torque, torque sluggishness and surges occur during the mode transition, and residual torque oscillation occurs after the mode transition. In this paper, a discrete-time model predictive control (DMPC)-based controller is proposed to address these drivability-related issues. Modeling improvements and novel drivability-related indices and constraints are all taken into consideration in the design of the discrete-time model predictive controller. Furthermore, by using discrete-time Laguerre functions and introducing the equilibrium state and the ranking of constraints, an explicit solution of the discrete-time model predictive controller is obtained. The calculation results demonstrate that the proposed controller can ensure a smooth and rapidly decaying torque difference during the mode transition, alleviating the residual torque oscillation after the mode transition and guaranteeing that the mode transition is completed within an acceptable duration. Full article

(This article belongs to the Special Issue Power Management for Hybrids and Vehicle Drivetrains)

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7407 KiB

Open AccessArticle

Hybrid Modulation of Bidirectional Three-Phase Dual-Active-Bridge DC Converters for Electric Vehicles

by Yen-Ching Wang, Fu-Ming Ni and Tzung-Lin Lee

Energies 2016, 9(7), 492; https://doi.org/10.3390/en9070492 - 27 Jun 2016

Cited by 14 | Viewed by 9341

Abstract

Bidirectional power converters for electric vehicles (EVs) have received much attention recently, due to either grid-supporting requirements or emergent power supplies. This paper proposes a hybrid modulation of the three-phase dual-active bridge (3ΦDAB) converter for EV charging systems. The designed hybrid modulation allows the converter to switch its modulation between phase-shifted and trapezoidal modes to increase the conversion efficiency, even under light-load conditions. The mode transition is realized in a real-time manner according to the charging or discharging current. The operation principle of the converter is analyzed in different modes and thus design considerations of the modulation are derived. A lab-scaled prototype circuit with a 48V/20Ah LiFePO₄ battery is established to validate the feasibility and effectiveness. Full article

(This article belongs to the Special Issue Power Management for Hybrids and Vehicle Drivetrains)

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22983 KiB

Open AccessArticle

Online Power Management with Embedded Offline-Optimized Parameters for a Three-Source Hybrid Powertrain with an Experimental Emulation Application

by Bedatri Moulik and Dirk Söffker

Energies 2016, 9(6), 439; https://doi.org/10.3390/en9060439 - 7 Jun 2016

Cited by 11 | Viewed by 5148

Abstract

Real-time power management in the presence of one or more reversible energy storage systems is a current issue with hybrid electric vehicles (HEVs). To evaluate the potentials of rule-based power management, optimization with respect to two conflicting objectives, fuel consumption and state of charge (SoC) deviation, is considered in this contribution. A modular structure of power management with decoupled offline and online parts is presented. The online part incorporates look-up tables (LUTs) with parameters from the offline optimization part. This permits an inclusion of more LUTs corresponding to different drive patterns. The goal of this contribution is to combine the real-time applicability of rule-based power management and the multi-objective optimization property of genetic algorithms in a single control strategy. Component aging problems are addressed by suitable design. The influence of sizing is investigated. Finally, an experimental setup consisting of components capable of realizing the dynamics of real powertrain components is realized and introduced. A verification/plausibility assessment of modeled dynamics based on the literature is considered. This newly-introduced concept represents a class of power management, which is easy to implement, can tackle different objectives in real time, and adapt itself to unknown driver demands. Full article

(This article belongs to the Special Issue Power Management for Hybrids and Vehicle Drivetrains)

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4609 KiB

Open AccessEditor’s ChoiceArticle

Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses

by Hanho Son and Hyunsoo Kim

Energies 2016, 9(6), 420; https://doi.org/10.3390/en9060420 - 31 May 2016

Cited by 23 | Viewed by 7907

Abstract

A near-optimal rule-based mode control (RBC) strategy was proposed for a target plug-in hybrid electric vehicle (PHEV) taking into account the drivetrain losses. Individual loss models were developed for drivetrain components including the gears, planetary gear (PG), bearings, and oil pump, based on experimental data and mathematical governing equations. Also, a loss model for the power electronic system was constructed, including loss from the motor-generator while rotating in the unloaded state. To evaluate the effect of the drivetrain losses on the operating mode control strategy, backward simulations were performed using dynamic programming (DP). DP selects the operating mode, which provides the highest efficiency for given driving conditions. It was found that the operating mode selection changes when drivetrain losses are included, depending on driving conditions. An operating mode schedule was developed with respect to the wheel power and vehicle speed, and based on the operating mode schedule, a RBC was obtained, which can be implemented in an on-line application. To evaluate the performance of the RBC, a forward simulator was constructed for the target PHEV. The simulation results show near-optimal performance of the RBC compared with dynamic-programming-based mode control in terms of the mode operation time and fuel economy. The RBC developed with drivetrain losses taken into account showed a 4%–5% improvement of the fuel economy over a similar RBC, which neglected the drivetrain losses. Full article

(This article belongs to the Special Issue Power Management for Hybrids and Vehicle Drivetrains)

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4413 KiB

Open AccessArticle

Robust Longitudinal Speed Control of Hybrid Electric Vehicles with a Two-Degree-of-Freedom Fuzzy Logic Controller

by Jau-Woei Perng and Yi-Horng Lai

Energies 2016, 9(4), 290; https://doi.org/10.3390/en9040290 - 16 Apr 2016

Cited by 12 | Viewed by 5988

Abstract

This paper proposes a new robust two-degree-of-freedom (DoF) design method for controlling the nonlinear longitudinal speed problem of hybrid electric vehicles (HEVs). First, the uncertain parameters of the HEV model are described by fuzzy α-cut representation, in which the interval uncertainty and the possibility can be simultaneously indicated by the fuzzy membership function. For the fuzzy parametric uncertain system, the maximum uncertainty interval can be translated into the weighting matrix Q of the linear quadratic tracking problem to guarantee that the designed feedback controller is robust. Second, the fuzzy forward compensator is incorporated with a robust feedback controller to enhance the system tracking response. The simulation results demonstrate that the proposed controller has higher tracking performance compared to the single-DoF self-tuning fuzzy logic controller or conventional optimal H_∞ controller. Full article

(This article belongs to the Special Issue Power Management for Hybrids and Vehicle Drivetrains)

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