| 1. |
- Andric, Jelena, 1979, et al.
(författare)
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Calibration Procedure for Measurement-Based Fast Running Model for Hardware-in-the-Loop Powertrain Systems
- 2020
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2020- April:April
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Tidskriftsartikel (refereegranskat)abstract
- The requirements set for the next-generation powertrain systems (e.g. performance and emissions) are becoming increasingly stringent with ever-shortening time-to-markets at reduced costs. To remain competitive automotive companies are progressively relying on model-driven development and virtual testing. Virtual test benches, such as Hardware-in the-Loop simulators, are powerful tools to reduce the amount of physical testing and speed up engine software calibration process. The introduction of these technologies places new, often conflicting demands (such as higher predictability, faster simulation speed, and reduced calibration effort ) upon simulation models used at Hardware-in-the-Loop test benches. The new models are also expected to offer compliance to industry standards, performance and usability to further increase the usage of virtual tests in powertrain development. The present work describes a novel verification process for creating a fast running model for a heavy-duty diesel engine using FRM-d Builder in GT-SUITE simulation software. The approach uniquely applies the combination physical modelling and parameter estimation techniques, while relying solely on test cell measurements without data maps from the manufactures (e.g. for turbine and compressor). The procedure provides detailed description for subsystem calibration for turbocharger and intake path. The developed model is successfully employed at the VIRTEC system in Volvo Penta. The simulation results for engine performance and exhaust emissions provide favourable results for both steady-state and transient operating conditions.
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| 2. |
- Astaneh, Majid, 1990, et al.
(författare)
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Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications
- 2020
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Ingår i: Energies. - : MDPI AG. - 1996-1073 .- 1996-1073. ; 13:14
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale introduction of electric vehicles (EVs) to the market sets outstanding requirements for battery performance to extend vehicle driving range, prolong battery service life, and reduce battery costs. There is a growing need to accurately and robustly model the performance of both individual cells and their aggregated behavior when integrated into battery packs. This paper presents a novel methodology for Lithium-ion (Li-ion) battery pack simulations under actual operating conditions of an electric mining vehicle. The validated electrochemical-thermal models of Li-ion battery cells are scaled up into battery modules to emulate cell-to-cell variations within the battery pack while considering the random variability of battery cells, as well as electrical topology and thermal management of the pack. The performance of the battery pack model is evaluated using transient experimental data for the pack operating conditions within the mining environment. The simulation results show that the relative root mean square error for the voltage prediction is 0.7–1.7% and for the battery pack temperature 2–12%. The proposed methodology is general and it can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs.
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| 3. |
- Astaneh, Majid, 1990, et al.
(författare)
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Lithium-ion Battery Pack Design for Electric Vehicles Using GT-AutoLion: Multi-Physics Simulation and Multi-Criteria Optimization Approach
- 2021
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Ingår i: Global Gamma Technologies Virtual Conference.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- High specific energy battery systems with improved thermal performance are required for large-scale introduction of electric vehicles (EVs) into the market. This study presents a comprehensive multi-physics simulation and multi-criteria optimization framework for Lithium-ion (Li-ion) battery pack design for EV applications. The battery cells are modeled by electrochemical thermally coupled approach using GT-AutoLion. Multi-objective optimization using genetic algorithm is employed to explore energy and thermally efficient cell design alternatives. The performances of the optimally designed cells are then evaluated under pack environment to account for inhomogeneities in large traction battery packs under realistic working scenarios. It is observed that considering the thermal efficiency of battery cells is crucial for obtaining improved battery pack performance. The integrated framework developed in this work provides systematic pack-aware guidelines for manufacturers already at the initial cell design stage. Moreover, the proposed design optimization methodology is generic, handing over valuable knowledge for future cell and pack designs for various applications.
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| 4. |
- Astaneh, Majid, 1990, et al.
(författare)
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Lithium-Ion Battery Pack Modelling for Electric Vehicles in Mining Applications Using GT-AutoLion
- 2020
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Large traction battery packs play a pivotal role in electric vehicles (EVs) to fulfill the system demand for high voltage and capacity. Therefore, accurate and robust modeling of both individual battery cells and their aggregated behavior in battery packs is of crucial importance. This presentation proposes a novel approach to integrate the validated electrochemical-thermal models of the Lithium-ion (Li-ion) cells into battery pack simulations for electric vehicle applications. The approach employs the calibration optimization methodology that utilizes experimental measurements for battery cells and for the battery pack under realistic operating conditions in mining applications. The random variability of battery cells, as well as electrical topology and thermal management of the pack have been considered in the present study to mimic the actual behavior of the battery pack under consideration. The simulations were carried out in GT-AutoLion and the optimizations were performed using GT-SUITE direct optimizer. The experimental data were provided by Northvolt AB, a leading European manufacturer of the next-generation Li-ion battery cells and complete battery systems. The proposed methodology is general and can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs.
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| 5. |
- Astaneh, Majid, 1990, et al.
(författare)
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Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications
- 2022
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Ingår i: Energy. - : Elsevier BV. - 0360-5442. ; 239
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale commercialization of electric vehicles (EVs) seeks to develop battery systems with higher energy efficiency and improved thermal performance. Integrating simulation-based design optimization in battery development process expands the possibilities for novel design exploration. This study presents a dual-stage multiphysics simulation optimization methodology for comprehensive concept design of Lithium-ion (Li-ion) battery packs for EV applications. At the first stage, multi-objective optimization of electrochemical thermally coupled cells is performed using genetic algorithm considering the specific energy and the maximum temperature of the cells as design objectives. At the second stage, the energy efficiency and the thermal performances of each optimally designed cell are evaluated under pack operation to account for cell-to-pack interactions under realistic working scenarios. When operating at 1.5 C discharge current, the battery pack comprising optimally designed cells for which the specific energy and the maximum temperature are equally weighted delivers the highest specific energy with enhanced thermal performance. The most favorable pack design shows 8% reduction in maximum pack temperature and 16.1% reduction in module-to-module temperature variations compared to commercially available pack. The methodology for design optimization presented in this work is generic, providing valuable knowledge for future cell and pack designs that employ different chemistries and configurations.
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| 6. |
- Norouzi, MohammadJavad, et al.
(författare)
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Shape evolution of long flexible fibers in viscous flows
- 2022
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Ingår i: Acta Mechanica. - : Springer Science and Business Media LLC. - 1619-6937 .- 0001-5970. ; 233, s. 2077-2091
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Tidskriftsartikel (refereegranskat)abstract
- The present work studies numerically the dynamics and shape evolution of long flexible fibers suspended in a Newtonian viscous cellular flow using a particle-level fiber simulation technique. The fiber is modeled as a chain of massless rigid cylindrical segments connected by ball and socket joints; one-way coupling between the fibers and the flow is considered while Brownian motion is neglected. The effect of stiffness, equilibrium shape, and aspect ratio of the fibers on the shape evolution of the fibers are analyzed. Moreover, the influence of fiber stiffness and their initial positions and orientations on fiber transport is investigated. For the conditions considered, the results show that the fiber curvature field resembles that of the flow streamline. It is found that the stiffer fibers experience not only a quicker relaxation phase, in which they transient from their initial shape to their "steady-state shape," but they also regain their equilibrium shape to a larger extent. The findings also demonstrate that even a small deviation of fiber shape from perfectly straight impacts significantly the early-stage evolution of the fiber shape and their bending behavior. Increasing the fiber aspect ratio, when other parameters are kept fixed, leads the fiber to behave more flexible, and it consequently deforms to a larger extent to adjust to the shape of the flow streamlines. In agreement with the available experimental results, the fiber transport studies show that either the fiber becomes trapped within the vortices of the cellular array or it moves across the vortical arrays while exhibiting various complex shapes.
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| 7. |
- Ramesh Babu, Anandh, 1996, et al.
(författare)
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System-level modeling and thermal simulations of large battery packs for electric trucks
- 2021
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Ingår i: Energies. - : MDPI AG. - 1996-1073 .- 1996-1073. ; 14:16
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Tidskriftsartikel (refereegranskat)abstract
- Electromobility has gained significance over recent years and the requirements on the performance and efficiency of electric vehicles are growing. Lithium-ion batteries are the primary source of energy in electric vehicles and their performance is highly dependent on the operating temperature. There is a compelling need to create a robust modeling framework to drive the design of vehicle batteries in the ever-competitive market. This paper presents a system-level modeling methodology for thermal simulations of large battery packs for electric trucks under real-world operating conditions. The battery pack was developed in GT-SUITE, where module-to-module discretization was performed to study the thermal behavior and temperature distribution within the pack. The heat generated from each module was estimated using Bernardi’s expression and the pack model was calibrated for thermal interface material properties under a heat-up test. The model evaluation was performed for four charging/discharging and cooling scenarios typical for truck operations. The results show that the model accurately predicts the average pack temperature, the outlet coolant temperature and the state of charge of the battery pack. The methodology developed can be integrated with the powertrain and passenger cabin cooling systems to study complete vehicle thermal management and/or analyze different battery design choices.
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| 8. |
- Yang, Ying, et al.
(författare)
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Data-driven rolling eco-speed optimization for autonomous vehicles
- 2024
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Ingår i: Frontiers of Engineering Management. - 2096-0255 .- 2095-7513. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.
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