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- Genre: Academic theses
Description
This research effort focuses on thermal management system (TMS) design for a high-performance, Plug-in Hybrid Electric Vehicle (PHEV). The thermal performance for various components in an electrified powertrain is investigated using a 3D finite difference model for a complete vehicle system, including inherently temperature-sensitive components. The components include the electric motor (EM), power electronics, Energy Storage System (ESS), and Internal Combustion Engine (ICE).
A model-based design approach is utilized, where a combination of experimental work and simulation are integrated. After defining heat sources and heat sinks within the power train system, temporal and spatial boundary conditions were extracted experimentally to facilitate the 3D simulation under different road-load scenarios. Material properties, surface conditions, and environmental factors were defined for the geometrical surface mesh representation of the system. Meanwhile the finite differencing code handles the heat transfer phenomena via conduction and radiation, all convective heat transfer mode within the powertrain are defined using fluid nodes and fluid streams within the powertrain.
Conclusions are drawn through correlating experimental results to the outcome from the thermal model. The outcome from this research effort is a 3D thermal performance predictive tool that can be utilized in order to evaluate the design of advanced thermal management systems (TMSs) for alternative powertrains in early design/concept stages of the development process.
For future work, it is recommended that a full validation of the 3D thermal model be completed. Subsequently, design improvements can be made to the TMS. Some possible improvements include analysis and evaluation of shielding of the catalytic converter, exhaust manifold, and power electronics, as well as substituting for material with better thermal performance in other temperature-sensitive components, where applicable. The result of this improvement in design would be achieving an effective TMS for a high-performance PHEV.
A model-based design approach is utilized, where a combination of experimental work and simulation are integrated. After defining heat sources and heat sinks within the power train system, temporal and spatial boundary conditions were extracted experimentally to facilitate the 3D simulation under different road-load scenarios. Material properties, surface conditions, and environmental factors were defined for the geometrical surface mesh representation of the system. Meanwhile the finite differencing code handles the heat transfer phenomena via conduction and radiation, all convective heat transfer mode within the powertrain are defined using fluid nodes and fluid streams within the powertrain.
Conclusions are drawn through correlating experimental results to the outcome from the thermal model. The outcome from this research effort is a 3D thermal performance predictive tool that can be utilized in order to evaluate the design of advanced thermal management systems (TMSs) for alternative powertrains in early design/concept stages of the development process.
For future work, it is recommended that a full validation of the 3D thermal model be completed. Subsequently, design improvements can be made to the TMS. Some possible improvements include analysis and evaluation of shielding of the catalytic converter, exhaust manifold, and power electronics, as well as substituting for material with better thermal performance in other temperature-sensitive components, where applicable. The result of this improvement in design would be achieving an effective TMS for a high-performance PHEV.
ContributorsCarroll, Joshua Kurtis (Author) / Mayyas, Abdel Ra'Ouf (Thesis advisor) / Wishart, Jeffrey (Committee member) / Contes, James (Committee member) / Arizona State University (Publisher)
Created2017
Description
For years the automotive industry has been shifting towards hybridization and electrification of conventional powertrains due to increase in fossil fuel cost and environmental impact due heavy emission of Green House Gases (GHG) and various pollutants into atmosphere by combustion engine powered vehicles. Hybrid Electric Vehicles (HEV) have proved to achieve superior fuel economy and reduced emissions. Supervisory control strategies determining the power split among various onboard power sources are evolving with time, providing better fuel economies.
With increasing complexity of control systems driving HEV’s, mathematical modeling and simulation tools have become extremely advanced and have derived whole industry into adopting Model Based Design (MBD) and Hardware-in-the-loop (HIL) techniques to validate the performance of HEV systems in real world.
This report will present a systematic mythology where MBD techniques are used to develop hybrid powertrain, supervisory control strategies and control systems. To validate the effectiveness of various energy management strategies for HEV energy management in a real world scenario, Conventional rule-based power split strategies are compared against advanced Equivalent Consumption Minimization Strategy (ECMS), in software and HIL environment.
Since effective utilization of the fuel reduction potential of a HEV powertrain requires a careful design of the energy management control methodology, an advanced ECMS strategy involving implementation with Fuzzy Logic to reduce computational overload has been proposed. Conventional real-time implementation of ECMS based strategy is difficult due to the involvement of heavy computation. Methods like Fuzzy Logic based estimation can be used to reduce this computational overload.
Real-time energy management is obtained by adding a Fuzzy Logic based on-the-fly algorithm for the estimation of driving profile and adaptive equivalent consumption minimization strategy (A-ECMS) framework. The control strategy is implemented to function without any prior knowledge of the future driving conditions. The idea is to periodically refresh the energy management strategy according to the estimated driving pattern, so that the Battery State of Charge (SOC) is maintained within the boundaries and the equivalent fuel consumption is minimized. The performance of the presented Fuzzy Logic based adaptive control strategy utilizing driving pattern recognition is benchmarked using a Dynamic Programming based global optimization approach.
With increasing complexity of control systems driving HEV’s, mathematical modeling and simulation tools have become extremely advanced and have derived whole industry into adopting Model Based Design (MBD) and Hardware-in-the-loop (HIL) techniques to validate the performance of HEV systems in real world.
This report will present a systematic mythology where MBD techniques are used to develop hybrid powertrain, supervisory control strategies and control systems. To validate the effectiveness of various energy management strategies for HEV energy management in a real world scenario, Conventional rule-based power split strategies are compared against advanced Equivalent Consumption Minimization Strategy (ECMS), in software and HIL environment.
Since effective utilization of the fuel reduction potential of a HEV powertrain requires a careful design of the energy management control methodology, an advanced ECMS strategy involving implementation with Fuzzy Logic to reduce computational overload has been proposed. Conventional real-time implementation of ECMS based strategy is difficult due to the involvement of heavy computation. Methods like Fuzzy Logic based estimation can be used to reduce this computational overload.
Real-time energy management is obtained by adding a Fuzzy Logic based on-the-fly algorithm for the estimation of driving profile and adaptive equivalent consumption minimization strategy (A-ECMS) framework. The control strategy is implemented to function without any prior knowledge of the future driving conditions. The idea is to periodically refresh the energy management strategy according to the estimated driving pattern, so that the Battery State of Charge (SOC) is maintained within the boundaries and the equivalent fuel consumption is minimized. The performance of the presented Fuzzy Logic based adaptive control strategy utilizing driving pattern recognition is benchmarked using a Dynamic Programming based global optimization approach.
ContributorsKumar, Sushil (Author) / Mayyas, Abdel Ra'Ouf (Thesis advisor) / Kannan, Arunachala Nadar Mada (Committee member) / Contes, James (Committee member) / Arizona State University (Publisher)
Created2015