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A Comprehensive Hybrid Vehicle Model for Energetic Analyses on Different Powertrain Architectures
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 09, 2019 by SAE International in United States
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In the global quest for preventing fossil fuel depletion and reducing air pollution, hybridization plays a fundamental role to achieve cleaner and more fuel-efficient automotive propulsion systems.
While hybrid powertrains offer many opportunities, they also present new developmental challenges. Due to the many variants and possible architectures, development issues, such as the definition of powertrain concepts and the optimization of operating strategies, are becoming more and more important.
The paper presents model-based fuel economy analyses of different hybrid vehicle configurations, depending on the position of the electric motor generator (EMG). The analyses are intended to support the design of powertrain architecture and the components sizing, depending on the driving scenario, with the aim of reducing fuel consumption and CO2 emissions.
The analyses are performed making use of a comprehensive vehicle model, based on a hybrid (black-box and lumped parameters) approach, of a medium passenger car equipped with a turbocharged Diesel engine. The model has been enhanced to account for the additional components of two different powertrain configurations: one with the EMG directly coupled to the crankshaft and the other with the EMG positioned downstream the gearbox.
Simulations have been carried out vs. standard and real driving cycles for two energy management strategies, namely a rule-based strategy (RBS) and an equivalent consumption minimization strategy (ECMS). The results allow evaluating the impact of powertrain configuration and component sizing on fuel economy and CO2 emissions, in case of urban and extra-urban routes.
CitationCervone, D., Sessa, B., Arsie, I., Pianese, C. et al., "A Comprehensive Hybrid Vehicle Model for Energetic Analyses on Different Powertrain Architectures," SAE Technical Paper 2019-24-0064, 2019, https://doi.org/10.4271/2019-24-0064.
Data Sets - Support Documents
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