Thermal Management in Plug-In Hybrid Electric Vehicles: A Real-Time Nonlinear Model Predictive Control Implementation
A real-time nonlinear model predictive control (NMPC) for the thermal management (TM) of the electrical components cooling circuit in a Plug-In Hybrid Electric Vehicle (PHEV) is presented. The electrical components are highly temperature sensitive and, therefore, working out of the ranges recommende...
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| Veröffentlicht in: | IEEE transactions on vehicular technology Jg. 66; H. 9; S. 7751 - 7760 |
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| Sprache: | Englisch |
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IEEE
01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9545, 1939-9359 |
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| Abstract | A real-time nonlinear model predictive control (NMPC) for the thermal management (TM) of the electrical components cooling circuit in a Plug-In Hybrid Electric Vehicle (PHEV) is presented. The electrical components are highly temperature sensitive and, therefore, working out of the ranges recommended by the manufacturer can lead to their premature aging or even failure. Consequently, the goals for an accurate and efficient TM are to keep the main component, the Li-ion battery, within optimal working temperatures, and to consume the minimum possible electrical energy through the cooling circuit actuators. This multi-objective requirement is formulated as a finite-horizon optimal control problem (OCP) that includes a multi-objective cost function, several constraints, and a prediction model especially suitable for optimization. The associated NMPC is performed on real time by the optimization package MUSCOD-II and is validated in three different repeatable test-drives driven with a PHEV. Starting from identical conditions, each cycle is driven once being the cooling circuit controlled with NMPC and once with a conventional approach based on a finite-state machine. Compared to the conventional strategy, the NMPC proposed here results in a more accurate and healthier temperature performance, and at the same time, leads to reductions in the electrical consumption up to 8%. |
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A real-time nonlinear model predictive control (NMPC) for the thermal management (TM) of the electrical components cooling circuit in a Plug-In Hybrid Electric Vehicle (PHEV) is presented. The electrical components are highly temperature-sensitive and therefore working out of the ranges recommended by the manufacturer can lead to their premature aging or even failure. Consequently, the goals for an accurate and efficient TM are two: to keep the main component, the Li-ion battery, within optimal working temperatures, and to consume the minimum possible electrical energy through the cooling circuit actuators. This multi-objective requirement is formulated as a finite-horizon optimal control problem (OCP) that includes a multi-objective cost function, several constraints and a prediction model especially suitable for optimization. The associated NMPC is performed on real-time by the optimization package MUSCOD-II and is validated in three different repeatable test-drives driven with a PHEV. Starting from identical conditions, each cycle is driven once being the cooling circuit controlled with NMPC and once with a conventional approach based on a finite-state machine. Compared to the conventional strategy, the NMPC proposed here results in a more accurate and healthier temperature performance, and at the same time, leads to reductions in the electrical consumption up to 8%. A real-time nonlinear model predictive control (NMPC) for the thermal management (TM) of the electrical components cooling circuit in a Plug-In Hybrid Electric Vehicle (PHEV) is presented. The electrical components are highly temperature sensitive and, therefore, working out of the ranges recommended by the manufacturer can lead to their premature aging or even failure. Consequently, the goals for an accurate and efficient TM are to keep the main component, the Li-ion battery, within optimal working temperatures, and to consume the minimum possible electrical energy through the cooling circuit actuators. This multi-objective requirement is formulated as a finite-horizon optimal control problem (OCP) that includes a multi-objective cost function, several constraints, and a prediction model especially suitable for optimization. The associated NMPC is performed on real time by the optimization package MUSCOD-II and is validated in three different repeatable test-drives driven with a PHEV. Starting from identical conditions, each cycle is driven once being the cooling circuit controlled with NMPC and once with a conventional approach based on a finite-state machine. Compared to the conventional strategy, the NMPC proposed here results in a more accurate and healthier temperature performance, and at the same time, leads to reductions in the electrical consumption up to 8%. |
| Author | Graeber, Manuel Alvarez-Florez, Jesus Ruiz-Mansilla, Rafael Ocampo-Martinez, Carlos Moreno-Eguilaz, Manuel Kalmus, Julian Lopez-Sanz, J. Lux, Gerhard |
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| Cites_doi | 10.1109/TVT.2015.2405918 10.1007/978-3-642-01094-1_32 10.3384/ecp12076781 10.1109/WCICA.2012.6357828 10.1016/S0959-1524(01)00023-3 |
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| Contributor | Universitat Politècnica de Catalunya. Departament de Màquines i Motors Tèrmics Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Universitat Politècnica de Catalunya. CREMIT - Centre de Recerca de Motors i Instal·lacions Tèrmiques Universitat Politècnica de Catalunya. GREENTECH - Grup de Recerca en Tecnologies Renovables Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
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| References | ref14 diehl (ref12) 2009 ref11 bock (ref13) 1984; xlii duijkeren (ref3) 2015 pesaran (ref1) 2001 association (ref10) 2010 ref7 sanz (ref8) 2016 ref5 schmied (ref2) 2015 jiangyan (ref4) 2014 (ref9) 2004 hoffmann (ref6) 2010 |
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| SubjectTerms | Actuators Automobiles Automòbils Bateries Batteries Circuits Constraint modelling Control predictiu Coolants Cooling Electric components Electric vehicles Enginyeria elèctrica Enginyeria mecànica Finite state machines Hybrid electric vehicles Integrated circuit modeling Li-ion battery cooling Lithium-ion batteries Mathematical model Motors Motors elèctrics Multiple objective analysis Nonlinear control Nonlinear model predictive control (NMPC) Object oriented modeling Optimal control Optimization plug-in hybrid electric vehicles (PHEV) Predictive control Real time Real-time systems Thermal management thermal management (TM) Vehicles elèctrics híbrids Àrees temàtiques de la UPC |
| Title | Thermal Management in Plug-In Hybrid Electric Vehicles: A Real-Time Nonlinear Model Predictive Control Implementation |
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