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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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 66; no. 9; pp. 7751 - 7760
Main Authors: Lopez-Sanz, J., Ocampo-Martinez, Carlos, Alvarez-Florez, Jesus, Moreno-Eguilaz, Manuel, Ruiz-Mansilla, Rafael, Kalmus, Julian, Graeber, Manuel, Lux, Gerhard
Format: Journal Article Publication
Language:English
Published: New York IEEE 01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary: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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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2017.2678921