Cabin heat thermal management in hybrid vehicles using model predictive control

Esen, Hasan; Tashiro, Takuya; Bernardini, Daniele; Bemporad, Alberto

doi:10.1109/med.2014.6961325

Cited by 14 publications

(9 citation statements)

References 18 publications

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“…where T b (t) will be treated as a (slow) additive load disturbance, and g(t) as a time-varying gain, or equivalently a multiplicative input disturbance, with known upper and lower bounds. Incidentally, the use of LTV models for temperature control problems can be encountered in the literature (see the recent papers [28], [29]).…”

Section: Controlled System Modelmentioning

confidence: 99%

Event-Based Power/Performance-Aware Thermal Management for High-Density Microprocessors

Leva

Terraneo

Giacomello

et al. 2018

IEEE Trans. Contr. Syst. Technol.

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The density of modern microprocessors is so high, that operating all their units at full power would destroy them by thermal runaway. Hence, thermal control is vital, but at the same time has to integrate with power/performance management, to not unduly limit computational speed. In addition, the controller must be simple and computationally light, as millisecond-scale response is required. Finally, since microprocessors face a variety of operating conditions, postsilicon tuning is an issue. We here present a solution, by exploiting event-based control and a hardware/software partition to maximize efficiency, lightness, and flexibility. We show experiments on real hardware, evidencing the obtained advantages over the state of the art

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Section: Controlled System Modelmentioning

confidence: 99%

Event-Based Power/Performance-Aware Thermal Management for High-Density Microprocessors

Leva

Terraneo

Giacomello

et al. 2018

IEEE Trans. Contr. Syst. Technol.

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“…Wang et al presented that by using nonlinear model predictive control (NMPC) to minimize the energy consumption of the A/C system and regulate the cabin temperature for hybrid and electric passenger cars, up to 9% of energy efficiency improvement can be resulted in the energy efficiency of the A/C system [133]. Esen et al discussed the advantages of using MPC for hybrid vehicles cabin heating, where up to 3% fuel savings can be achieved compared to a reference baseline controller [134].…”

Section: Thermal System Controlmentioning

confidence: 99%

Review of Cabin Thermal Management for Electrified Passenger Vehicles

Lajunen

Yang

Emadi

2020

IEEE Trans. Veh. Technol.

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The technical maturity and rapidly increasing market share of electrified vehicles have given more importance to the cabin thermal management. Efficient thermal management has a key role to maintain adequate electric operating range, protect components from aging and ensure passenger comfort. This research comprehensively reviews the cabin thermal management systems for electrified vehicles. Various vehicle cabin thermal modeling techniques and the key concepts for cabin thermal comfort have been discussed. Different technical and operational solutions of the cabin thermal management in hot and cold climate conditions are evaluated. The recent developments including heat pumps, thermal system control, passive thermal management, and cabin preconditioning to increase thermal management efficiency are analyzed. The results show that increasing amount of research is focusing on improving the thermal efficiency of the individual electrical components and the vehicle level systems. However, new solutions will be required for reducing the cabin thermal load under hot conditions and improving the heating system operational flexibility and efficiency in cold climates.

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“…There are, as well, investigations on advanced control systems. For example, (Esen et al, 2014) and (Karnik et al, 2016) use MPC-controllers in the application field of thermal management systems, and (Afram and Janabi-Sharifi, 2014) gives an general overview for HVAC systems. To reduce the computational effort, these approaches however often do not rely on first-principles models, but rather on data models or linearized state-space representations.…”

Section: State Of the Artmentioning

confidence: 99%

Nonlinear Model Predictive Control of a Thermal Management System for Electrified Vehicles using FMI

Fischer¹,

Kraus²,

Kirches³

et al. 2017

Linköping Electronic Conference Proceedings

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Energy-efficient thermal management systems for Emobility help to decrease energy consumption and increase range. Due to transient external conditions and the increasing system complexity, optimization-based control approaches are required in order to harness the full potential of such systems. In (Fischer et al., 11th Int. Modelica Conf, 2015), we have presented a model-based development cycle for a thermal management system in Emobility to this end. In this article, we build upon this work to describe the use of this model within a nonlinear model predictive control (NMPC) approach. The main benefits of using an advanced optimization-based control system in this application are a) the ability to control the battery temperature and the cabin temperature simultaneously, b) the increased energy efficiency achieved by exploiting the predictive character of the optimizationbased control approach, c) the possibility to include operational limits as constraints in the optimization problems and d) the fast reaction to disturbances or model parameter changes. We evaluate the merit of the proposed advanced control system by way of a comparison to conventional PID controller.

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Cabin heat thermal management in hybrid vehicles using model predictive control

Cited by 14 publications

References 18 publications

Event-Based Power/Performance-Aware Thermal Management for High-Density Microprocessors

Event-Based Power/Performance-Aware Thermal Management for High-Density Microprocessors

Review of Cabin Thermal Management for Electrified Passenger Vehicles

Nonlinear Model Predictive Control of a Thermal Management System for Electrified Vehicles using FMI

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