Neural Network-based Optimal Control for Advanced Vehicular Thermal Management Systems

Tamimi, Asma Al; Salah, Mohammad; Al-Jarrah, Ahmad

doi:10.4271/2011-01-2184

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…A simple control was designed by Choukroun and Chanfreau [6], their aim was to improve fuel economy which they achieved in a test bench (2% improvement). Al Tamini et al [1] built a servo-motor based cooling system. Lower power consumption was obtained and the controller performance was satisfactory during the simulations.…”

Section: Thermal Management and Control Theorymentioning

confidence: 99%

See 1 more Smart Citation

Heavy Duty Vehicle Cooling System Auxiliary Load Management Control: An Application of Linear Control Strategy (MIMO and SISO)

Sermeno

Bideaux

Brun

et al. 2014

Volume 8A: Heat Transfer and Thermal Engineering

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International audienceVehicle Thermal Management covers the engineering field of solutions that maintain the complete vehicle in acceptable operating conditions regarding components and fluid temperatures in an engine. The maximum efficiency rating of a Diesel engine reaches up to 45%; a vast amount of the energy produced is transformed into heat. This heat is partly rejected in the exhaust gases and partly transmitted to the engine cooling circuit. The latter can be seen in two different ways, on the one hand, cooling is necessary to regulate the fluids and component temperature to an optimum operating point for fuel efficiency and maintain engine performance. On the other hand it constitutes a loss since the coolant system actuators are engine driven (pump, fan, etc.).In order to improve the fuel efficiency of the vehicle one can reduce the losses generated by the cooling system. Ideally, the full motive force of the engine should be used for propulsion, and new and more efficient energy sources have to be explored to power the secondary systems (cooling, compressed air…). The electrification of some components in the cooling system can limit losses and improve component energy efficiency but it is not the only answer and in many cases this approach might be a limited. Recent studies have shown that by improving the control strategy of the cooling system the fuel consumption can be improved, however no real data is available since its implementation has been limited.In keeping with latter approach, this paper introduces a novel control which aims at a more efficient regulation of the cooling system operation of a Heavy Duty Truck cooling system. The main complexity in such a system remains the interactions between actuators. In this paper we propose a way to solve this using a control based on model inversion and decoupling strategy. It needs to be noted that any new approach requires the current control specifications to be modified. This enables also a better understanding of the system. However, other goals can be exploited through the use of an advanced control and the new control specifications such as a reduction of thermal shock, reduction of thermal fatigue, minimization of system overcooling (directly impacts fuel consumption but also the noise levels).Finally, the controller has been tested on a Simulation Platform using a Matlab/Simulink (Controller) and compared to the existing system control using a reference driving cycle

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Section: Thermal Management and Control Theorymentioning

confidence: 99%

“…One of the advantages of the flatness approach is that it enables the decoupling of the systems outputs and inputs. 1 Advanced control has different meanings according to the user (researcher or engineer). It is more than just the use of state of the art software or the use of a complex algorithm.…”

Section: Thermal Management and Control Theorymentioning

confidence: 99%

Heavy Duty Vehicle Cooling System Auxiliary Load Management Control: An Application of Linear Control Strategy (MIMO and SISO)

Sermeno

Bideaux

Brun

et al. 2014

Volume 8A: Heat Transfer and Thermal Engineering

View full text Add to dashboard Cite

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“…On the other hand, it is desirable to operate the engine as hot as possible to maximize thermal efficiency. Different strategies have been proposed for thermal management system such as classic state feedback controller [5], [8], optimal control methods [9], non-linear controllers [10], [11], [4], etc. In the current work, the concept of energy buffer control in the conventional trucks is presented.…”

Section: Introductionmentioning

confidence: 99%

Predictive control of the engine cooling system for fuel efficiency improvement

Khodabakhshian

Feng

Wikander

2014

2014 IEEE International Conference on Automation Science and Engineering (CASE)

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Abstract-The engine cooling system in trucks is one of the main sources of parasite load. Thus fuel efficiency can be improved by optimal control of engine thermal management system considering fuel consumption minimization as the objective. Although several optimal control methods have been proposed for the engine cooling system, their main emphasize is on regulating engine and coolant temperature in an acceptable range rather than minimizing fuel consumption. In contrast, this paper investigates the fuel saving potential of predictive optimal control methods for the engine cooling system of conventional trucks. Our method exploits the idea of energy buffers in the automotive system, where the engine cooling system and the battery serve as energy buffers. The advantages of this approach are the recovery of brake energy and the balance of energy sources so that the total energy loss is minimized. A model predictive controller is used as the real time controller, and the results are compared with a simple state feedback controller and a global optimal solution obtained by dynamic programming. The results show limited but notable improvement in fuel efficiency. The results also construct a base for ongoing research on energy buffer control in conventional heavy trucks.

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Reducing auxiliary energy consumption of heavy trucks by onboard prediction and real-time optimization

et al. 2017

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Neural Network-based Optimal Control for Advanced Vehicular Thermal Management Systems

Cited by 5 publications

References 17 publications

Heavy Duty Vehicle Cooling System Auxiliary Load Management Control: An Application of Linear Control Strategy (MIMO and SISO)

Heavy Duty Vehicle Cooling System Auxiliary Load Management Control: An Application of Linear Control Strategy (MIMO and SISO)

Predictive control of the engine cooling system for fuel efficiency improvement

Reducing auxiliary energy consumption of heavy trucks by onboard prediction and real-time optimization

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