Systèmes de contrôle pour la gestion thermique d'un vehicule

Les systèmes de refroidissement des véhicules continuent à se développer et devenir de plus en plus complexes. Ceci introduit des nouveaux problèmes dus aux interactions des composants et les perturbations du système. Avec la montée des prix des carburants; les développeurs et les compagnies cherchent à améliorer la consommation en respectant les normes d’émission. Une partie de l’énergie produite par le moteur est utilisé par les composants du circuit de refroidissement. L’utilisation d’auxiliaires électriques est une manière de réduire ces pertes parasites, mais ce n’est pas la seule solution. Des études récents proposent que un control plus adaptes des composants peux réduire la consommation de carburant. Actuellement, le groupe Volvo en essayant d’améliorer la performance du système de refroidissement des camions a installe des nouveaux composants pour la gestion thermique du moteur. Néanmoins, des problèmes ont été identifie lors d’essais véhicule. Une meilleure compréhension du système et de l’implémentation de composants est nécessaire pour limiter les effets non voulus. Le système de refroidissement d’un poids lourd a été étudié grâce à l’outil Bond Graph. Puis des nouvelles stratégies de control sont introduites : commande prédictive, commande par platitude, commande sans model et commande avec model réduit. Ces méthodes ont été implémentées dans une plateforme de simulation sur Matlab/Simulink. Les gains de consommation obtenue à partir de simulations sont entre 0.5 et 0.9%. Une analyse structurelle de l’architecture actuelle est présentée. D’après les conclusions de cette analyse, des propositions pour la modification de l’architecture du circuit sont évalués.The increasing complexity of engine cooling systems results in added interactions and disturbances to the performance. Besides, non-propulsion loads (fan, water pump…) draw a significant percentage of the engine’s power thus lowering the vehicle’s fuel efficiency. Recent studies have shown that by controlling components the efficiency can be improved by adjusting fan speed according to cooling needs, coolant flow, and oil flow. Currently, the Volvo group in order to optimize the performance of their truck’s cooling systems had installed new thermal management components. However, problems were found while testing control strategies and a better understanding of the interaction between components is required to prevent this from happening again. In this work, the bond graph approach has been applied for the study of the cooling system of a Heavy duty vehicle and has enabled subsystem interactions to be identified. Based on a simplified model issued from the bond graph, several control strategies have been built. These controllers are based on different control approaches: model predictive control, flatness control, model free control and model free control with reduced order model. These controllers were implemented in a simulation platform in the Matlab/Simulink environment. Results of the implementation of the new advanced control strategies are given. Fuel economy gains ranged between 0.5 and 0.9 %. A structural analysis of the current architecture is also proposed aiming at the optimization of the system. Given the insights from the analysis, an assessment of new concepts for the cooling system architecture is proposed

Heavy Duty Vehicle Cooling System Auxiliary Load Management Control: a Comparison of Advanced Control Strategies

International audienc

Heavy Duty Vehicle Cooling System Auxiliary Load Management Control: a Comparison of Advanced Control Strategies

International audienc

Heavy Duty Vehicle Cooling System Auxiliary Load Management Control: Evaluating the Maximum Gain of Implementing an Advanced Control Strategy

International audienceVehicle thermal management covers the engineering field of solutions that maintain the complete vehicle in acceptable operating conditions regarding component and fluid temperatures in an engine. The maximum efficiency rating of a Diesel engine reaches up to 45%.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…).This paper introduces a dynamic programming algorithm which is used to determine the maximum gains during operation for a given architecture of the cooling system of a Heavy Duty Truck. The algorithm, based in Bellman principle, will determine the best control trajectory for the pump and the fan according to energetic and control goals (fuel economy, regulation of temperature…). For this purpose, a reduced model of the cooling system based on energy balance equations has been determined and validated through simulations.An optimal control path is determined by the algorithm for a given cycle, which is then used as an input to the controllers in a simulation platform for the same reference driving cycle in order to calculate more accurately the energy consumption. The simulation platform is built in GT-Suite (system model) and Matlab/Simulink (inputs). The result show how it is possible to reduce the energy consumption by optimizing the control strategy of the cooling system, in particular hints are given in terms of thermostatic valve threshold tuning

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

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

Impact analysis of the electrified auxiliary system on HEV/PHEV energy efficiency

International audienceIn this paper, we present a completed Hybrid/Plug-in Hybrid Electric Vehicle (HEV/PHEV) model including an Electrified Auxiliary System (EAS), and an impact analysis of this auxiliary system on the vehicle energy efficiency. Our approach focus-es only on three auxiliaries (an electrified air supply system, an electrified refrigerator, and an electric hydraulic power steering) but could be applied on other devices. Numerical simulation shows that this system has a high impact on the energy efficiency of the HEV/PHEV, while its energy consumption could reach up to 18.6% of the total energy requirement for vehicle propulsion. For the purpose of improving the vehicle energy efficiency thank to the electrified auxiliary management, an optimization approach based on forward dynamic programming is developed to determine the optimal auxiliary power consumption profile. The results of this work conduct to some prominent suggestions for the design of the electrified auxiliary management

Impact analysis of the electrified auxiliary system on HEV/PHEV energy efficiency

International audienceIn this paper, we present a completed Hybrid/Plug-in Hybrid Electric Vehicle (HEV/PHEV) model including an Electrified Auxiliary System (EAS), and an impact analysis of this auxiliary system on the vehicle energy efficiency. Our approach focus-es only on three auxiliaries (an electrified air supply system, an electrified refrigerator, and an electric hydraulic power steering) but could be applied on other devices. Numerical simulation shows that this system has a high impact on the energy efficiency of the HEV/PHEV, while its energy consumption could reach up to 18.6% of the total energy requirement for vehicle propulsion. For the purpose of improving the vehicle energy efficiency thank to the electrified auxiliary management, an optimization approach based on forward dynamic programming is developed to determine the optimal auxiliary power consumption profile. The results of this work conduct to some prominent suggestions for the design of the electrified auxiliary management