Evaluation of the energy management strategies for a hybrid pneumatic engine

Cette thèse porte sur l évaluation de plusieurs stratégies de gestion d énergie pour un nouveau concept de moteur hybride pneumatique. Ce concept combine un moteur à combustion interne avec un système de stockage d énergie sous forme d air comprimée. Une soupape supplémentaire relie alors la chambre de combustion à un réservoir d air et permet un fonctionnement en mode moteur pneumatique ou pompe pneumatique (récupératif). La première stratégie, Causale, est basée sur des principes heuristiques. La deuxième, à Coefficient de Pénalité Constant, vise la minimisation d un critère énergétique global. Un coefficient de pondération permet de mettre en opposition, pour un travail donné, les coûts énergétiques d un mode pneumatique d une part et d un mode thermique d autre part. Le mode offrant le coût le plus faible sera choisi. La troisième stratégie, à Coefficient de Pénalité Variable, sur le même principe utilise un coefficient de pondération variable selon la quantité d énergie pneumatique disponible. Une stratégie, à reconnaissance de situation de conduite, permet d adapter les stratégies à la situation reconnue (par exemple, embouteillage, autoroutier). Enfin, la dernière stratégie tente de recopier la solution optimale de référence (obtenue par programmation dynamique) à l aide d un modèle. Toutes les stratégies ont été validées en simulation sur cycles standards. De plus une méthode, basée sur les chaînes de Markov, de constructions de cycle de conduite artificiels mais réalistes est proposée. Les consommations obtenues avec les différentes stratégies proposées sont comparées en référence aux consommations minimales atteignables. Les résultats montrent que 40% de gain de consommation peuvent être atteints.This thesis presents a study of several energy management strategies for a novel hybrid pneumatic engine concept. The concept combines an internal combustion engine with a system of compressed air for energy storage. An additional charge valve connects the combustion chamber to an air pressure tank, enabling the engine to function in pneumatic motor mode or as a pneumatic pump (recuperation mode). The first strategy is called Causal and implements a rule-based control technique. The second one, called Constant Penalty Coefficient, is derived from optimal control theory and is based on an equivalent consumption minimization strategy. A penalty coefficient is introduced to evaluate, for a given torque demand, the respective energy costs of the two modes, pneumatic and conventional, enabling the mode offering the lowest cost to be chosen. The third strategy, called Variable Penalty Coefficient, is based on the same principle but uses a variable penalty coefficient depending on the amount of pneumatic energy available in the compressed air tank. Another strategy investigated, called Driving Pattern Recognition, adapts the strategies to the driving situation recognized (for example, traffic jam, or highway). The last strategy studied attempts to reproduce the optimal reference solution obtained by dynamic programming, using a neural mode. All the strategies have been validated by simulation on standard driving cycles. In addition, a method based on the Markov chain process have been develop to make artificial yet realistic driving cycles. The consumptions obtained with the various strategies are compared with the minimal consumptions achievable. Results demonstrate that 40% of fuel saving can be achieved on certain cycles. Several of the strategies proposed give results that are close to optimal.ORLEANS-SCD-Bib. electronique (452349901) / SudocSudocFranceF

Measurement of Operator-machine Interaction on a Chaku-chaku Assembly Line

Assembly operations in the automotive industry represent a substantial proportion of overall manufacturing time and total manufacturing cost. With product complexity increasing year after year, humans continue to remain a cost-effective solution to the needs of flexible manufacturing. The human element is largely marginalized in Manufacturing 2.0 and necessitates a better understanding of the human\u27s impact on the future of manufacturing. The work herein illustrates a method through the use of the Industrial Internet of Things (IIoT) to capture ubiquitous data streams from human and automated machinery with the intention to make available the data necessary and elucidate the potential to deepen the understanding of the human impact on Industry 4.0 assembly systems

Measurement of Operator-machine Interaction on a Chaku-chaku Assembly Line

Assembly operations in the automotive industry represent a substantial proportion of overall manufacturing time and total manufacturing cost. With product complexity increasing year after year, humans continue to remain a cost-effective solution to the needs of flexible manufacturing. The human element is largely marginalized in Manufacturing 2.0 and necessitates a better understanding of the human\u27s impact on the future of manufacturing. The work herein illustrates a method through the use of the Industrial Internet of Things (IIoT) to capture ubiquitous data streams from human and automated machinery with the intention to make available the data necessary and elucidate the potential to deepen the understanding of the human impact on Industry 4.0 assembly systems