Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

Various commercial software packages are available for simulating compressed air network operations. However, none of these software packages are able to dynamically prioritise compressor selection on large compressed air networks in the mining industry. In this dissertation, a dynamic compressor selector (DCS) will be developed that will actively and continuously monitor system demand. The software will ensure that the most suitable compressors, based on efficiency and position in the compressed air network, are always in operation. The study will be conducted at a platinum mine. Compressed air flow and pressure requirements will be maintained without compromising mine safety procedures. Significant energy savings will be realised. DCS will receive shaft pressure profiles from each of the shafts’ surface compressed air control valves. These parameters will be used to calculate and predict the compressed air demand. All pipe friction losses and leaks will be taken into account to determine the end-point pressure losses at different flow rates. DCS will then prioritise the compressors of the compressed air network based on the overall system requirement. This software combines the benefits of supply-side and demand-side management. Potential energy savings with DCS were proven and compressor cycling reduced. A DCS user-friendly interface was created to easily set up any mine’s compressed air network. / Thesis (MIng (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2013

Various commercial software packages are available for simulating compressed air network operations. However, none of these software packages are able to dynamically prioritise compressor selection on large compressed air networks in the mining industry. In this dissertation, a dynamic compressor selector (DCS) will be developed that will actively and continuously monitor system demand. The software will ensure that the most suitable compressors, based on efficiency and position in the compressed air network, are always in operation. The study will be conducted at a platinum mine. Compressed air flow and pressure requirements will be maintained without compromising mine safety procedures. Significant energy savings will be realised. DCS will receive shaft pressure profiles from each of the shafts’ surface compressed air control valves. These parameters will be used to calculate and predict the compressed air demand. All pipe friction losses and leaks will be taken into account to determine the end-point pressure losses at different flow rates. DCS will then prioritise the compressors of the compressed air network based on the overall system requirement. This software combines the benefits of supply-side and demand-side management. Potential energy savings with DCS were proven and compressor cycling reduced. A DCS user-friendly interface was created to easily set up any mine’s compressed air network. / Thesis (MIng (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2013

Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

This research presents a two-part study on a fuel cell electric van (FCEV), focusing on vehicle modelling and developing different control strategies for the modelled vehicle. The modelling phase accounts for the aging effects on the fuel cell (FC) and battery, analyzing FCEV behavior over time. This includes estimating and integrating the degradation impacts on characteristic curves, such as the FC’s polarization and efficiency curves, the battery’s charging and discharging resistance curves, and the open-circuit voltage curve. A simplified fuel cell system (FCS) model is designed to consider power losses in multiple components, including the FC stack, air compressor, and others. The dynamic limits of the FC are also included to yield more realistic results. The model is based on the vehicle Opel Vivaro FC specifications, incorporating parameters like maximum FC power, battery capacity, vehicle weight, and tire dimensions. Subsequently, various control strategies are applied to analyze their effectiveness in FC and battery State-of-Health (SOH) degradation and hydrogen consumption. A rule-based energy management system (EMS) is implemented first, which operates with five different operational modes dependent on the vehicle’s state. This is followed by a look-up table (LUT) based strategy, which uses two two-dimensional tables generated by a Neural Network (NN). The network is trained with discretized optimal / Thesis / Master of Applied Science (MASc)

Ce mémoire présente une étude comparative de quatre stratégies de gestion énergétique temps réel, appliquées d'une part à un véhicule hybride thermique-électrique, et d'autre part à un véhicule électrique à pile à combustible : contrôle basé sur des règles empirique (RBS), minimisation de la consommation équivalente (A-ECMS), loi de commande optimale (OCL) établie à partir d'une modélisation analytique du système et programmation dynamique stochastique (SDP) associée à une modélisation des cycles de conduite par chaîne de Markov. Le principe du minimum de Pontryaguin et la programmation dynamique, applicables hors ligne, sont mis en œuvre pour fournir des résultats de référence. Les problèmes d’implémentation numérique et de paramétrage des stratégies sont discutés. Une analyse statistique effectuée sur la base de cycles aléatoires générés par chaînes de Markov permet d’évaluer la robustesse des stratégies étudiées. Les résultats obtenus en simulation, puis sur un dispositif expérimental montrent que les méthodes les plus simples (RBS ou OCL) conduisent à des consommations élevées. SDP aboutit aux meilleures performances avec en moyenne la plus faible consommation de carburant dans les conditions réelles de conduite et un état énergétique final du système de stockage parfaitement maîtrisé. Les résultats d’A-ECMS sont comparables à ceux de SDP en moyenne, mais avec une plus grande dispersion, en particulier pour l'état de charge final. Afin d'améliorer les performances des méthode, des jeux de paramètres dédiés aux différents contextes de conduite sont considérés. / This thesis presents a comparative study between four recent real-time energy management strategies (EMS) applied to a hybrid electric vehicle and to a fuel cell vehicle applications: rule-based strategy (RBS), adaptive equivalent consumption minimization strategy (A-ECMS), optimal control law (OCL) and stochastic dynamic programming (SDP) associated to driving cycle modeling by Markov chains. Pontryagin’s minimum principle and dynamic programming are applied to off-line optimization to provide reference results. Implementation and parameters setting issues are discussed for each strategy and a genetic algorithm is employed for A-ECMS calibration.The EMS robustness is evaluated using different types of driving cycles and a statistical analysis is conducted using random cycles generated by Markov process. Simulation and experimental results lead to the following conclusions. The easiest methods to implement (RBS and OCL) give rather high fuel consumption. SDP has the best overall performance in real-world driving conditions. It achieves the minimum average fuel consumption while perfectly respecting the state-sustaining constraint. A-ECMS results are comparable to SDP’s when using parameters well-adjusted to the upcoming driving cycle, but lacks robustness. Using parameter sets adjusted to the type of driving conditions (urban, road and highway) did help to improve A-ECMS performances.