<div>This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine efficiency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.</div>

Global climate change and the electricity supply constraints could possibly be one of the utmost strategic issues facing businesses and consumers of all sizes currently in South Africa. Energy Efficiency is the ability to produce the same output but with less energy. The implementation of Energy Efficiency strategies is pivotal in order to sustain the climatic conditions as well as mitigate the supply constraints the South African utility Eskom is experienced. The aim of this study was to reiterate the importance of energy efficiency strategies and to identify the barriers and challenges companies face towards implementing energy efficiency and energy management strategies. This dissertation identified incentives and rebate schemes available to promote energy efficiency strategies and discussed the policies and strategies the South African Government implemented towards realising the energy efficiency target of 12%.The literature review conclude with discussing best practices indentified by implementing corporate energy efficiency strategies. The level of preparedness and progress in implementing an energy management system and strategies between the different companies were assessed. The target population includes the high intensity user group (HIU), listed and SME companies in the different industry sectors in South Africa. The study concludes that there are still multiple challenges facing companies in implementing sustainable energy efficiency strategies. Although government and multiple stakeholders are initiating incentive and rebate models to promote the implementation of energy efficiency measures, industry still lacks the commitment to change their behaviour toward implementing energy management strategies. / Thesis (MBA)--North-West University, Potchefstroom Campus, 2013.

Global climate change and the electricity supply constraints could possibly be one of the utmost strategic issues facing businesses and consumers of all sizes currently in South Africa. Energy Efficiency is the ability to produce the same output but with less energy. The implementation of Energy Efficiency strategies is pivotal in order to sustain the climatic conditions as well as mitigate the supply constraints the South African utility Eskom is experienced. The aim of this study was to reiterate the importance of energy efficiency strategies and to identify the barriers and challenges companies face towards implementing energy efficiency and energy management strategies. This dissertation identified incentives and rebate schemes available to promote energy efficiency strategies and discussed the policies and strategies the South African Government implemented towards realising the energy efficiency target of 12%.The literature review conclude with discussing best practices indentified by implementing corporate energy efficiency strategies. The level of preparedness and progress in implementing an energy management system and strategies between the different companies were assessed. The target population includes the high intensity user group (HIU), listed and SME companies in the different industry sectors in South Africa. The study concludes that there are still multiple challenges facing companies in implementing sustainable energy efficiency strategies. Although government and multiple stakeholders are initiating incentive and rebate models to promote the implementation of energy efficiency measures, industry still lacks the commitment to change their behaviour toward implementing energy management strategies. / Thesis (MBA)--North-West University, Potchefstroom Campus, 2013.

In searching for more efficient vehicles with lower carbon emissions, researchers have invested enormous time and resources in designing new materials, components, systems, and control methods. The result is not only an immense volume of publications and patents but also a true electrification revolution in the transportation sector. Although the advancements are remarkable, much is still to be developed. Energy management systems are often designed to fulfil drive cycles that represent just a fraction of the actual use of the vehicles, disregarding essential factors such as driving conditions that may vary in real life. Furthermore, control algorithms should not ignore one of the most relevant driving aspects, comfort. Driving should be a pleasant activity since we spend much time of our lives performing this task. This research proposes a novel algorithm for managing energy consumption in electrified vehicles, the regen-based equivalent consumption minimization strategy (R-ECMS). Its suitability for solving the power-split problem is evaluated. Experiments emulating labelling schedules are conducted considering an example application. Robustness to different drive cycles and flexibility of the algorithm to various modes of operation are assessed. Furthermore, the method is integrated into an autonomous longitudinal control. The function leverages vehicle dynamics and journey mapping to assure energy efficiency and adequate drivability. Finally, the tests are conducted using human-driven cycles leveraging driving simulation technology. That allows for including driver subjective feelings in the design and assessing the algorithm's performance in realistic driving conditions. / Thesis / Doctor of Philosophy (PhD)

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.

Mobility is a topic of great interest in research and engineering since critical aspects such as safety, traffic efficiency, and environmental sustainability still represent wide open challenges for researchers and engineers. In this thesis, at first, we address the cooperative driving safety problem both from a centralized and decentralized perspective. Then we address the problem of optimal energy management of hybrid vehicles to improve environmental sustainability, and finally, we develop an intersection management systems for Connected Autonomous Vehicle to maximize the traffic efficiency at an intersection. To address the first two topics, we define a common framework. Both the cooperative safety and the energy management for Hybrid Electric Vehicle requires to model the driver behavior. In the first case, we are interested in evaluating the safety of the driver’s intentions, while in the second case, we are interested in predicting the future velocity profile to optimize energy management in a fixed time horizon. The framework is the Co-Driver, which is, in short, a bio-inspired agent able both to model and to imitate a human driver. It is based on a layered control structure based on the generation of atomic human-like longitudinal maneuvers that compete with each other like affordances. To address driving safety, the Co-Driver behaves like a safe driver, and its behavior is compared to the actual driver to understand if he/she is acting safely and providing warnings if not. In the energy management problem, the Co-Driver aims at imitating the driver to predict the future velocity. The Co-Driver generates a set of possible maneuvers and selects one of them, imitating the action selection process of the driver. At first, we address the problem of safety by developing and investigating a framework for Advanced Driving Assistance Systems (ADAS) built on the Co-Driver. We developed and investigated this framework in an innovative context of new intelligent road infrastructure, where vehicles and roads communicate. The infrastructure that allows the roads to interact with vehicles and the environment is the topic of a research project called SAFESTRIP. This project is about deploying innovative sensors and communication devices on the road that communicate with all vehicles. Including vehicles that are equipped with Vehicle-To-Everything (V2X) technology and vehicles that are not, using an interface (HMI) on smart-phones. Co-Driver-based ADAS systems exploit connections between vehicles and (smart) roads provided by SAFESTRIP to cover several safety-critical use cases: pedestrian protection, wrong-way vehicles on-ramps, work-zones on roads and intersections. The ADAS provide personalized warning messages that account for the adaptive driver behavior to maximize the acceptance of the system. The ability of the framework to predict human drivers’ intention is exploited in a second application to improve environmental sustainability. We employ it to feed with the estimated speed profile a novel online Model Predictive Control (MPC) approach for Hybrid Electric Vehicles, introducing a state-of-the-art electrochemical model of the battery. Such control aims at preserving battery life and fuel consumption through equivalent costs. We validated the approach with actual driving data used to simulate vehicles and the power-train dynamics. At last, we address the traffic efficiency problem in the context of autonomous vehicles crossing an intersection. We propose an intersection management system for Connected Autonomous Vehicles based on a bi-level optimization framework. The motion planning of the vehicle is provided by a simplified optimal control problem, while we formulate the intersection management problem (in terms of order and timing) as a Mixed Integer Non-Linear Programming. The latter approximates a linear problem with a powerful piecewise linearization technique. Therefore, thanks to this technique, we can bound the error and employ commercial solvers to solve the problem (fast enough). Finally, this framework is validated in simulation and compared with the "Fist-Arrived First-Served" approach to show the impact of the proposed algorithm.

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<p dir="ltr">Electric aircraft offer a promising avenue for reducing aviation's environmental impact through decreased greenhouse gas emissions and noise pollution. Nonetheless, their adoption is hindered by the challenge of limited operational range. Addressed in the study is the range limitation by integrating and optimizing multiple energy storage components—hydrogen fuel cells, Li-ion batteries, and ultracapacitors—through advanced energy management strategies. Utilizing meta-heuristic optimization methods, the research assessed the dynamic performance of each energy component and the effectiveness of the energy management strategy, primarily measured by the hydrogen consumption rate. MATLAB simulations validated the proposed approach, indicating a decrease in hydrogen usage, thus enhancing efficiency and potential cost savings. Artificial Gorilla Troop Optimization yielded the best results with the lowest average hydrogen consumption rate (102.62 grams), outperforming Particle Swarm Optimization (104.68 grams) and Ant Colony Optimization (105.96 grams). The findings suggested that employing a combined energy storage and optimization strategy can significantly improve the operational efficiency and energy conservation of electric aircraft. The study highlighted the potential of such strategies to extend the range of electric aircraft, contributing to a more sustainable aviation future.</p>