Off-grid hybrid renewable energy systems are known as an attractive and sustainable solution for supplying clean electricity to autonomous consumers. Typically, this applies to the communities that are located in remote or islanded areas where it is not cost-effective to extend the grid facilities to these regions. In addition, the use of diesel generators for electricity supply in these remote locations are proven to be uneconomical due to the difficult terrain which translates into high fuel transportation costs. The use of renewable energy sources, coupling with the diesel generator allows for the diesel fuel to be offset. However, to date, a common design standard for the off-grid system has yet to be found and some challenges still exist while attempting to design a reliable system. These include the sizing of hybrid systems, coordination between the operation of dissimilar power generators and the fluctuating load demands, optimal utilisation of the renewable energy resources and identifying the underlying principles which reduce the reliability of the off-grid systems. In order to address these challenges, this research has first endeavoured into developing a sizing algorithm which particularly seeks the optimal size of the batteries and the diesel generator usage. The batteries and diesel generator function in filling the gap between the power generated from the renewable energy resources and the load demand. Thus, the load requirement is also an important factor in determining the cost-effectiveness of the overall system in the long run. A sensitivity analysis is carried out to provide a better understanding of the relationship between the assessed renewable energy resources, the load demand, the storage capacity and the diesel generator fuel usage. The thesis also presents the modelling, simulation and experimental work on the proposed hybrid wind-diesel-battery system. These are being implemented with a full-scale system and they are based on the off-the-shelf components. A novel algorithm to optimise the operation of a diesel generator is also proposed. The steady-state and dynamic analysis of the proposed system are presented, from both simulation and an experimental perspective. Three single-phase grid-forming inverters and a fixed speed wind turbine are used as a platform for case studies. The grid-forming inverters adopt droop control method which allows parallel operation of several grid-forming sources. Droop control-based inverters are known as independent and autonomous due to the elimination of intercommunication links among distributed converters. Moreover, the adopted fixed speed wind turbine employs a squirrel cage induction generator which is well known for its robustness, high reliability, simple operation and low maintenance. The results show a good correlation between the modelling, the experimental measurements, and the field tested results. The final stage of this research explores the effect of tower shadow on off-grid systems. Common tower designs for small wind turbine applications, which are the tubular and the lattice configurations, are considered in this work. They generate dissimilar tower shadow profiles due to the difference in structure. In this research, they are analytically modelled for a wind turbine which is being constructed as a downwind configuration. It is proven that tower shadow indeed brings negative consequence to the system, particularly its influence on battery lifetime within an off-grid system. This detrimental effect occurs when power generation closely matches the load demand. In this situation, small frequent charging and discharging cycles or the so called microcycles, take place. The battery lifetime reduction due to these microcycles has been quantified and it is proven that they are not negligible and should be taken into consideration while designing an off-grid hybrid system.

A form of laboratory tests are performed to determine battery degradation due to charging and discharging of batteries (cycling). This is done as part of quality assurance in battery production since a certain amount of degradation corresponds to the end of the battery lifetime. Currently, this requires a significant amount of cycling. Thus, if it’s possible to decrease the number of cycles required, the time and costs for battery degradation testing can be reduced. The aim of this thesis is therefore to create a model for prediction of battery lifetime while using early cycle data. Further, to assist planning regarding scale of cycle testing this study aims to examine the impact of implementing such a prediction model in production. To examine which data driven model that should be used to predict the battery lifetime at the company, extensive feature engineering is performed where measurements from specific cycles are used, inspired by the previous work of Severson et al. (2019) and Fei et al. (2021). Two models are then examined: Linear Regression with Elastic net and Support Vector Regression. To investigate the extent to which an implementation of such a model can affect battery testing capacity, two scenarios are compared. The first scenario is that of the current cycle testing at the company and the second scenario involves implementing a prediction model. The comparison then examines the time required for battery testing and the number of machines to cycle the batteries (cyclers). Based on the results obtained, the data driven model that should be implemented is a Support Vector Regression model with features relating to different battery cycling phases or measurements, such as charge process, temperature and capacity. It can also be shown that if a battery lifetime prediction model is implemented, it can reduce the time and number of cyclers required for testing with approximately 93 %, compared to traditional testing.

Data-driven prediction of battery health has gained increased attention over the past couple of years, in both academia and industry. Accurate early-stage predictions of battery performance would create new opportunities regarding production and use. Using data from only the first 100 cycles, in a data set of 124 cells where lifetimes span between 150 and 2300 cycles, this work combines parametric linear models with non-parametric Gaussian process regression to achieve cycle lifetime predictions with an overall accuracy of 8.8% mean error. This work presents a relevant contribution to current research as this combination of methods is previously unseen when regressing battery lifetime on a high dimensional feature space. The study and the results presented further show that Gaussian process regression can serve as a valuable contributor in future data-driven implementations of battery health predictions. / Datadriven prediktion av batterihälsa har fått ökad uppmärksamhet under de senaste åren, både inom akademin och industrin. Precisa prediktioner i tidigt stadium av batteriprestanda skulle kunna skapa nya möjligheter för produktion och användning. Genom att använda data från endast de första 100 cyklerna, i en datamängd med 124 celler där livslängden sträcker sig mellan 150 och 2300 cykler, kombinerar denna uppsats parametriska linjära modeller med ickeparametrisk Gaussisk processregression för att uppnå livstidsprediktioner med en genomsnittlig noggrannhet om 8.8% fel. Studien utgör ett relevant bidrag till den aktuella forskningen eftersom den använda kombinationen av metoder inte tidigare utnyttjats för regression av batterilivslängd med ett högdimensionellt variabelrum. Studien och de erhållna resultaten visar att regression med hjälp av Gaussiska processer kan bidra i framtida datadrivna implementeringar av prediktion för batterihälsa.

Internet of Things (IoT) refers to the interconnection of uniquely identifiable smart devices which enables them to participate more actively in everyday life. Among large-scale applications, machine-type communications (MTC) supported by cellular networks will be one of the most important enablers for the success of IoT. The existing cellular infrastructure has been optimized for serving a small number of long-lived human-oriented communications (HoC) sessions, originated from smartphones whose batteries are charged in a daily basis. As a consequence, serving a massive number of non-rechargeable machine-type devices demanding a long battery lifetime is a big challenge for cellular networks. The present work is devoted to energy consumption modeling, battery lifetime analysis, and lifetime-aware network design for massive MTC services over cellular networks. At first, we present a realistic model for energy consumption of machine devices in cellular connectivity, which is employed subsequently in deriving the key performance indicator, i.e. network battery lifetime. Then, we develop an efficient mathematical foundation and algorithmic framework for lifetime-aware clustering design for serving a massive number of machine devices. Also, by extending the developed framework to non-clustered MTC, lifetime-aware uplink scheduling and power control solutions are derived. Finally, by investigating the delay, energy consumption, spectral efficiency, and battery lifetime tradeoffs in serving coexistence of HoC and MTC traffic, we explore the ways in which energy saving for the access network and quality of service for HoC traffic can be traded to prolong battery lifetime for machine devices. The numerical and simulation results show that the proposed solutions can provide substantial network lifetime improvement and network maintenance cost reduction in comparison with the existing approaches. / <p>QC 20161103</p>

The need for energy efficiency in wireless communication is prevalent in all areas, but to an even greater extent in low-power and lossy networks that rely on resource-constrained devices. This degree project seeks to address the problem of modelling the battery lifetime of a duty-cycled node, participating in a wireless sensor network that is typically used in smart home and building applications. Modelling in MATLAB combined with experimentation are employed to predict the life expectancy and to validate using a hardware implementation. Various scenarios including sleepy end devices in a wireless sensor network are modelled and validated; these range from variable wake-up frequency and packet payload transmission to increasing network contention with the addition of network load. A comprehensive analysis of the main factors contributing to wasteful energy usage is provided in this thesis project, and it can be concluded that the model can estimate the battery lifetime under different testing scenarios with an error less than 5 %. / Det finns ett stort behov av energieffektivitet inom trådlös kommunikation, särskilt inom nätverk med bortfall och låg strömförbrukning där resursbegränsade enheter nyttjas. Det här examensarbetet eftersträvar att lösa problemet med att modellera batterilivslängden hos en sensoranordning med en låg driftcykel, som en del av ett trådlöst sensornätverk avsett för att tillämpas i smarta hus och byggnader. Modellering i MATLAB kombinerat med experimentering används för att förutsäga den förväntade livslängden samt för att validera en hårdvaruimplementering. Flertalet scenarier med sovande noder modelleras och valideras, med allt från variabel uppvakningsfrekvens och paketöverföring till ökande resurskonflikter med ytterligare belastning på nätverket. I detta examensprojekt inkluderas en heltäckande analys av huvudorsakerna till energislöseriet hos enheterna och slutsatsen kan dras att modellen kan beräkna batterilivslängden för olika testscenarier med mindre än 5 % fel.

The purpose of this thesis is to predict the lifespan of a battery using a predictive model, utilizing data from early cycles. The goal is to minimize both time and costs for the company by reducing the number of cycles needed for testing. Currently, the company tests a diverse set of batteries, which is both time and resource-consuming. To investigate which data-driven predictive model should be used by the company to predict battery capacity at XX cycles, a thorough literature study has been conducted. In summary, a variety of variables from specific cycles have been calculated based on inspiration from Fei et al. (2021), Severson et al. (2019), Enholm et al. (2022) and an internal project from the company. Following this, two different predictive models, Gaussian Process Regression and Ordinary Least Squared Regression, are applied and compared.  Based on the obtained results, Gaussian Process Regression had a slight better results but a significantly higher complexity compared to Ordinary Least Squared Regression. Therefore, the data-driven model that should be implemented at the company is an Ordinary Least Squared Regression with variables related to different phases during a cycle. This result is primarily based on the varying degrees of complexity of the models. / Syftet med detta examensarbete är att med hjälp av en datadriven prediktionsmodell kunna prediktera livslängden på ett batteri genom att använda data från tidiga cykler. Målet är att minimera både tid och kostnader för företaget genom att minska antalet cykler som behövs för testning. I dagsläget testar företaget en mängd batterier vilket både är tids- samt resurskrävande. För att undersöka vilken datadriven prediktionsmodell som bör användas av företaget för att prediktera batteriekapacitet vid XX cykler har en gedigen litteraturstudie utförts. Sammanfattningsvis har en mängd variabler av de mätningar som finns från specifika cykler beräknats utifrån inspiration från Fei med flera (2021), Severson med flera (2019), Enholm med flera (2022) samt ett internt projekt från företaget. Efter detta applicerades och jämfördes två olika prediktionsmodeller: Gaussian Process Regression och Ordinary Least Squared Regression.  Baserat på de erhållna resultaten hade Gaussian Process Regression något bättre resultat men en betydligt högre komplexitet jämfört med Ordinary Least Squared Regression. Därför är den datadrivna modell som bör implementeras på företaget en Ordinary Least Squared Regression med variabler relaterade till olika faser under en cykel. Detta resultat grundar sig framför allt i olika grad av komplexitet hos modellerna.