Switching from fossil-fuel-powered vehicles to electric vehicles has become an international focus in the pursuit of combatting climate change. Regardless, the adoption of electric vehicles has been slow, in part, due to range anxiety. One solution to mitigating range anxiety is to provide a more accurate state of charge (SOC) and range estimation. SOC estimation of lithium-ion batteries for electric vehicle application is a well-researched topic, yet minimal tools and code exist online for researchers and students alike. To that end, a publicly available Kalman filter-based SOC estimation function is presented. The MATLAB function utilizes a second-order resistor-capacitor equivalent circuit model. It requires the SOC-OCV (open circuit voltage) curve, internal resistance, and equivalent circuit model battery parameters. Users can use an extended Kalman filter (EKF) or adaptive extended Kalman filter (AEKF) algorithm and temperature-dependent battery data. A practical example is illustrated using the LA92 driving cycle of a Turnigy battery at multiple temperatures ranging from -10C to 40C. Current range estimation methods suffer from inaccuracy as factors including temperature, wind, driver behaviour, battery voltage, current, SOC, route/terrain, and much more make it difficult to model accurately. One of the most critical factors in range estimation is the battery. However, most models thus far are represented using equivalent circuit models as they are more widely researched. Another limitation is that any machine learning-based range estimation is typically based on historical driving data that require odometer readings for training. A range estimation algorithm using a machine learning-based voltage estimation model is presented. Specifically, the long short-term memory cell in a recurrent neural network is used for the battery model. The model is trained with two datasets, classic and whole, from the experimental data of four Tesla/Panasonic 2170 battery cells. All network training is completed on SHARCNET, a resource provided by Canada Compute to researchers. The classically trained network achieved an average root mean squared error (RMSE) of 44 mV compared to 34 mV achieved by the network trained on the whole dataset. Based on the whole dataset, all test cases achieve an end range estimation of less than 5 km with an average of 0.29 km. / Thesis / Master of Applied Science (MASc)

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In the past decade, automobile manufacturers have gone through the initial adoption phase of electric mobility. The increasing momentum behind electric vehicles (EV) suggests that electrified storage systems will play an important role in electric mobility going forward. Lithium ion batteries have become one of the most common solutions for energy storage due to their light weight, high specific energy, low self-discharge rate, and non-memory effect. To fully benefit from a lithium-ion energy storage system and avoid its physical limitations, an accurate battery management system (BMS) is required. One of the key issues for successful BMS implementation is the battery model. A robust, accurate, and high fidelity battery model is required to mimic the battery dynamic behavior in a harsh environment. This dissertation introduces a robust and accurate model-based approach for lithium-ion battery management system. Many strategies for modeling the electrochemical processes in the battery have been proposed in the literature. The proposed models are often highly complex, requiring long computational time, large memory allocations, and real-time control. Thus, model-order reduction and minimization of the CPU run-time while maintaining the model accuracy are critical requirements for real-time implementation of lithium-ion electrochemical battery models. In this dissertation, different modeling techniques are developed. The proposed models reduce the model complexity while maintaining the accuracy. The thermal management of the lithium ion batteries is another important consideration for a successful BMS. Operating the battery pack outside the recommended operating conditions could result in unsafe operating conditions with undesirable consequences. In order to keep the battery within its safe operating range, the temperature of the cell core must be monitored and controlled. The dissertation implements a real-time electrochemical, thermal model for large prismatic cells used in electric vehicles' energy storage systems. The presented model accurately predicts the battery's core temperature and terminal voltage. / Thesis / Doctor of Philosophy (PhD)

In this research, the focus was on the development of a Battery Cycler system with the primary objective of accurately predicting both the State of Charge (SoC) and State of Health (SoH) for batteries. Machine learning techniques, specifically MLP Regression, K-Nearest Neighbor, and Decision Tree Regression, were harnessed for a comprehensive analysis. The data collection and processing phase spanned 44 days. The findings underscore the potential of employing relatively uncomplicated machine learning models for the prediction of SoC and SoH. Particularly noteworthy was the strong performance of K-Nearest Neighbor, especially after deliberate optimization efforts were applied. Despite the simplicity of these techniques, the results suggest significant promise for battery management and health assessment. Nevertheless, challenges linked to temperature fluctuations and current noise were identified as factors impacting predictive performance. Mitigating these challenges is imperative to enhance the robustness and precision of predictive models in future iterations. The implications of this work extend to broader applications in battery management systems, offering insights into potential avenues for optimizing battery usage, extending longevity, and enhancing overall performance. Leveraging machine learning methodologies in a straightforward manner, this research is anticipated to contribute to advancements in battery health monitoring and management, setting the stage for more intricate models in subsequent studies. / I denna forskning har vi tagit oss an utvecklingen av ett battericykelsystem med målet att noggrant förutsäga både laddningstillstånd (SoC) och hälsotillstånd (SoH) för batterier. Genom att utnyttja maskininlärningstekniker utförde vi en omfattande analys med hjälp av MLP-regression, K-Nearest Neighbor och Decision Tree regression. Över en tidsperiod av 44 dagar samlades batteridata in och bearbetades noggrant. Resultatet understryker möjligheten att använda relativt okomplicerade maskininlärningsmodeller för att förutsäga både laddningstillstånd och hälsotillstånd. Särskilt K-Nearest Neighbor framstår som en lovande kandidat med tanke på förutsägbarhetsnoggrannheten den visat, särskilt efter medvetna ansträngningar för optimering. Trots teknikernas enkelhet antyder våra resultat en betydande potential för batterihantering och hälsobedömning. Emellertid har utmaningar som temperaturvariationer och strömbuller identifierats som faktorer som påverkar förutsägelseprestanda. Att bemöta dessa utmaningar är avgörande för att öka robustheten och precisionen i våra förutsägelsemodeller i framtida utföranden.. Denna forsknings arbetsresultat sträcker sig till bredare användningsområden inom batterihanteringssystem och erbjuder insikter i möjliga riktningar för att optimera batterianvändning, livslängd och övergripande prestanda. Genom att utnyttja maskininlärningsmetoder förutser vi att denna forskning kommer att bidra till framsteg inom övervakning och hantering av batterihälsa, och lägga grunden för mer sofistikerade modeller i framtiden.

Les véhicules hybrides se démocratisent avec une utilisation croissante des éléments de stockage à base de lithium-ion. Dans ce contexte d'exploitation, le type d'usage est atypique et dépend fortement des stratégies de répartition des énergies au sein du véhicule. Parmi les hybridations, la catégorie "mild hybrid" conserve la motorisation thermique pour l'autonomie qu'elle apporte, et lui adjoint une machine électrique associée à un élément de stockage réversible, afin de permettre une récupération de l'énergie cinétique du véhicule. L'objet de ces travaux porte sur la mise en place d'algorithmes destinés à la détermination des états de charge (SoC), de santé (SoH) et de fonction (SoF) de chacune des cellules qui compose un pack batterie lithium-ion. Ces fonctionnalités sont implantées dans un système de gestion dénommé BMS pour Battery Management System. Dans un souci de réduction des coûts de production, nos travaux s'attachent à limiter la puissance de calcul et les moyens de mesure nécessaires à la détermination de ces états. À partir de mesures effectuées lors d'une utilisation de la batterie dans une application "mild hybrid", les méthodes développées permettent la détermination des états, ainsi que d'une partie des paramètres internes aux cellules. Cette utilisation est caractérisée par de forts courants et un maintien de l'état de charge autour de 50 %, ceci afin de maximiser la disponibilité de la batterie et d'en minimiser le vieillissement. L'utilisation d'observateurs et de méthodes en boucle ouverte, à partir d'une modélisation simplifiée de cellule, nous permet d'obtenir des résultats satisfaisants avec une puissance de calcul réduite / Hybrid vehicles are developing with increasing use of energy storage elements based on lithium-ion battery. In this context, the use of battery is atypical and highly dependent on energy allocation strategies within the vehicle. Among these vehicles, the mild hybrid category retains heat engine for the autonomy that offer and adds to it an electric machine associated with a reversible storage system, to allow the kinetic energy recovery of the vehicle. The object of this work involves the development of algorithms for determining the states of charge (SoC) and health (SoH) and function (SoF) of each cell that compose a lithium-ion battery pack. These features are implemented in a Battery Management System (BMS) for industrial production. In order to reduce production costs, our work attempts to limit the computing power and the measuring sensors necessary for these states determination. From battery measurements in a "mild hybrid" use, developed methods allow the states determination, as well as some of the internal parameters of cells. This application is characterized by high currents and maintaining a SoC of around 50%, in order to maximize the availability of the battery and to minimize aging. The use of observers and estimators, using a simplified model cell, allows us to achieve satisfactory results with a reduced computing power

Lead acid batteries are widely used in domestic, industrial and automotive applications. Even after lot of advancements in battery technologies, lead acid cells are still in use because of their high capacity and low cost. To use any battery effectively, first we should be able to identify the available capacity or State of Charge (SoC). There are many techniques available to measure SoC of a lead acid battery. One such unique method is to measure the capacity using eddy current sensors. This method is unique because it is non-obtrusive and online. Eddy current sensors (ECS) are wire wound inductors which work on the principle of electromagnetic induction. Eddy currents are the currents generated on a conductive material when it is kept in a varying magnetic. Eddy current sensors generate varying magnetic eldest and will be able to identify the properties of conductive materials like thickness, conductivity, material composition etc. Also they can be used as proximity sensors. Lead acid batteries use lead metal as cathode. Upon usage(discharge) the lead metal converts to lead sulfate and revert back to lead after charging. These changes in lead electrode can be monitored using eddy current sensors. The impedance of an eddy current sensor will change when it is kept close to the lead electrode when the battery is charging or discharging. These impedance parameters can be monitored to determine the battery SoC. When lead is deposited on cathode, there will be more eddy current loss in the target and the total resistance of coil increases. On the other hand, when lead is deposited on the electrode because of increase in the magnitude of eddy currents which oppose the source magnetic, the total inductance of coil decreases. We can observe exactly opposite behaviour of coil resistance and inductance when the lead electrode is converted to less conductive lead sulfate. There is a lot of research on using ECS to measure SoC of lead acid batteries and there are still many challenges to be addressed. First we have explained about different circuit designs we have used to monitor the battery capacity using eddy current sensors. After that, we have explained about our complete experimental setup and the procedure to measure the sensor parameters using the setup. Then, we have discussed about different issues involved in the eddy current sensing based state of charge measurement. Eddy current sensors are affected by temperature variations. We have studied the coil resistance behaviour with temperature at different frequencies using simulations and experiments. We have obtained the conditions for linear variation of coil resistance with temperature. The measured temperature compensation scheme is applied and the results are discussed. We have also modified the measurement system design in order to minimize the lift o errors. We have used a metallic clamp structure to minimize the lift o errors. We have used finite element analysis based simulations to study different design parameters and their effect on the sensitivity of eddy current sensor. We have created 2D eddy current models and the sensitivity of coil resistance is computed by changing the coil dimensions and the core permeability. We have also performed error analysis and computed the error due to the tilt angle shift between coil and electrode. We have also computed the error due to the internal heating of battery. We have also studied the effect of acid strati cation on state of charge for both sealed and hooded batteries. We have proposed a multi coil method to minimize the errors in SoC measurement due to acid strati cation for Flooded type batteries. We have used finite element analysis based simulations to compute the error due to acid strati cation by increasing the number of coils. Finally we have derived the equation for electrode Q factor using the transformer model of eddy current sensor. The derived Q factor equation is then used to study the aging of lead acid batteries both by using experiments and simulations. Finally we have explained a detail procedure to measure the state of charge(SoC) and state of health(SoH) of a hooded lead acid battery using eddy current sensing method.

Följande studie är utförd på uppdrag av företaget Nilar som tillverkar Nickel-Metallhydridbatterier (NiMH-batterier) vid sin produktionanläggning i Gävle. Den nuvarande beräkningen av State of Charge (SoC) sker på deras Battery Management Unit (BMU) och är implementerad i Structured Text i exekveringsmiljön CODESYS. Nilar vill flytta SoC-beräkningen från BMU:n så att den kan exekveras på en Interface Control Unit (ICU). Motiveringen till detta är för att distribuera SoC-beräkningen då ett flertal ICU:er finns tillgängliga per Battery Management System (BMS) men även för att i framtiden helt byta ut CODESY. Syftet med denna studie är att migrera implementationen av SoC-algoritmen till programmeringsspråket C så att algoritmen senare kan exekveras på ICU:n. Därefter optimeras algoritmen för att sänka exekveringstiden. Studien utforskar kodstrukturella och funktionella skillnader mellan implementationerna samt metoder för att optimera SoC-algoritmen. Migreringen av algoritmen fullföljdes utan större inverkan på noggrannheten. Algoritmen optimerades genom att skapa en variant av en LU-faktorisering som var specifikt anpassad för det aktuella problemet. Optimeringen av algoritmen resulterade i en minskning på 25% av den totala exekveringstiden för algoritmen. De nya implementationerna tar markant längre tid att exekvera då batteriet befinner sig under laddning jämfört när det befinner sig under urladdning, någonting som inte kan noteras för den gamla implementationen. / The following study was carried out on the behalf of Nilar, which manufactures Nickel–metal hydride batteries at its production site in Gävle. The current State of Charge (SoC) calculation is done on their Battery Manegment Unit (BMU) and is implemented in Structured Text for the CODESYS runtime. Nilar wants to move the SoC calculation from the BMU so that its executed on a Interface Control Unit (ICU). The reasoning behind this is to distribute the SoC computation as several ICUs are available per Battery Management System (BMS) but also to remove the CODESYS dependency in the future. The purpose of this study is to migrate the implementation of the SoC-algorithm to the programming language C so that the algorithm can be executed on an ICU in the future. Furthermore this study aims to optimize the the algorithm to lower the execution time. The study explores differences in code structure and functionallity between the implementations as well as methods to optimize the SoC algorithm. The migration of the algorithm was completed without major impact on the accuracy. The algorithm was optimized by creating a variant of a LU factorization that was specifically suited to LU factorize the given problem. The optimization of the algorithm resulted in a 25% lower total execution time. The new implementations suffers from a longer total execution time when the battery is charging compared to when it’s discharging, something that’s not prevalent for the old implementation.

Pour assurer une énergie électrique de qualité et de façon permanente, il existe des systèmes électroniques d’alimentation spécifiques. Il s’agit des Alimentations Sans Interruptions (ASI). Une ASI comme tout autre système peut tomber en panne ce qui peut entrainer une perte de redondance. Cette perte induit une maintenance corrective donc une forme d’indisponibilité ce qui représente un coût. Nous proposons dans cette thèse de travailler sur deux composants parmi les plus sensibles dans les ASI à savoir les condensateurs électrolytiques et les batteries au plomb. Dans une première phase, nous présentons, les systèmes de surveillance existants pour ces deux composants en soulignant leurs principaux inconvénients. Ceci nous permet de proposer le cahier des charges à mettre en œuvre. Pour les condensateurs électrolytiques, nous détaillons les différentes étapes de caractérisation et de vieillissement ainsi que la procédure expérimentale de vieillissement standard accéléré et les résultats associés. D’autre part, nous présentons les résultats de simulation du système de surveillance et de prédiction de pannes retenu. Nous abordons la validation expérimentale en décrivant le système développé. Nous détaillons les cartes électroniques conçues, les algorithmes mis en œuvre et leurs contraintes d’implémentation respectifs pour une réalisation temps réel. Enfin, pour les batteries au plomb étanches, nous présentons les résultats de simulation du système de surveillance retenu permettant d’obtenir le SOC et le SOH. Nous détaillons la procédure expérimentale de vieillissement en cycles de charge et décharge de la batterie nécessaire pour avoir un modèle électrique simple et précis. Nous expliquons les résultats expérimentaux de vieillissement pour finir avec des propositions d’amélioration de notre système afin d’obtenir un SOH plus précis. / To ensure power quality and permanently, some electronic system supplies exist. These supplies are the Uninterrupted Power Supplies (UPS). An UPS like any other system may have some failures. This can be a cause of redundancy loss. This load loss causes a maintenance downtime which may represent a high cost. We propose in this thesis to work on two of the most sensitive components in the UPS namely electrolytic capacitors and lead acid batteries. In a first phase, we present the existing surveillance systems for these two components, highlighting their main drawbacks. This allows us to propose the specifications which have to be implemented for this system. For electrolytic capacitors, we detail different stages of characterization ; the aging accelerated standard experimental procedure and their associated results. On the other hand, we present the simulation results of monitoring and failure prediction system retained. We discuss the experimental validation, describing the developed system. We detail the electronic boards designed, implemented algorithms and their respective constraints for a real time implementation. Finally, for lead acid batteries, we present the simulation results of the monitoring system adopted to obtain the SOC and SOH. We describe the aging experimental procedure of charging and discharging cycles of the batteries needed to find a simple and accurate electric models. We explain the aging experimental results and in the end we give suggestions for improving our system to get a more accurate SOH.

Cada vez mais robôs móveis autônomos estão sendo utilizados em diversas tarefas e em ambientes com elevado risco para atividades humanas que a paralisação de suas atividades podem gerar outros riscos, perdas e elevados custos. Assim, o estado de carga (SOC) de sistemas de baterias em robôs móveis autônomos é um parâmetro importante na prevenção de uma falha primária nessa aplicação, a ausência de energia. Este trabalho apresenta os métodos existentes na literatura para a determinação do estado de carga de baterias e as tecnologias de baterias disponíveis utilizadas em robôs móveis autônomos ou veículos autônomos guiados. A partir desses estudos foi desenvolvido um modelo de medida, baseado no modelo combinado e foram realizados testes de bancadas para levantamento dos parâmetros e características de três modelos de células de baterias: Lítio Polímero (Li-PO), Níquel-Cádmio (NiCd) e Lítio-Ferro-Polímero (LiFePO4). Com esses parâmetros, aplicou-se o método de estimativa de carga baseado na técnica do Filtro de Kalman Estendido (EKF). Através dos testes, analisou-se comparativamente a resposta do método proposto e a resposta do método OCV e a capacidade de carga real. / Autonomous mobile robots have being increasingly used in various tasks, environments and activities of high risk to human that the stoppage of its activities may generate other risks, losses and high costs. Thus the state of charge (SOC) of battery systems in autonomous mobile robots, is an important parameter to prevent a primary failure in this application, the lack of energy. The paper presents the existing methods in the literature to determine the battery state of charge and battery commercial technologies available used in an autonomous mobile robot or autonomous guided vehicle, from these studies a measurement model based on combined model was developed and testing benches for three cells models on Lithium Polymer Battery (Li-PO), Nickel Cadmium (NiCd) and lithium-iron-Polymer (LiFePO4) batteries were performed for lifting the parameters and apply the battery state of charge method based on the Extended Kalman Filter (EKF) technique. The tests were analyzed in order to observe the comparatively response of the proposed method, the OCV method and Real charge capacity.

Ce mémoire traite de la modélisation électrique des accumulateurs lithium-ion, de l’estimation de leur état de charge (SOC) et de leur état de santé (SOH). Le premier chapitre revient sur les généralités concernant la technologie lithium-ion : caractéristiques, performances, constitution de l’élément de stockage, choix et nature des électrodes, conséquences qui en découlent d’un point de vue énergétique. Le principe de fonctionnement et les équations générales des phénomènes électrochimiques sont aussi développés. Des exemples d’application dans différents secteurs industriels sont ensuite proposés pour plusieurs gammes de puissance et d’énergie. Le second volet aborde la modélisation électrique des accumulateurs lithium-ion. Pour une meilleure compréhension des phénomènes complexes mis en jeu au sein des batteries, des éléments de modélisation physique sont exposés. Puis nous envisageons une synthèse des différents modèles de nature électrique rencontrés dans la littérature. Sur la base de campagnes de mesures menées sur un élément lithium-ion de 6,8 Ah, nous proposons, dans un troisième chapitre, notre propre modèle électrique équivalent valable pour les phases de décharge et de relaxation. En particulier nous déclinons plusieurs solutions pour distribuer l’énergie et rendre compte des différents effets de ligne. Les outils de caractérisation et les procédures d’extractions des paramètres sont traités en détail. Dans un dernier chapitre nous étudions les possibilités d’estimer en ligne l’état de charge (SOC) et l’état de santé (SOH) d’un élément lithium-ion en cours d’exploitation. Après un bref rappel des méthodes académiques et industrielles actuelles, nous nous orientons vers l’emploi d’un filtre de Kalman. Afin d’estimer ses performances par rapport au coulombmètre, nous proposons un modèle et un algorithme que nous évaluons par simulation et testons sur élément réel / This dissertation of thesis deals with the electrical modelling of lithium-ion accumulators and the determination of both state-of-charge (SOC) and state-of-health (SOH). The first chapter is focused on generalities about lithium-ion technology: characteristics, qualities, constitution of the storage device, choice and nature of the electrodes and their consequences on energetical features. The principle and the general equations of the electrochemical phenomena are developed as well. Application examples from different industrial areas are displayed for several power and energy ranges. The second section is about the electrical modelling of lithium-ion accumulators. With a view to better understand the complex electrochemical phenomena, elements of physical modelling are proposed. Then, the synthesis of different electrical models released in the press is considered. On the basis of experimental campaigns lead on a 6.8 Ah lithium-element, we proposed, in a third chapter, our own equivalent electrical model suitable for both discharge phases and relaxation period. In particular, we depict several alternatives to distribute the energy and describe the different line effects. Both characterization tools and parameters extraction procedure are clearly detailed. In the last section, we tackle both SOC and SOH on-line determination. After a short review of academicals and industrial solutions, we rapidly head towards the use of a Kalman filter. In order to compare its features versus the coulombmeter, we propose a model and an algorithm, numerical simulations and experimental tests are performed