La mesure d'impédance électrique en embarqué sur véhicule est un sujet clé pour améliorer les fonctions de diagnostic d'un pack batterie. On cherche en particulier à fournir ainsi des mesures supplémentaires à celles du courant pack et des tensions cellules, afin d'enrichir les indicateurs de vieillissement dans un premier temps, et d'état de santé et de charge dans un second temps. Une méthode classique de laboratoire pour obtenir des mesures d'impédance d'une batterie est la spectroscopie d'impédance électrochimique (ou EIS). Elle consiste à envoyer un signal sinusoïdal en courant (ou tension) de fréquence variable balayant une gamme de fréquences d'intérêt et mesurer ensuite la réponse en tension (ou courant) pour chaque fréquence. Une technique d'identification active basée sur l'utilisation des signaux large bande à motifs carrés est proposée. En particulier, des simulations ont permis de comparer les performances d'identification de différents signaux d'excitation fréquemment utilisés dans le domaine de l'identification et de vérifier les conditions correspondant à un comportement linéaire et invariant dans le temps de l'élément électrochimique. L'évaluation de la qualité d'estimation est effectuée en utilisant une grandeur spécifique : la cohérence. Cette grandeur statistique permet de déterminer un intervalle de confiance sur le module et la phase de l'impédance estimée. Elle permet de sélectionner la gamme de fréquence où la batterie respecte les hypothèses imposées par la méthode d'identification large bande. Afin de valider les résultats, une électronique de test a été conçue. Les résultats expérimentaux permettent de mettre en valeur l'intérêt de cette approche par motifs carrés. Un circuit de référence est utilisé afin d'évaluer les performances en métrologie des méthodes. L'étude expérimentale est ensuite poursuivie sur une batterie Li-ion soumise à un courant de polarisation et à différents états de charge. Des essais comparatifs avec l'EIS sont réalisés. Le cahier de charge établi à l'aide d'un simulateur de batterie Li-ion a permis d'évaluer les performances de la technique large bande proposée et de structurer son utilité pour l'estimation des états de vieillissement et de charge. / Embedded electrical impedance measurement is a key issue to enhance battery monitoring and diagnostic in a vehicle. It provides additional measures to those of the pack's current and cell's voltage to enrich the aging's indicators in a first time, and the battery states in a second time. A classical method for battery impedance measurements is the electrochemical impedance spectroscopy (EIS). At each frequency, a sinusoidal signal current (or voltage) of a variable frequency sweeping a range of frequencies of interest is at the input of the battery and the output is the measured voltage response (or current). An active identification technique based on the use of wideband signals composed of square patterns is proposed. Particularly, simulations were used to compare the performance of different excitation signals commonly used for system identification in several domains and to verify the linear and time invariant behavior for the electrochemical element. The evaluation of the estimation performance is performed using a specific quantity: the spectral coherence. This statistical value is used to give a confidence interval for the module and the phase of the estimated impedance. It allows the selection of the frequency range where the battery respects the assumptions imposed by the non-parametric identification method. To experimentally validate the previous results, an electronic test bench was designed. Experimental results are used to evaluate the wideband frequency impedance identification. A reference circuit is first used to evaluate the performance of the used methodology. Experimentations are then done on a Li–ion battery. Comparative tests with EIS are realized. The specifications are established using a simulator of Li-ion battery. They are used to evaluate the performance of the proposed wide band identification method and fix its usefulness for the battery states estimation: the state of charge and the state of health.

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.

Les batteries électriques sont omniprésentes dans notre vie quotidienne : ordinateur, téléphone, etc. Elles jouent un rôle important dans le défi de la transition énergétique : anticiper la raréfaction des énergies fossiles et réduire la pollution, en développant le stockage des énergies renouvelables et les transports électriques. Cependant, l'estimation de l'état de charge (State of Charge – SoC) d'une batterie est difficile et les modèles de prédiction actuels sont peu robustes. En effet, une batterie est un système électrochimique complexe, dont la dynamique est influencée non seulement par ses caractéristiques internes, mais aussi par les conditions d'usages souvent non contrôlables : température, profil d’utilisation, etc. Or, une estimation précise du SoC permet de garantir une utilisation sûre de la batterie en évitant une surcharge ou surdécharge ; mais aussi d’estimer son autonomie. Dans cette étude, nous utilisons un modèle à espaces d'états gouverné par une chaîne de Markov cachée. Ce modèle est fondé sur des équations physiques et la chaîne de Markov cachée permet d’appréhender les différents «régimes de fonctionnement» de la batterie. Pour garantir l’unicité des paramètres du modèle, nous démontrons son identifiabilité à partir de contraintes simples et naturelles sur ses paramètres «physiques ». L’estimation du SoC dans un véhicule électrique doit être faîte en ligne et avec une puissance de calcul limitée. Nous estimons donc le SoC en utilisant une technique d’échantillonnage préférentiel séquentiel. D’autre part l’estimation des paramètres est faîte à partir d’une base d’apprentissage pour laquelle les états de la chaîne de Markov et le SoC ne sont pas observés. Nous développons et testons trois algorithmes adaptés à notre modèle à structure latente : un échantillonneur particulaire de Gibbs, un algorithme de Monte-Carlo EM pénalisé par des contraintes d’identifiabilité et un algorithme de Monte-Carlo EM pénalisé par une loi a priori. Par ailleurs les états cachés de la chaîne de Markov visent à modéliser les différents régimes du fonctionnement de la batterie. Nous identifions leur nombre par divers critères de sélection de modèles. Enfin, à partir de données issues de trois types de batteries (cellule, module et pack d’un véhicule électrique), notre modèle a permis d’appréhender les différentes sollicitations de la batterie et donne des estimations robustes et précises du SoC. / Electric batteries are omnipresent in our daily lives: computers, smartphones, etc. Batteries are important for anticipating the scarcity of fossil fuels and tackling their environmental impact. Therefore, estimating the State of Charge (SoC) of a battery is nowadays a challenging issue, as existing physical and statistical models are not yet robust. Indeed a battery is a complex electrochemical system. Its dynamic depends not only on its internal characteristics but also on uncontrolled usage conditions: temperature, usage profile, etc. However the SoC estimation helps to prevent overcharge and deep discharge, and to estimate the battery autonomy. In this study, the battery dynamics are described by a set of physical linear equations, switching randomly according to a Markov chain. This model is referred to as switching Markov state space model. To ensure the unicity of the model parameters, we prove its identifiability by applying straightforward and natural constraints on its “physical” parameters. Embedded applications, like electric vehicles, impose online estimated with hardware and time constraints. Therefore we estimate the SoC using a sequential importance sampling technique. Furthermore the model includes two latent variables: the SoC and the Markov chain state. Thus, to estimate the parameters, we develop and test three algorithms adapted to latent structure models: particle Gibbs sampler, Monte Carlo EM penalized with identifiability constraints, and Monte Carlo EM penalized with a prior distribution. The hidden Markov states aim to model the different “regimes” of the battery dynamics. We identify their number using different model selection criteria. Finally, when applied to various data from three battery types (cell, module and pack of an electric vehicle) our model allows us to analyze the battery dynamics and to obtain a robust and accurate SoC estimation under uncontrolled usage conditions.

This thesis describes the results of two successive experiments. The first was focused on the influence of impurities in the active mass of negative electrode in PSoC (Partial State of Charge) mode, which simulates the conditions occurring in HEV (hybrid electric vehicle). In the second experiment, cells with the same additives were exposed to optimum downforce 4N/cm2, by which we wanted to prove the influence of pressure on life of VRLA (Valve Regulated Lead Acid) cells working in operating HEV conditions.

Lead-acid batteries of hybrid electric vehicles (HEV) are functioning in PSoC regime (Partial State of Charge). In PSoC regime new fail mechanisms occur. These mechanisms usually lead to nonreversible sulphation of negative electrodes. Main aim of this research is to avoid negative electrode sulphation and to improve lifetime of lead-acid accumulators used in HEV.

This work deals with lead-acid batteries operating in hybrid electric vehicles mode, i.e. in a partial state of charge. The adverse effects such as premature capacity loss and large internal resistance can markedly affect the life of a lead battery. The experiment described in this work aims to clarify the influence of the titanium dioxide in the active mass of negative electrodes especially in the life of lead acid batteries.

Master´s thesis deals with the utilization of lead-acid accumulators in hybrid electric vehicle regime. It describes the side effects known as PCL, generated during PSOC regime and connected with the length of the battery lifespan. One possibility how to eliminate these effects, is the use of additives in active mass of the negative electrode, in the form of glass fibres. The project provides the parametres and reactions of additive, that have lead to the improvement of battery properties. The purpose of this thesis is to verify the positive effect of the aditive on the lifespan of the lead-acid accumulator.

The main aim of this paper is to contribute to the elucidation of electrochemical processes. These processes occur in the investigated experimental lead-acid cell. For this purpose, electrochemical impedance spectroscopy was used. There are two methods of measurement presented. First one is used to measure impedance between electrodes. Second one is used to measure impedance of collector ribs inside positive or negative electrode. The results are represented by electrical equivalent circuits, whose elements are described in detail in the theoretical part.

The increasing demand for electric vehicles (EVs) has led to technological advancementsin the field of battery technology. State of charge (SOC) estimation is a vital function ofthe battery management system - the heart of EVs, and Kalman filtering is a commonmethod for SOC estimation. Due to the non uniformities in tuning and testing scenarios,quantifying performance of SOC estimation algorithms is difficult. Gathering data fordifferent operational scenarios is also cumbersome. In this thesis, SOC estimation algorithmsare developed and tested for a variety of scenarios like varying sensor noise andbias properties, varying state and parameter initializations as well as different initial celltemperatures. A validated and open-source simulation plant model is used to enable easygathering of data for different operational scenarios.The simulation results show that unscented Kalman filter performs better than extendedKalman filter in presence of hard nonlinearities and high initial uncertainties. However,both filters gave similar performance under nominal conditions implying that the choiceof estimation algorithms must depend on operational scenarios. Observability analysisalso gave valuable information to aid in selection of algorithms. The simulation plantmodel facilitated easy data collection for initial development of algorithms, which werethen tested successfully using a real dataset. Further testing using real datasets is requiredto enhance validation. / Den ¨okande efterfr°agan p°a elfordon har lett till teknologiska framsteg inom omr°adet batteriteknik.Estimering av batteriets laddningstillst°and ¨ar en essentiell funktion i batteristyrsystemet,hj¨artat i ett elfordon, och g¨ors ofta genom att till¨ampa metoden Kalmanfiltrering.P°a grund av varierande implementations och testmetodik i litteraturen ¨ar detsv°art att kvantifiera estimeringsalgoritmer. I denna avhandling utvecklas algoritmer f¨oratt estimera ett batteris laddningstillst°and. Algoritmerna testas f¨or olika former av sensorfeloch initialtillst°and, samt f¨or en rad olika temperaturer. En validerad datormodell avbatteri, sensorer och omgivning nyttjas f¨or att generera representativa data f¨or de olikaf¨orh°allandena.Simuleringsresultat visar att den s°a kallade doftl¨osa varianten av Kalmanfiltret (UKF)presterade b¨attre ¨an det utvidgade Kalmanfiltret (EKF) i fall d¨ar systembeteendet ¨ar mycketolinj¨art och d°a initialtillst°andet ¨ar os¨akert. Under normala f¨orh°allanden presterardock de b°ada algoritmerna likv¨ardigt, vilket antyder att valet av algoritm b¨or g¨oras medavseende anv¨andningsscenario. En observerbarhetsanalys av de olika filtervarianterna gavytterligare v¨ardefull information f¨or valet av algoritm. Efter utveckling av filtreringsalgoritmernai simuleringsmilj¨o utf¨ordes tester p°a faktiska m¨atdata med goda resultat. F¨or attfullst¨andig validering av algoritmerna kr¨avs emellertid mer utt¨ommande tester.

In commercial vehicles, where the driver overnights with the engine turned off whilestill consuming electricity, it is important to know how much the battery can bedischarged before reliable engine starting is at risk. The vehicle’s ability to crank theengine, i.e. startability, changes with the vehicle’s ambient temperature and thebatteries state of charge. The aim of this project is therefore to test the startability ofa commercial vehicle and its cranking system’s behaviour at different ambienttemperatures and battery state of charge. Physical startability tests were planned andperformed on a commercial vehicle at different temperatures inside a climatechamber. The results of these tests show the torque of the vehicle’s powertrainincreasing with lowering temperature while the cranking system’s performancedecreases. This decrease in the cranking system’s performance is a result of thebattery’s lowering ability to supply power at lower temperatures. / I tunga fordon, där chauffören spenderar natten med avstängd motor men fortsätteratt förbruka el, är det viktigt att veta hur mycket batterierna kan urladdas innanmotorn inte kan startas. Fordonets förmåga att starta motorn, det vill säga dessstartbarhet, ändras med omgivningstemperaturen och batteriernas laddnivå. Syftetmed projektet var därför att undersöka startbarheten på ett tungt fordon samt dessstartsystems beteende vid olika omgivningstemperaturer och laddningsnivåer påbatterierna. Under projektets gång planerades samt genomfördes provstarter meddet tunga fordonet vid olika omgivningstemperaturer i en klimatkammare.Provstarterna påvisade att släpmomentet på fordonets drivlina ökade med enfallande temperatur medan startsystemets prestanda försämrades. Startsystemetsförsämrade prestanda beror huvudsakligen på batteriernas försämrade förmåga attavge effekt vid lägre temperaturer.