This work deals with design of battery management system. Requirements for battery management system and its conception is discussed in the first part of the work. System is able to disconnect load or charger from battery using MOS-FET transistors. It measures battery cell's voltages and is capable of passive balancing. Microcontroller is used for data processing and system control. Schematics, printed circuit board layout and control algorithm was designed. Prototype of the battery management system was then manufactured and tested.

No description available.

No description available.

No description available.

The (Battery management system)BMS has the task of ensuring that for the individual bat-tery cell parameters such as the allowed operating voltage window or the allowable temperature range are not violated. Since the battery itself is a highly distinct nonlinear electrochemical de-vice it is hard to detect its internal characteristics directly. The requirement of predicting battery packs’ present operating condition will become one of the most important task for the BMS. Therefore, special algorithms for battery monitoring are required.In this thesis, a model based battery state estimation technique using an adaptive filter tech-nology is investigated. Different battery models are studied in terms of complexity and accuracy. Following up with the introduction of different adaptive filter technology, the implementation of these methods into battery management system is decribed. Evaluations on different estimation methods are implemented from the point of view of the dynamic performance, the requirement on the computing power and the accuracy of the estimation. Real test drive data will be used as a reference to compare the result with the estimation value. Characteristics of different moni-toring methods and models are reported in this work. Finally, the trade-offs between different monitor’s performance and their computational complexity are analyzed. / BMS (eng. battery management system) har till uppgift att se till att viktiga parametrar såsom tillspännings- och temperaturintervall upprätthålls för varje individuell battericell. Då en battericells beteende är ickelinjärt är det svårt att bestämma cellens interna karakteristika direkt. Att kunna förutsäga dessa karakteristika för ett komplett batteripack kommer att en mycket viktig funktion hos framtida BMS. I detta examensarbete har en modellbaserad tillståndsestimeringsmetod med användande av adaptiv filtrering undersökts. Olika batterimodeller har studerats med avseende på komplexitet och noggrannhet. Efter introduktionen av olika metoder för adaptiv filtrering har dessa metoder implementerats i en BMS modell. Utvärdering av de olika metoderna för att åstadkomma tillståndsestimering har sedan utförts med avseende på dynamisk prestanda, krav på beräkningskraft och noggrannhet hos de resulterande estimaten. Data från uppmätta kördata från ett fordon har använts som referens för att jämföra de olika estimaten. Slutligen presenteras en jämförelse mellan de olika tillståndsestimeringsmetodernas prestanda när de appliceras på de olika batterimodellerna.

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)

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

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.

L'utilisation de systèmes de stockage de l'énergie électrique tels que les batteries nécessite l'assemblage de plusieurs cellules. Comme chacune de ces dernières peut avoir des caractéristiques légèrement différentes ainsi que des conditions d'environnement thermique ou de vieillissement distinctes, l'utilisation d'un système d'équilibrage permettant une bonne gestion de la répartition de l'énergie au sein des éléments qui composent le pack est nécessaire. Les travaux de recherche présentés se rapportent à l'étude et à la conception d'un tel circuit d'équilibrage à base de convertisseurs d'énergie intégrables ainsi qu'aux sollicitations électriques engendrées dans les cellules lors de son utilisation. / To store electrical energy in batteries, the use of multiple cells is needed. Since each of these cells can have slightly different characteristics and also different thermal or aging environmental conditions, a balancing system is required to manage the energy inside the battery pack. The researches described in this document show the study and the design of such a balancing system based on power electronics converters and also the impact of these systems on the electrochemical cells.

No description available.