Online impedance spectroscopy estimation of a dc–dc converter connected battery using a switched capacitor-based balancing circuit

This study investigates a novel method of undertaking online electrochemical impedance spectroscopy measurements to estimate battery impedance across the frequency range using a battery balancing circuit. A switched capacitor balancing system is used to generate an excitation signal of low-frequency of variable values from which battery voltage and current can be measured to estimate the impedance

Parameter-independent battery control based on series and parallel impedance emulation

Appropriate voltage control is essential in order to extend the useful life of a battery. However, when universal chargers are used, the design of this control becomes more complicated, given the fact that the battery impedance value may vary considerably, depending not only on the operating point but also on the type, size and aging level of the battery. This paper firstly shows how the voltage regulation can become extremely variable or even unstable when the controller is designed according to the proposals in the literature. We then go on to propose the emulation of a series and parallel impedance with the battery, which is easy to implement and achieves a control that is completely independent of the battery connected. The simulation results obtained for batteries with resistances ranging from 10 mO to 1 O, show the problems with existing controls and confirm that the proposed control response is similar for all the possible range of battery resistances.Peer ReviewedPostprint (published version

A Single- and Three-Phase Grid Compatible Converter for Electric Vehicle On-Board Chargers

This article proposes a voltage-source converter for an on-board electric vehicle charger, which is compatible with both the single- and three-phase (1-Φ and 3-Φ) grids. The classic 3-Φ active ac-dc rectifier circuit is used for both the 1-Φ and 3-Φ connections, but a new control scheme and LCL filter are designed to address the double-line frequency power pulsation issue caused by a 1-Φ grid without using bulky dc capacitors. The third leg of the circuit is utilized to control the power pulsation in conjunction with stored energy in the LCL filter between the grid and charger rectifier. Neither additional active nor passive components are required. For the 3-Φ connection, the rectifier is under balanced operation; when connected with the 1-Φ grid, all three legs are controlled cooperatively as a 3-Φ rectifier but under unbalanced operation to recreate the 1-Φ voltage. Hence, advantages from the 3-Φ rectifier, such as space vector pulsewidth modulation and Y/Δ transformation, can be utilized to increase utilization of dc-link voltage and filter capacitance, respectively. The operation principle, control, and LCL filter design are reported and validated by both simulation and experiments of a 3-kW porotype

Otimização do Processo de Carregamento de Baterias Li-ion com base em Inteligência Artificial

A evolução das tecnologias de armazenamento de energia está, nos dias de hoje, associada à recorrente transformação do atual paradigma da produção da energia elétrica e à forma como é consumida. Por questões de necessidade, e não apenas por conveniência, é cada vez mais inevitável o armazenamento da energia elétrica que não pode ser consumida no momento em que é produzida, para garantir sustentabilidade energética. Portanto, por revelarem alta densidade energética, alta eficiência e uma maior longevidade, as baterias de iões de lítio são uma tecnologia de armazenamento de energia que têm sido recentemente aplicadas no armazenamento de energia proveniente da produção de origem renovável, bem como no sector dos veículos elétricos/híbridos e nos dispositivos portáteis. Assim sendo, este trabalho tem como objetivo o desenvolvimento de um método de carregamento, que tem por base um algoritmo de inteligência artificial designado como redes neuronais, que privilegie a minimização da deterioração precoce das baterias com o propósito de prolongar o seu tempo de vida útil. Para a elaboração deste método proposto o trabalho desenvolvido pode ser descrito em duas fases. A primeira é referente à análise e estudo da resistência interna de baterias com diferentes níveis de envelhecimento, onde serão comparados e verificados os valores da resistência interna em várias condições de carregamento. Isto possibilitará uma concepção real do estado de vida das baterias e também do modo como estas se comportam durante um carregamento. A segunda fase consiste no desenvolvimento de um método de carregamento com o recurso à implementação de uma rede neuronal. O método proposto minimizará a deterioração das baterias e garantirá um carregamento adaptado e ideal para as suas condições. Os resultados obtidos são comparados com métodos tradicionais existentes na literatura.The evolution of energy storage systems is nowadays associated with the recurrent transformation of the current paradigm of electric energy production and the way it is consumed. For reasons of necessity, and not just for convenience, it is increasingly unavoidable to store electrical energy that cannot be consumed when it is produced to guarantee energy sustainability. Therefore, as they demonstrate high energy density, high efficiency and longer longevity, lithium-ion batteries are an energy storage technology that recently has been applied to the storage of energy from renewable sources, as well as in the vehicle electric / hybrid sector and portable devices. Therefore, this work has the objective of developing a charging method, based on an artificial intelligence algorithm called neural networks, which focuses on the minimization of early deterioration of the batteries with the purpose of prolonging their useful life. For the elaboration of this proposed method the work developed can be reconciled in two phases. The first one refers to the analysis and study of the internal resistance of batteries with different levels of aging, where the values of the internal resistance for various conditions will be compared and verified. This will allow a real conception of the state of life of the batteries as well as the way it behaves during a charging. The second phase consists in the development of a charging method with the implementation of a neural network. The proposed method will minimize the deterioration of batteries and guarantee an adapted and ideal charge for their conditions. The results obtained are compared with traditional methods in the literature

Desenvolvimento de Métodos Inteligentes de Carregamento e Balanceamento para Sistemas de Armazenamento

Entre a grande diversidade de baterias existentes tecnologicamente maduras, as baterias de Iões de Lítio (Li-ion) tornaram-se populares devido à sua longevidade, alta densidade de energia, alta eficiência e não sofrerem de efeito de memória. Este trabalho tem como objetivo desenvolver novos métodos de carregamento que incluam balanceamento para sistemas de baterias de Iões de Lítio. Este pode ser dividido em duas fases distintas: a primeira fase tem que ver com o desenvolvimento de toda a plataforma, a segunda fase refere-se ao desenvolvimento de novos algoritmos. De forma resumida a primeira fase teve como objetivo desenvolver todo o hardware e software necessários para a criação de uma plataforma versátil e intuitiva onde fosse possível criar algoritmos de carregamento e balanceamento de forma eficiente. O sistema principal do hardware implementado trata-se do Battery Management System (BMS), responsável por toda a monitorização do pack de baterias utilizado. A segunda fase do trabalho focou-se no desenvolvimento de novos algoritmos inteligentes de carregamento que incluam o balanceamento. A opção por esta abordagem refere-se ao facto de ser essencial controlar os desequilíbrios entre células durante o carregamento, resultando numa maior eficiência do sistema de armazenamento. Os resultados obtidos são comparados com os algoritmos tradicionais existentes na literatura.Among the wide diversity of existing technologically mature batteries, lithium-ion (Li-ion) batteries have become popular because of their longevity, high energy density, high efficiency and lack of memory effect. This work aims to develop new charging methods that include balancing for lithium-ion battery systems. This can be divided into two distinct phases: the first phase has to do with the development of the entire platform; the second phase refers to the development of new algorithms. Briefly in the first phase was developed all the hardware and software required to create a versatile and intuitive platform where it was possible to create charging and balancing algorithms. The main hardware implemented system is the Battery Management System (BMS), responsible for all the monitoring of the battery pack used. The second phase of the work focused on developing new intelligent charging algorithms that include balancing. The option for this approach has to do with the fact that it is essential to control the imbalances between cells during charging, resulting in greater efficiency of the storage system. The obtained results are compared with the traditional algorithms present in the literature

Investigation of different methods of online impedance spectroscopy of batteries

A key challenge in a battery energy storage system is understanding the availability and reliability of the system from the perspective of the end customer. A key task in this process is recognising when a battery or a module within a system starts to degrade and then mitigating against this using the control system or battery management system. Battery characterisation parameters such as internal impedance and state of health and state of charge of the battery are a useful representation of the battery conditions. This thesis investigates the feasibility of undertaking Electrochemical Impedance Spectroscopy (EIS) methods online to generate an understanding of battery impedance. In order to perform an EIS measurement, an excitation signal of fixed frequency must be generated and the voltage and current measured and used to calculate the impedance. This thesis proposed different methods of generating a low-frequency excitation signal using hardware found in most battery systems to extract the harmonic impedance of a battery cell to aim towards a low cost on-line impedance estimation. This work focuses on producing impedance spectroscopy measurements through the power electronics system, a battery balancing system and the earth leakage monitoring system to attempt to get comparable results to off-line EIS measurements under similar conditions. To generate an excitation signal through the power electronic circuit, different control methods were used including varying; the duty cycle, the switching frequency and the starting position of the switched wave and the addition of an impulse type function. Although utilising a variable duty cycle to generate a harmonic impedance has been previously published in literature, the other techniques analysed within this these have not previously been considered. The thesis looks at the theoretical analysis of the circuits and control techniques and then follows this up with simulation and experimental studies. The results showed that all the methods investigated have the capability to generate a low frequency perturbation signal to undertake online EIS measurement. However, there are potential trade-offs, for example increased inductor ripple current. Not all of the methods produce sufficiently accurate results experimentally .However, five of the methods were used to generate EIS plots similar to those undertaken offline