Battery is the sole electrical energy source when electric vehicle(EV) is moving. To reduce traveling anxiety, an effective energy management system to indicate the state-of-charge (SOC) of the battery and make a balance between vehicle performance and endurance is very important. This research is aimed to develop a SOC estimation system with high accuracy. The proposed method in this thesis is based on under load voltage and multilevel Peukert's equation to estimate the SOC. The proposed method is compared with the open circuit voltage method for initial SOC estimation and with coulometric method for cumulative SOC estimation under various EV driving conditions simulated by an adjustable electronics load. Experimental results indicate that the proposed method can provide reasonable accuracy as compared with other tested methods for LiFePO4 battery SOC estimations.

Due to the discrepancy among series-connected cells in a lead-acid battery pack, the restored capacities may not be the same during the charging/discharging processes. Through repeated charging and discharging, one can find that the unbalance of individual series-connected battery cell of lead-acid battery is getting worse, because those cells of poor state of charge may have negative voltage, causing reverse charging from the other cells. This abnormal operation results in not only more energy consumption, but also the acceleration of the battery-cell aging and the remarkable reduction of its lifetime. For the purpose of investigating this problem, the operating characteristics of each cell is under study. The battery cells are purposely charged by different floating voltages and discharged by different cutoff voltages. The investigation results show that the lifecycles of battery cells are affected more significantly by floating charging voltage than by the discharging cutoff voltage. It is also found that a long time reverse charging has a destructive effect on the battery cells.

This thesis describes the development of a system to continuously monitor the battery in a car and warn the user of an upcoming battery failure. An automotive battery endures enormous strain when it starts the engine, and when it supplies loads without the engine running. Note that the current during a cranking event often exceeds 500 Amperes. Despite the strains, a car battery still typically lasts 4-6 years before requiring replacement. There is often no warning of when a battery should be replaced and there is never a good time for a battery failure. All currently available lead-acid battery monitoring systems use voltage and current sensing to monitor battery impedance and estimate battery health. However, such a system is costly due to the current sensor and typically requires an expert to operate the system. This thesis describes a prototype system to monitor battery state of health and provide advance warning of an upcoming battery failure using only voltage sensing. The prototype measures the voltage during a cranking event and determines if the battery is healthy or not. The voltage of an unhealthy battery will drop lower than a healthy one, and it will not recover as quickly. The major contributions of the proposed research to the field are an algorithm to predict automotive battery state-of-health that is temperature-dependent and a prototype implementation of the algorithm on an ARM processor development board. / Master of Science

The work in this thesis is focused on the development and validation of an automotive battery monitoring system that estimates the health of a lead-acid battery during engine cranking and provides a low state of health (SOH) warning of potential battery failure. A reliable SOH estimation should assist users in preventing a sudden battery failure and planning for battery replacement in a timely manner. Most commercial battery health estimation systems use the impedance of a battery to estimate the SOH with battery voltage and current; however, using a current sensor increases the installation cost of a system due to parts and labor. The battery SOH estimation method with the battery terminal voltage during engine cranking was previously proposed. The proposed SOH estimation system intends to improve existing methods. The proposed method requires battery voltages and temperature for a reliable SOH estimation. Without the need for a costly current sensor, the proposed SOH monitoring system is cost-effective and useful for automotive applications. Measurement results presented in this thesis show that the proposed SOH monitoring system is more effective in evaluating the health of a lead-acid battery than existing methods. A low power microcontroller equipped prototype implements the proposed SOH algorithm on a high performance ARM Cortex-M4F based MCU, TM4C123GH6PM. The power dissipation of the final prototype is approximately 144 mW during an active state and 36 mW during a sleep state. With the reliability of the proposed method and low power dissipation of the prototype, the proposed system is suitable for an on-board battery monitoring as there is no on-board warning that estimates the health of a battery in modern cars. / Master of Science

Tesis para optar al grado de Doctora en Ingeniería Eléctrica / La disponibilidad de energía es una de las limitaciones que presentan las Redes de Sensores Inalámbricas (WSN, Wireless Sensor Network). Tradicionalmente, las baterías han sido utilizadas para proveer energía a los nodos de sensores y al tener una vida útil limitada afectan el tiempo de vida de la red. Soluciones como el uso de baterías de gran tamaño o el reemplazo de ellas no son viables, debido al gran número de sensores que componen la red y a que pueden ser desplegados en zonas de difícil acceso. Esta situación ha motivado que las soluciones para la conservación de la energía en las WSNs se enfoquen en el desarrollo de técnicas que actúen a nivel de las capas física y de enlace de datos, como es el caso de los protocolos de Control de Acceso al Medio (MAC, Medium Access Control). Los protocolos MAC son una de las soluciones ampliamente estudiadas y utilizadas porque permiten un equilibrio entre la conservación de energía y otros parámetros críticos de la red, como el rendimiento, latencia, reducción de colisiones y mensajes de control. También tienen la facilidad de adaptarse a las nuevas aristas de trabajo que surgen al incorporar nuevas tecnologías como lo son los Dispositivos de Recolección de Energía (EHD, Energy Harvesting Device). Otro aspecto que está siendo considerado y estudiado en el diseño de los protocolos MAC es la información que se puede extraer de la batería, ya que al estimar la capacidad disponible de la misma, el mecanismo del Duty Cycling (DuC) puede ser ajustado con el propósito de aumentar la eficiencia energética y por lo tanto, extender la vida útil de la red. Es necesario desarrollar técnicas que incorporen un mecanismo de conservación de energía que integre información de la batería a través de indicadores como el Estado de Carga (SOC, State of Charge) y Estado de Salud (SOH, State of Health) para mejorar la eficiencia energética en WSN. La idea de incorporar información de la batería se debe a que la capa MAC está a cargo de controlar los modos de operación del nodo sensor, lo que está directamente relacionado con la cantidad de corriente exigida a la batería. Conocidos los perfiles de uso de la batería es posible estimar los indicadores SOC y SOH que se han utilizado ampliamente en diversas aplicaciones para conocer la cantidad de energía disponible en la batería y la degradación que ha sufrido la misma. En este trabajo se propuso el desarrollo de un protocolo que actúa en la subcapa MAC y que considera la información de la batería para tomar decisiones con respecto al tiempo activo y de reposo del nodo de sensor, con la finalidad de promover el uso eficiente de la energía y extender la vida útil de la red. Los resultados obtenidos validan esta nueva propuesta de algoritmo y establecer pautas para guiar el diseño de protocolos MAC que se centren en minimizar el consumo de energía teniendo en cuenta los dispositivos de recolección de energía y la información de la batería.

Magíster en Ciencias de la Ingeniería, Mención Eléctrica / Ingeniero Civil Eléctrico / Actualmente existe una gran cantidad de equipos y dispositivos que utilizan baterías como su fuente primaria o secundaria de energía. Para estos sistemas es crítico contar con información del desempeño de sus baterías, dado que este conocimiento puede ayudar a tomar decisiones apropiadas y asegurar autonomía en el tiempo. Dos importantes variables que deben ser monitoreadas son el "Estado-de-Salud" (SOH, del inglés State-of-Health) y el "Estado-de-Carga" (SOC, del inglés State-of-Charge). Este trabajo se enfoca en generar esquemas de pronóstico para ambas variables, donde se tome en cuenta la temperatura de operación. Con este propósito, se diseñaron y realizaron un conjunto de pruebas de laboratorio con celdas de Ion-Litio donde se caracterizó el impacto de la temperatura en factores tales como la energía entregada en un ciclo, la impedancia interna, o tendencia de degradación. A partir de estos datos, y esquemas existentes en la literatura, se proponen modelos empíricos para la degradación y para la descarga de una batería mediante una representación de espacio-estados, definiendo directamente un estado como el SOH y el SOC respectivamente. Las estimaciones y predicciones a largo plazo se efectúan bajo un enfoque Bayesiano, basado en el filtro de partículas. Además, se propone la implementación de lazos de control externos para corregir condiciones iniciales erróneas de los estados, y un módulo de detección de outliers para trabajar con datos perdidos o inválidos. La validación de estos esquemas se realiza con datos generados en laboratorio, además de datos de degradación publicados por NASA Ames Prognostic Center of Excellence. El esquema propuesto para el SOH es capaz de incorporar explícitamente el efecto de la temperatura de operación (bajo el concepto de "Capacidad Usable"), y estimar y pronosticar el SOH a una temperatura de referencia. Por otro lado, el esquema para el SOC fue validado incluyendo una mejor representación de la fenomenología del proceso de descarga comparada a la existente, y se deja una propuesta de cómo incluir el efecto de la temperatura en el modelo. La implementación de estos esquemas de pronóstico permite la incorporación de la temperatura de operación, que a pesar de su gran influencia en el comportamiento de las baterías es considerada constante en muchos casos presentes en la literatura; además de incluir algunas mejoras prácticas en los algoritmos de estimación. Las propuestas de este trabajo dejan las bases para avanzar en la incorporación de otros fenómenos importantes como la profundidad de descarga, o la magnitud de la corriente de descarga.

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Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-ion batteries are increasingly being considered in EVs due to their high energy density, slow loss of charge when not in use, and for lack of hysteresis effect. Conventionally, the batteries are connected in series to achieve the load voltage requirements. However, for the batteries with intrinsic discrepancies or different initial states, cell balancing is a concern because it is the weakest cell that determines the empty point for the battery and an undercharged series cell will shorten the lifetime of the entire pack. The imbalance potential of the battery behaves as the way of State-of-Charge (SOC) mismatch and it’s also temperature dependent. Therefore, in this thesis, an active cell balancing optimization was proposed and conducted in MATLAB to optimize battery unused capacity and thermal effect simultaneously based on bi-directional balancing system and pre-estimated SOC. The bi-directional balancing system was physically built based on “Fly-back” converter to compare balancing performance in discharging, idle, and plug-in charging mode. Moreover, a battery combined model worked collaboratively with robust state and parameter estimation strategies, namely Extended Kalman Filter (EKF) and Smooth Variable Structure Filter (SVSF) in order to estimate SOC for cell balancing. As a result, the proposed method can effectively optimize SOC mismatch around 2.5%. Meanwhile, more uniform temperature was achieved and the maximum temperature can be reduced about 7 ℃. / Thesis / Master of Applied Science (MASc)

In battery management systems, the main figure of merit is the battery's SOC, typically obtained from voltage and current measurements. Present estimation methods use simplified battery models that do not fully capture the electrical characteristics of the battery, which are useful for system design. This thesis studied SOC estimation for a lithium-ion battery using a nonlinear, electrical-circuit battery model that better describes the electrical characteristics of the battery. The extended Kalman filter, unscented Kalman filter, third-order and fifth-order cubature Kalman filter, and the statistically linearized filter were tested on their ability to estimate the SOC through numerical simulation. Their performances were compared based on their root-mean-square error over one hundred Monte Carlo runs as well as the time they took to complete those runs. The results show that the extended Kalman filter is a good choice for estimating the SOC of a lithium-ion battery.

The electricity price changes depending on the time of the day in most countries. In Sweden there is a spot price that changes every hour while China uses Time of Use (ToU) tariff. To avoid the most expensive hours this degree project investigates the feasibility of using lithium- ion batteries to shift the load of an industrial air compressor. The Depth of Discharge and the State of Charge (SoC) for the battery are analyzed to find the optimal use of the battery. Through simulations in MATLAB the degradation-curve and State of Charge were analyzed, which was further used for economics analysis. The feasibility of the system is evaluated by using payback time and Net Present Value (NPV). Results show that a battery has a slightly longer lifetime when it is working in a SoC of 50-70%, but a larger SoC is more profitable from the perspective of NPV. For the SoC of 0-100%, the NPV is about ~9683 US$. Compared to Sweden, using batteries to shift load is more profitable in China, which is mainly due to the high electricity prices. For the same air compressor, the payback time is 5 and 15 years for the investment of batteries in China and Sweden respectively.