Smart Battery Technology for Lifetime Improvement

Applications of lithium-ion batteries are widespread, ranging from electric vehicles to energy storage systems. In spite of nearly meeting the target in terms of energy density and cost, enhanced safety, lifetime, and second-life applications, there remain challenges. As a result of the difference between the electric characteristics of the cells, the degradation process is accelerated for battery packs containing many cells. The development of new generation battery solutions for transportation and grid storage with improved performance is the goal of this paper, which introduces the novel concept of Smart Battery that brings together batteries with advanced power electronics and artificial intelligence (AI). The key feature is a bypass device attached to each cell that can insert relaxation time to individual cell operation with minimal effect on the load. An advanced AI-based performance optimizer is trained to recognize early signs of accelerated degradation modes and to decide upon the optimal insertion of relaxation time. The resulting pulsed current operation has been proven to extend lifetime by up to 80% in laboratory aging conditions. The Smart Battery unique architecture uses a digital twin to accelerate the training of performance optimizers and predict failures. The Smart Battery technology is a new technology currently at the proof-of-concept stage

Electric Vehicles Charging Stations’ Architectures, Criteria, Power Converters, and Control Strategies in Microgrids

Electric Vehicles (EV) usage is increasing over the last few years due to a rise in fossil fuel prices and the rate of increasing carbon dioxide (CO2) emissions. The EV charging stations are powered by the existing utility power grid systems, increasing the stress on the utility grid and the load demand at the distribution side. The DC grid-based EV charging is more efficient than the AC distribution because of its higher reliability, power conversion efficiency, simple interfacing with renewable energy sources (RESs), and integration of energy storage units (ESU). The RES-generated power storage in local ESU is an alternative solution for managing the utility grid demand. In addition, to maintain the EV charging demand at the microgrid levels, energy management and control strategies must carefully power the EV battery charging unit. Also, charging stations require dedicated converter topologies, control strategies and need to follow the levels and standards. Based on the EV, ESU, and RES accessibility, the different types of microgrids architecture and control strategies are used to ensure the optimum operation at the EV charging point. Based on the above said merits, this review paper presents the different RES-connected architecture and control strategies used in EV charging stations. This study highlights the importance of different charging station architectures with the current power converter topologies proposed in the literature. In addition, the comparison of the microgrid-based charging station architecture with its energy management, control strategies, and charging converter controls are also presented. The different levels and types of the charging station used for EV charging, in addition to controls and connectors used in the charging station, are discussed. The experiment-based energy management strategy is developed for controlling the power flow among the available sources and charging terminals for the effective utilization of generated renewable power. The main motive of the EMS and its control is to maximize usage of RES consumption. This review also provides the challenges and opportunities for EV charging, considering selecting charging stations in the conclusion.publishedVersio

Battery Pack Cells Mon itoring for Intelligent Charging

This dissertation intends to create a system capable of cell charging, cell balancing or both at the same time for batteries with multiple cells connected in series. It also tries to understand why there is only few literature connected with cell balancing and cell charging at the same time. For that purpose, this dissertation presents a review on the state of the art of many concepts related both to balancing and charging in order to pick the right methods and equipment to achieve the objectives of this work. This dissertation includes literature review on batteries, cell balancing methods and topologies, cell charging methods and a small review on state of charge estimation methods. Later on, this document studies and explains hardware and software requirements and choices in order to understand the final developed circuit. Lastly, development difficulties, results and conclusions are presented.Esta dissertação pretende criar um sistema capaz de carregar, balancear ou ambos em simultâneo num pack com diversas células ligadas em série. Tenta ainda perceber a razão de haver tão pouca bibliografia que junte em simultâneo carregamento e balanceamento de baterias. Para alcançar estes objetivos, esta dissertação conta com uma revisão do estado da arte de vários temas relacionados tanto com balanceamento como com carregamento de forma a perceber os métodos e equipamentos mais adequados para implementar. A dissertação inclui revisão bibliográfica em baterias, métodos de balanceamento e suas topologias, métodos de carregamento de baterias e ainda alguma revisão sobre métodos de estimação de estado de carga. Posteriormente, este documento estuda e explica os requisitos de software e hardware e as escolhas feitas para o desenvolvimento do circuito. Finalmente apresentam-se as dificuldades de desenvolvimento encontradas, os resultados e ainda algumas conclusões

Design And Implementation Of Co-Operative Control Strategy For Hybrid AC/DC Microgrids

This thesis is mainly divided in two major sections: 1) Modelling and control of AC microgrid, DC microgrid, Hybrid AC/DC microgrid using distributed co-operative control, and 2) Development of a four bus laboratory prototype of an AC microgrid system. At first, a distributed cooperative control (DCC) for a DC microgrid considering the state-of-charge (SoC) of the batteries in a typical plug-in-electric-vehicle (PEV) is developed. In DC microgrids, this methodology is developed to assist the load sharing amongst the distributed generation units (DGs), according to their ratings with improved voltage regulation. Subsequently, a DCC based control algorithm for AC microgrid is also investigated to improve the performance of AC microgrid in terms of power sharing among the DGs, voltage regulation and frequency deviation. The results validate the advantages of the proposed methodology as compared to traditional droop control of AC microgrid. The DCC-based control methodology for AC microgrid and DC microgrid are further expanded to develop a DCC-based power management algorithm for hybrid AC/DC microgrid. The developed algorithm for hybrid microgrid controls the power flow through the interfacing converter (IC) between the AC and DC microgrids. This will facilitate the power sharing between the DGs according to their power ratings. Moreover, it enables the fixed scheduled power delivery at different operating conditions, while maintaining good voltage regulation and improved frequency profile. The second section provides a detailed explanation and step-by-step design and development of an AC/DC microgrid testbed. Controllers for the three-phase inverters are designed and tested on different generation units along with their corresponding inductor-capacitor-inductor (LCL) filters to eliminate the switching frequency harmonics. Electric power distribution line models are developed to form the microgrid network topology. Voltage and current sensors are placed in the proper positions to achieve a full visibility over the microgrid. A running average filter (RAF) based enhanced phase-locked-loop (EPLL) is designed and implemented to extract frequency and phase angle information. A PLL-based synchronizing scheme is also developed to synchronize the DGs to the microgrid. The developed laboratory prototype runs on dSpace platform for real time data acquisition, communication and controller implementation