Daniel T. Gladwin

Bio: Daniel T. Gladwin is an academic researcher from University of Sheffield. The author has contributed to research in topics: Battery (electricity) & Energy storage. The author has an hindex of 13, co-authored 92 publications receiving 797 citations.

A Battery Energy Management Strategy for U.K. Enhanced Frequency Response and Triad Avoidance

Abstract: This paper describes a control algorithm for a battery energy storage system (BESS) to deliver a charge/discharge power output in response to changes in the grid frequency constrained by the National Grid Electricity Transmission (NGET)—the primary electricity transmission network operator in the U.K.—while managing the state of charge of the BESS to optimize the availability of the system. Furthermore, this paper investigates using the BESS in order to maximize triad avoidance benefit revenues while layering other services. Simulation using a 2 MW/1 MWh lithium–titanate BESS validated model is carried out to explore possible scenarios using the proposed algorithms. Finally, experimental results of the 2 MW/1 MWh Willenhall Energy Storage System verify the performance of the proposed algorithms.

Survey on magnetic resonant coupling wireless power transfer technology for electric vehicle charging

Abstract: Wireless power transfer (WPT) technology makes it possible to supply power through an air-gap, without the need for current-carrying wires. One important technique of WPT technology is magnetic resonant coupling (MRC) WPT. Based on the advantages of MRC WPT, such as safety and high power transfer efficiency over a long transmit distance, there are many possible applications of MRC WPT. This study provides a comprehensive, state-of-the-art review of the MRC WPT technology and wireless charging for electric vehicle (EV). A comparative overview of MRC WPT system design which includes a detailed description of the prototypes, schematics, compensation circuit topologies (impedance matching), and international charging standards. In addition, this study provides an overview of wireless EV charging including the static wireless EV charging and the dynamic wireless EV charging, which focuses on the coil design, power transfer efficiency, and current research achievement in the literature.

Battery energy storage systems for the electricity grid: UK research facilities

Abstract: Grid-connected battery energy storage systems with fast acting control are a key technology for improving power network stability and increasing the penetration of renewable generation. This paper describes two battery energy storage research facilities connected to the UK electricity grid. Their performance is detailed, along with hardware results, and a number of grid support services are demonstrated, again with results presented. The facility operated by The University of Manchester is rated at 236kVA, 180kWh, and connected to the 400V campus power network, The University of Sheffield operates a 2MVA, 1MWh facility connected to an 11kV distribution network.

Analysis and Design of LLC Resonant Converters With Capacitor–Diode Clamp Current Limiting

Abstract: This paper presents a design methodology for LLC resonant converters with capacitor-diode clamp for current limiting in overload conditions. A new fundamental harmonic approximation-based equivalent circuit model is obtained through the application of describing function techniques, by examining the fundamental behavior of the capacitor-diode clamp. An iterative procedure to determine the conduction point of the diode clamp is also given. The behavior of this type of converter is analyzed and guidelines for designing the current limiting characteristics are discussed. The characterization of a 90 W converter design using the proposed methodology is presented. The converter voltage gain and the voltage-current characteristics under different overload conditions and operating frequencies are predicted using the proposed model, which accuracies are validated against the prototype with good correlation.

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Abstract: Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

Abstract: With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

Abstract: Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.

A brief review on key technologies in the battery management system of electric vehicles

Abstract: Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries

Abstract: Carbon impression and the growing reliance on fossil fuels are two unique concerns for world emission regulatory agencies. These issues have placed electric vehicles (EVs) powered by lithium-ion batteries (LIBs) on the forefront as alternative vehicles. The LIB has noticeable features, including high energy and power density, compared with other accessible electrochemical energy storage systems. However, LIB is exceedingly nonlinear and dynamic; therefore, it generally requires an accurate online state-of-charge (SOC) estimation algorithm for real-time applications. Accurate battery modelling is an essential and primary requirement of online SOC estimation to simulate the dynamics. In this paper, different modelling methods suitable for online SOC estimation are discussed, and four groups of available online SOC estimation approaches are reviewed. After the general survey, the study explores the available Kalman filter (KF) family algorithms suitable for model-based online SOC estimation. The mathematical process and limitations of different KF family algorithms are analysed in depth and discussed. Moreover, challenging steps in the implementation of KF family algorithms in model-based online SOC estimation processes, such as selection of battery model, initial SOC and filter tuning, are elaborated for the efficient development of a battery management system, especially for EV application. The on-going research is propelled by KF-based online SOC estimation approaches distinctly emphasised through reviewing various studies for future research progression.