Lithium-ion battery data and where to find it

Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core of transformational developments in battery design, modelling and management is data. In this work, the datasets associated with lithium batteries in the public domain are summarised. We review the data by mode of experimental testing, giving particular attention to test variables and data provided. Alongside highlighted tools and platforms, over 30 datasets are reviewed

Adaptive Techniques for Estimation and Online Monitoring of Battery Energy Storage Devices

The battery management system (BMS) plays a defining role in the safety and proper operation of any battery energy storage system (BESS). Without significant advances in the state-of-the-art of BMS algorithms, the future uptake of high power/energy density battery chemistries by consumers in safety-critical applications, is not feasible. Therefore, this thesis aims to provide a coherent body of work on the enhancement of the most important tasks performed by a modern BMS, that is, the estimation and monitoring of various battery states, e.g. state-of-charge (SOC), state-of-health (SOH) and state-of-power (SOP). The Kalman Filter is an elegant set of robust equations that is often utilised by designers in modern BMS, to estimate the battery states and parameters in real time. A nonlinear version of the KF technique, namely the Extended Kalman Filter (EKF) is applied throughout this thesis to estimate the battery’s states including SOC, as well as the battery’s impedance parameters. To this end, a suitable model structure for online battery modelling and identification is selected through a comparative study of the most popular electrical equivalent-circuit battery models for real-time applications. Then, a novel improvement to the EKF-based battery parameters identification technique is made through a deterministic initialisation of the battery model parameters through a broadband system identification technique, namely the pseudorandom binary sequences (PRBS). In addition, a novel decentralised framework for the enhancement of the EKF-based SOC estimation for those lithium-ion batteries with an inherently flat open-circuit voltage (OCV) response is formulated. By combining these techniques, it is possible to develop a more reliable battery states monitoring system, which can achieve estimation errors of less than 1%. Finally, the proposed BMS algorithms in this thesis are embedded on a low-cost microprocessor hardware platform to demonstrated the usefulness of the developed EKF-based battery states estimator in a practical setting. This a significant achievement when compared to those costly BMS development platforms, such as those based on FPGAs (field-programmable gate arrays)

SoC estimation for lithium-ion batteries : review and future challenges

ABSTRACT: Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence

Performance Analysis of Energy Storage in Smart Microgrid Based on Historical Data of Individual Battery Temperature and Voltage Changes

In this work, a historical data based battery management system (BMS) was successfully developed and implemented using an embedded system for condition monitoring of a battery energy storage system in a smart microgrid. The performance was assessed for 28 days of operating time with a one-minute sampling time. The historical data showed that the maximum temperature increment and the maximum temperature difference between the batteries were 4.5 °C and 2.8 °C. One of the batteries had a high voltage rate of change, i.e. above 3.0 V/min, and its temperature rate of change was very sensitive, even at low voltage rate of changes. This phenomenon tends to indicate problems that may deplete the battery energy storage system's total capacity. The primary findings of this study are that the voltage and temperature rates of change of individual batteries in real operating conditions can be used to diagnose and foresee imminent failure, and in the event of a failure occurring the root cause of the problem can be found by using the historical data based BMS. To ensure further safety and reliability of acceptable practical operating conditions, rate of change limits are proposed based on battery characteristics for temperatures below 0.5 °C/min and voltages below 3.0 V/min

Advances in Electric Drive Vehicle Modeling with Subsequent Experimentation and Analysis

A combination of stricter emissions regulatory standards and rising oil prices is leading many automotive manufacturers to explore alternative energy vehicles. In an effort to achieve zero tail pipe emissions, many of these manufacturers are investigating electric drive vehicle technology. In an effort to provide University of Kansas students and researchers with a stand-alone tool for predicting electric vehicle performance, this work covers the development and validation of various models and techniques. Chapter 2 investigates the practicality of vehicle coast down testing as a suitable replacement to moving floor wind tunnel experimentation. The recent implementation of full-scale moving floor wind tunnels is forcing a re-estimation of previous coefficient of drag determinations. Moreover, these wind tunnels are relatively expensive to build and operate and may not capture concepts such as linear and quadratic velocity dependency along with the influence of tire pressure on rolling resistance. The testing method explained here improves the accuracy of the fundamental vehicle modeling equations while remaining relatively affordable. The third chapter outlines various models used to predict battery capacity. The authors investigate the effectiveness of Peukert's Law and its application in lithium-based batteries. The work then presents the various effects of battery temperature on capacity and outlines previous work in the field. This paper then expands upon Peukert's equation in order to include both variable current and temperature effects. The method proposed captures these requirements based on a relative maximum capacity criterion. Experimental methods presented in the paper provide an economical testing procedure capable of producing the required coefficients in order to build a high-level, yet accurate state of charge prediction model. Moreover, this work utilizes automotive grade lithium-based batteries for realistic outcomes in the electrified vehicle realm. The fourth chapter describes an advanced numerical model for computing vehicle energy usage performance. This work demonstrates the physical and mathematical theories involved, while building on the principles of Newton's second law of motion. Moreover, this chapter covers the equipment, software, and processes necessary for collecting the required data. Furthermore, a real world, on-road driving cycle provides for a quantification of accuracy. Multiple University of Kansas student project vehicles are then studied using parametric studies applicable to the operating requirements of the vehicles. Further investigation demonstrates the accuracy and trends associated with the advanced models presented in Chapters 2 and 3. Lastly, chapter 5 investigates the design and building of a graphical user interface (GUI) for controlling the models created in the previous three chapters. The chapter continues to outline the creation of a stand-alone GUI as well as instructions for implementation, usage, and data analysis

Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers

Acoustic and X-ray Chacterisation of Lithium-Ion Battery Failure

Lithium-ion batteries have become synonymous with modern consumer electronics and potentially, are the cornerstone to development of integrated electrified infrastructure that can support a clean and renewable national energy grid. Despite the widespread applications due to the favourable performance parameters, recent events have elevated the safety concerns associated with lithium-ion batteries. However, there is great difficulty in rapid diagnostic analysis outside specialised laboratories which can hinder the review of functional safety- and novel energy dense- materials for lithium-ion energy storage. The dynamic evolution of internal architectures and novel active materials across multiple length scales are investigated in this thesis; with in-situ and operando acoustic spectroscopy (AS) via ultrasonic time of flight (ToF) probing, high speed synchrotron X-ray imaging, computed tomography and fractional thermal runaway calorimetry. The identification of characteristic precursor events such as gas-induced delamination in degradation mechanisms before eventual failure by AS; is correlated with X-ray imaging and post-mortem computed tomography (CT), highlighting the potential for battery management systems. Mitigation and prevention of failure with plasticized current collectors and thermally stable cellulose separators was also investigated at multiple length scales, with the transient mechanical structure compared with their commercial counterparts in cylindrical cells. Further work investigating the robustness of acoustic spectroscopy and polymer current collectors were applied to pure silicon nanowire negative electrodes. The studies reported in this thesis assess novel materials in lithium-ion batteries, and the potential impact of the work is highlighted. Development of AS via ToF probing offers another unique and field deployable insight allowing more complete and comprehensive understanding of batteries as they continue to evolve in complexity. Lithium-ion failure characterisation techniques and literature have evolved and provided insights into the function of polymer current collectors in different cell formats and chemistries. Findings presented in this thesis are anticipated to augment future inherently safer battery design and characterisation of lithium-ion energy storage thermal runaway