Identification And State Estimation For Linear Parameter Varying Systems With Application To Battery Management System Design
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Identification and State Estimation for Linear Parameter Varying Systems with Application to Battery Management System Design
The tools developed for LPV systems are then applied to solve the problems of model identification and state of charge (SoC) estimation for battery cells. The model identification problem is tackled using both identification schemes so that differences in performance and effectiveness between the methods can be compared and contrasted. A SoC estimator based on LPV system state estimation techniques is then designed using the model identified. Because parametric uncertainty is inherent in the estimator designed, the stability and performance of the estimator is analyzed using the notion of input to state stability. Experimental data is then used to illustrate the efficacy of this method. The goal of these applications is to show the relevance of the LPV structure and techniques to problems in battery management system design, so that research will be done to solve other problems in this area under the same framework.
Advances in Battery Manufacturing, Service, and Management Systems
Addresses the methodology and theoretical foundation of battery manufacturing, service and management systems (BM2S2), and discusses the issues and challenges in these areas This book brings together experts in the field to highlight the cutting edge research advances in BM2S2 and to promote an innovative integrated research framework responding to the challenges. There are three major parts included in this book: manufacturing, service, and management. The first part focuses on battery manufacturing systems, including modeling, analysis, design and control, as well as economic and risk analyses. The second part focuses on information technology’s impact on service systems, such as data-driven reliability modeling, failure prognosis, and service decision making methodologies for battery services. The third part addresses battery management systems (BMS) for control and optimization of battery cells, operations, and hybrid storage systems to ensure overall performance and safety, as well as EV management. The contributors consist of experts from universities, industry research centers, and government agency. In addition, this book: Provides comprehensive overviews of lithium-ion battery and battery electrical vehicle manufacturing, as well as economic returns and government support Introduces integrated models for quality propagation and productivity improvement, as well as indicators for bottleneck identification and mitigation in battery manufacturing Covers models and diagnosis algorithms for battery SOC and SOH estimation, data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH Presents mathematical models and novel structure of battery equalizers in battery management systems (BMS) Reviews the state of the art of battery, supercapacitor, and battery-supercapacitor hybrid energy storage systems (HESSs) for advanced electric vehicle applications Advances in Battery Manufacturing, Services, and Management Systems is written for researchers and engineers working on battery manufacturing, service, operations, logistics, and management. It can also serve as a reference for senior undergraduate and graduate students interested in BM2S2.
Smart Battery Management for Enhanced Safety
This book consolidates studies in the rapidly and foreseeably growing field of battery management. The primary focus is to overview the management of batteries (Li-ion batteries and some cases of flow batteries) with the fusion of mechanism and AI-based approaches. The book can be categorized into three groups, i.e., (i) mechanism and AI-based battery modeling and parameterization, (ii) AI-based diagnostic, early warning, and active safety control, and (iii) emerging techniques of smart battery and smart management, combining the emerging areas of embedded sensing and reconfigurable batteries. It is well recognized that the battery safety and management are the kernel of energy storage, renewable utilization, and low-carbon society, which have been highly popular in recent years. The exploration of AI techniques for advanced battery management has been seldom discussed systematically before. Moreover, the combination of AI and mechanism approaches can remarkably enhance the battery management, which however has never been focused on in previous books. Therefore, this book can add new knowledge to the paradigm and attract the attention of academics, scientists, engineers, and practitioners. It is a reference book for researchers and engineers in related fields. The step-by-step guidance, comprehensive introduction, and case studies make it accessible to audiences of different levels, from graduates to experienced engineers.