Download or read book Design Optimization Approach to Estimate the Second Life Lithium ion Batteries Life Cycle Prediction written by Kwangwoo Yeum and published by . This book was released on 2022 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lithium ion (Li-ion) batteries degrade with cyclic usage and storage duration. Batteries close to their end-of-life can no longer fulfill their performance requirements, and have an increased likelihood of catastrophic failures. Different usage conditions, complex manufacturing, and lack of essential data contribute to the complex degradation of second life batteries and hinder accurate analysis of battery capacity degradation. Therefore, a quick and precise diagnosis of used batteries has become an important research area for battery management, specifically in large-scale power storage systems. This thesis illustrates potential approaches for diagnosing battery degradation, considering both a physical-based and a data-driven model. The main objective is to boost the degradation prediction with the data-driven model by leveraging artificially generated data from the physical model. The approach is divided into two steps with two different battery cell models. A gradient-free optimization approach is introduced with the most widely used battery model (21700), which comes with published data of its battery cell structure, to optimize inside-of-the-cell structure. We estimate the physical battery cell structure to acquire artificial data and compare the performance of the estimated battery against the original battery. Then the degradation prediction is investigated with the A123 battery, which has extensive and high quality of battery degradation data but lacks exact cell physical structural information. We acquire artificial degradation data by estimating the physical battery cell structure with the optimization approach, and then utilize these data to boost the original battery life cycle prediction. Linear regression and backpropagation methods (resilient backpropagation, conjugate backpropagation, and bayesian regularization backpropagation) are applied along with various test matrices and cross-validation to compare the life cycle predictions. The life cycle prediction was first conducted with only the actual data. Then the artificial data was added to the training sets to conduct the life cycle prediction. The life cycle prediction error with actual data was improved with resilient backpropagation and a sigmoid activation function by 4 - 6% compared to linear regression. Backpropagation performs better on test sets containing a large amount of extreme values than the linear regression. Lastly, the prediction results with artificial data and actual data were compared by using the regression and resilient backpropagation. Adding artificial data reduced the life cycle prediction error by 0.3 - 1.3% in the regression method; there was a greater improvement when the life cycle prediction error with actual data was over 15%. However, with the backpropagation method, adding artificial data resulted in larger prediction errors.

Download or read book A Hybrid Prognostic Approach for Battery Health Monitoring and Remaining useful life Prediction written by Mohamed Ahwiadi and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Lithium-ion (Li-ion) batteries are commonly used in various industrial and domestic applications, such as portable communication devices, medical equipment, and electric vehicles. However, the Li-ion battery performance degrades over time due to the aging phenomenon, which may lead to system performance degradation or even safety issues, especially in vehicle and industrial applications. Reliable battery health monitoring and prognostics systems are extremely useful for improving battery performance, to diagnose the battery's state-of-health (SOH), and to predict its remaining-useful-life (RUL). In general, it is challenging to accurately track the battery's nonlinear degradation features as battery degradation parameters are almost inaccessible to measure using general sensors. In addition, a battery is an electro-chemical system whose properties vary with variations in environmental and operating conditions. Although there are some techniques proposed in the literature for battery SOH estimation and RUL analysis, these techniques have clear limitations in applications, due to reasons such as lack of proper representation of the posterior probability density functions to capture and model the nonlinear dynamic system of Li-ion batteries. In addition, these techniques cannot effectively deal with the time-varying system properties, especially for long-term predictions. To tackle these problems, a novel hybrid prognostic framework has been developed in this PhD work for battery SOH monitoring and RUL prediction. It integrates two new models: the model-based filtering method and the evolving fuzzy rule-based prediction technique. The strategy is to propose and use more efficient techniques in each module to improve processing, accuracy and reliability. Firstly, a newly enhanced mutated particle filter technique is proposed to enhance the performance of particle filter technique and improve the modeling accuracy of the battery system's degradation process. It consists of three novel aspects: an enhanced mutation approach, a selection scheme, and an outlier detection method. Secondly, an adaptive evolving fuzzy technique is suggested for long-term time series forecasting. It has a novel error-assessment method to control the fuzzy cluster/rule generation process-also, a new optimization technique to enhance incremental learning and improve modeling efficiency. Finally, a new hybrid prognostic framework integrates the merits of both proposed techniques to capture the underlying physics of the battery systems for its SOH estimation, and improve the prognosis of dynamic system for long-term prediction of Li-ion battery RUL. The effectiveness of the proposed techniques is verified through simulation tests using some commonly used-benchmark models and battery databases in this field, such as the one from the National Aeronautics and Space Administration (NASA) Ames Prognostic Center of Excellence. Test results have shown that the proposed hybrid prognostics framework can effectively capture the battery SOH degradation process, and can accurately predict its RUL.

Download or read book Multidimensional Lithium Ion Battery Status Monitoring written by Shunli Wang and published by CRC Press. This book was released on 2022-12-28 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multidimensional Lithium-Ion Battery Status Monitoring focuses on equivalent circuit modeling, parameter identification, and state estimation in lithium-ion battery power applications. It explores the requirements of high-power lithium-ion batteries for new energy vehicles and systematically describes the key technologies in core state estimation based on battery equivalent modeling and parameter identification methods of lithium-ion batteries, providing a technical reference for the design and application of power lithium-ion battery management systems. Reviews Li-ion battery characteristics and applications. Covers battery equivalent modeling, including electrical circuit modeling and parameter identification theory Discusses battery state estimation methods, including state of charge estimation, state of energy prediction, state of power evaluation, state of health estimation, and cycle life estimation Introduces equivalent modeling and state estimation algorithms that can be applied to new energy measurement and control in large-scale energy storage Includes a large number of examples and case studies This book has been developed as a reference for researchers and advanced students in energy and electrical engineering.

Download or read book Capacity Fade Analysis and Model Based Optimization of Lithium ion Batteries written by Venkatasailanathan Ramadesigan and published by . This book was released on 2013 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electrochemical power sources have had significant improvements in design, economy, and operating range and are expected to play a vital role in the future in a wide range of applications. The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This dissertation contributes to the efforts in the modeling, simulation and optimization of lithium-ion batteries and their use in the design of better batteries for the future. While physics-based models have been widely developed and studied for these systems, the rigorous models have not been employed for parameter estimation or dynamic optimization of operating conditions. The first chapter discusses a systems engineering based approach to illustrate different critical issues possible ways to overcome them using modeling, simulation and optimization of lithium-ion batteries. The chapters 2-5, explain some of these ways to facilitate (i) capacity fade analysis of Li-ion batteries using different approaches for modeling capacity fade in lithium-ion batteries, (ii) model based optimal design in Li-ion batteries and (iii) optimum operating conditions (current profile) for lithium-ion batteries based on dynamic optimization techniques. The major outcomes of this thesis will be, (i) comparison of different types of modeling efforts that will help predict and understand capacity fade in lithium-ion batteries that will help design better batteries for the future, (ii) a methodology for the optimal design of next-generation porous electrodes for lithium-ion batteries, with spatially graded porosity distributions with improved energy efficiency and battery lifetime and (iii) optimized operating conditions of batteries for high energy and utilization efficiency, safer operation without thermal runaway and longer life.

Download or read book Long Term Health State Estimation of Energy Storage Lithium Ion Battery Packs written by Qi Huang and published by Springer Nature. This book was released on 2023-08-18 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates in detail long-term health state estimation technology of energy storage systems, assessing its potential use to replace common filtering methods that constructs by equivalent circuit model with a data-driven method combined with electrochemical modeling, which can reflect the battery internal characteristics, the battery degradation modes, and the battery pack health state. Studies on long-term health state estimation have attracted engineers and scientists from various disciplines, such as electrical engineering, materials, automation, energy, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of extraction for health indicators and the significant influence of electrochemical modeling and data-driven issues in the design and optimization of health state estimation in energy storage systems. The book is intended for undergraduate and graduate students who are interested in new energy measurement and control technology, researchers investigating energy storage systems, and structure/circuit design engineers working on energy storage cell and pack.

Download or read book Modeling and State Estimation of Automotive Lithium Ion Batteries written by Shunli Wang and published by CRC Press. This book was released on 2024-07-16 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to evaluate and improve the state of charge (SOC) and state of health (SOH) of automotive lithium-ion batteries. The authors first introduce the basic working principle and dynamic test characteristics of lithium-ion batteries. They present the dynamic transfer model, compare it with the traditional second-order reserve capacity (RC) model, and demonstrate the advantages of the proposed new model. In addition, they propose the chaotic firefly optimization algorithm and demonstrate its effectiveness in improving the accuracy of SOC and SOH estimation through theoretical and experimental analysis. The book will benefit researchers and engineers in the new energy industry and provide students of science and engineering with some innovative aspects of battery modeling.

Download or read book Progress in Modeling and Simulation of Batteries written by John Turner and published by SAE International. This book was released on 2016-06-15 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling and simulation of batteries, in conjunction with theory and experiment, are important research tools that offer opportunities for advancement of technologies that are critical to electric motors. The development of data from the application of these tools can provide the basis for managerial and technical decision-making. Together, these will continue to transform batteries for electric vehicles. This collection of nine papers presents the modeling and simulation of batteries and the continuing contribution being made to this impressive progress, including topics that cover: • Thermal behavior and characteristics • Battery management system design and analysis • Moderately high-fidelity 3D capabilities • Optimization Techniques and Durability As electric vehicles continue to gain interest from manufacturers and consumers alike, improvements in economy and affordability, as well as adoption of alternative fuel sources to meet government mandates are driving battery research and development. Progress in modeling and simulation will continue to contribute to battery improvements that deliver increased power, energy storage, and durability to further enhance the appeal of electric vehicles.

Download or read book Modeling Stationary Lithium Ion Batteries for Optimization and Predictive Control Preprint written by and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.

Download or read book Advances in Lithium Ion Batteries for Electric Vehicles written by Haifeng Dai and published by Elsevier. This book was released on 2024-02-15 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Lithium-Ion Batteries for Electric Vehicles: Degradation Mechanism, Health Estimation, and Lifetime Prediction examines the electrochemical nature of lithium-ion batteries, including battery degradation mechanisms and how to manage the battery state of health (SOH) to meet the demand for sustainable development of electric vehicles. With extensive case studies, methods and applications, the book provides practical, step-by-step guidance on battery tests, degradation mechanisms, and modeling and management strategies. The book begins with an overview of Li-ion battery aging and battery aging tests before discussing battery degradation mechanisms and methods for analysis. Further methods are then presented for battery state of health estimation and battery lifetime prediction, providing a range of case studies and techniques. The book concludes with a thorough examination of lifetime management strategies for electric vehicles, making it an essential resource for students, researchers, and engineers needing a range of approaches to tackle battery degradation in electric vehicles. - Evaluates the cause of battery degradation from the material level to the cell level - Explains key battery basic lifetime test methods and strategies - Presents advanced technologies of battery state of health estimation

Download or read book Design and Analysis of Large Lithium Ion Battery Systems written by Shriram Santhanagopalan and published by Artech House. This book was released on 2014-12-01 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new resource provides you with an introduction to battery design and test considerations for large-scale automotive, aerospace, and grid applications. It details the logistics of designing a professional, large, Lithium-ion battery pack, primarily for the automotive industry, but also for non-automotive applications. Topics such as thermal management for such high-energy and high-power units are covered extensively, including detailed design examples. Every aspect of battery design and analysis is presented from a hands-on perspective. The authors work extensively with engineers in the field and this book is a direct response to frequently-received queries. With the authors’ unique expertise in areas such as battery thermal evaluation and design, physics-based modeling, and life and reliability assessment and prediction, this book is sure to provide you with essential, practical information on understanding, designing, and building large format Lithium-ion battery management systems.

Download or read book Battery Management Algorithm for Electric Vehicles written by Rui Xiong and published by Springer Nature. This book was released on 2019-09-23 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically introduces readers to the core algorithms of battery management system (BMS) for electric vehicles. These algorithms cover most of the technical bottlenecks encountered in BMS applications, including battery system modeling, state of charge (SOC) and state of health (SOH) estimation, state of power (SOP) estimation, remaining useful life (RUL) prediction, heating at low temperature, and optimization of charging. The book not only presents these algorithms, but also discusses their background, as well as related experimental and hardware developments. The concise figures and program codes provided make the calculation process easy to follow and apply, while the results obtained are presented in a comparative way, allowing readers to intuitively grasp the characteristics of different algorithms. Given its scope, the book is intended for researchers, senior undergraduate and graduate students, as well as engineers in the fields of electric vehicles and energy storage.

Download or read book Handbook on Battery Energy Storage System written by Asian Development Bank and published by Asian Development Bank. This book was released on 2018-12-01 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook serves as a guide to deploying battery energy storage technologies, specifically for distributed energy resources and flexibility resources. Battery energy storage technology is the most promising, rapidly developed technology as it provides higher efficiency and ease of control. With energy transition through decarbonization and decentralization, energy storage plays a significant role to enhance grid efficiency by alleviating volatility from demand and supply. Energy storage also contributes to the grid integration of renewable energy and promotion of microgrid.

Download or read book Modeling and Control of State of Charge of Modular and Second Life Battery Systems written by Yunfeng Jiang and published by . This book was released on 2019 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to meet the increasing requirements for better utilization of renewable energy technologies, lithium-ion battery energy storage system have currently been developed to power an ever-increasing electrical applications in renewable energy industry and automotive industry. Energy storage capabilities are determined by the battery size, typically expressed in the units from watt hour to megawatt hour. Battery capacity is often fixed and amount of charge is specified in state of charge. If battery is used to plan for long term energy provision, battery size has to be chosen large, making battery energy storage system cost prohibitive. It is necessary to design battery smaller and in modular, but allow them to be exchanged and make sure different modular keep same for state of charge via balancing. In this dissertation, some state-of-the-art modeling and control approaches for battery energy storage system are thoroughly proposed and validated in detail. Firstly, a fractional differential model method is presented for modeling the dynamics of a lithium-ion battery system over a large operating range, which is combination of conventional equivalent circuit model and electrochemical impedance spectroscopy experimental data. The proposed model includes a constant phase element term to approximate the non-linear dynamical behavior of the lithium-ion battery through broad operating range. The continuous-time system identification methods are introduced to estimate model parameters of the proposed fractional differential model. Validated on experimental data obtained from a lithium-ion battery, the estimated model provides better model accuracy and model performance than traditional integer equivalent circuit model methods. Secondly, to better utilize a battery energy storage system, a fractional differential battery modeling approach is proposed to characterize power delivery dynamics, given charge and discharge demand as an input, not only in normal operating range, but also in extreme cases, such as battery over-charging and over-discharging. In particular, the proposed model is combined by individual voltage and current models to predict the dynamics of the energy storage and delivery of a lithium-ion battery system. The continuous-time parameterization and estimation methods are fully described and validated on the experimental data from a lithium iron phosphate battery. Finally, some current scheduling strategies are proposed to solve battery heterogeneity and further improve the performance, lifespan and safety of a battery energy storage system with parallel connected battery modules. The scheduling algorithms are formulated in both open-loop and closed-loop implementation. The open-loop algorithm is formulated by solving a typical linear programming problem with detailed knowledge of the battery system. The closed-loop method is computed autonomously by recursive control algorithm without detailed battery knowledge, even when the characteristic parameters change as the battery pack ages. The experimental results indicate the feasibility and flexibility of the proposed current scheduling method in a battery pack system with parallel placed buck regulated battery modules.

Download or read book Rapid Remaining useful life Prediction of Li ion Batteries Using Image based Machine Learning written by Jonathan Couture and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increased integration of lithium-ion batteries in our everyday lives, accurate and reliable battery management systems have become an imperative aspect of the well-being of our everyday electronics. This thesis proposes the use of novel machine learning methodologies to predict the remaining-useful-life (RUL) of lithium-ion batteries reliably, accurately, and swiftly. Firstly, a method that prides itself on being publicly available, and which can be easily implemented alongside existing methodology, is proposed to increase the prediction accuracy of the conventional health indicator methodology by 6.72% by using images of data curves as inputs. Subsequently, a more in-depth machine learning model is presented which managed to considerably outperform the current literature in terms of speed, accuracy, and reliability, achieving an RUL prediction accuracy of 90.85%. These proposed methodologies have a wide range of applications, from fault diagnostics, state-of-charge, and state-of-health prediction, to other, more complex, regression applications.

Download or read book Holtz Gst tten Ordnung written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Holistic Battery Management System Design for Lithium ion Battery Systems Via Physics based Modeling Estimation and Control written by Anirudh Allam and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Lithium-ion battery systems used in electric vehicles and stationary grid storage applications are composed of numerous batteries that are interconnected to create a battery pack that can satisfy the high energy and power requirements of the desired application. However, the current research in the battery modeling and control community has focused mainly on lithium-ion batteries at the single cell-level in an isolated environment where the cell-to-cell interconnections and pack heterogeneities are not accounted for. Merely applying the existing knowledge of a single cell to such a large-scale battery pack assumes "modularity", wherein modularity is defined as the ability to extrapolate the behavior of a battery pack from a single cell. Recent experimental studies presented in the literature show evidence that the assumption of modularity, in terms of electrical, thermal, and aging behavior, does not hold true. The literature further highlights that a pack reaches its end-of-life sooner than a single cell, the thermal and aging gradient behavior of the pack is non-uniform and aggravated in comparison to a single cell, and the performance of a pack is adversely affected due to cell-to-cell heterogeneities induced by manufacturing variances. As a result, the design of Battery Management Systems for a pack must take these non-uniformities or peculiarities into account while developing algorithms for modeling, estimation, and control. To that end, this dissertation adopts a bottom-up approach by developing modeling and estimation tools at the cell-level, and then extending it to the module/pack-level for efficient control. An experimentally validated electrochemical model at the single cell-level forms the basis to develop a model-based observer to estimate "non-measurable" internal battery health variables. The cell-level electrochemical model is extended to a high-fidelity module-level model by incorporating the thermal, electrical, and aging interactions between cells to analytically and quantitatively understand the effect of heterogeneities and gradients on the behavior of battery modules. Subsequently, the model is utilized to develop an optimization-based control strategy to minimize the non-uniformities, thereby improving the safety and lifespan of battery modules. The outcome of this research will open up opportunities to advance knowledge of cell- and module-level dynamics, accurate real-time prognostic algorithms, and health-conscious module-level control. This research is primarily targeted towards the transportation sector (electric vehicles), but it can be extended to stationary grid storage applications, and more importantly used to determine the feasibility of using end-of-life lithium-ion cells in "second-use" applications.

Download or read book Towards a Systems level Understanding of Battery Systems written by Akshay Subramaniam and published by . This book was released on 2021 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current imperatives of electrification and decarbonization entail significant improvements in energy density, performance, and cost metrics for battery technology. This has motivated active research into new materials, cell designs, and external controls to ensure safe and efficient operation. Modeling and simulation approaches have a powerful complementary function in this regard, most notably exemplified by the models for Lithium-ion batteries by Newman and co-workers. The overarching theme of this dissertation is thus the development and application of electrochemical modeling approaches at multiple scales in problems relevant to the abovementioned contexts. At the systems level, the development of more intelligent and powerful Battery Management Systems is enabled by fast electrochemical models, which must balance competing considerations of accuracy, computational efficiency, and ease of parameterization. To this end, we report a rigorous and generalized methodology for "upscaling" continuum electrochemical models. This approach, based on the visualization of a battery as Tanks-in-Series, has been demonstrated for both Lithium-ion and more complex Lithium-sulfur batteries. With respect to full models, voltage prediction errors below 20 mV are achieved for high-energy cells in most practical cases. 30 mV errors are achieved for aggressive conditions of high-rate operation at sub-zero ambient temperatures, illustrating their practical utility. This approach results in improved computational speed since each conservation law is replaced by a relatively simple volume-averaged differential or algebraic equation. For examples of large-scale problems, this leads to 10x savings in computation time over fast implementations of conventional models, illustrating competitiveness for real-time applications. In the development of next-generation chemistries, continuum models can serve as a framework for the analysis and interpretation of experimental data, while providing design guidance and helping determine desirable operating regimes. Electrochemical phenomena at different length and time scales are manifested during operation through voltage and temperature signatures, cycle life, and coulombic efficiency. Optimization of cell-level metrics is thus predicated on their correlation with the internal electrochemistry. This entails the integration of electrochemical models at different levels of detail in a computationally efficient and robust manner. To this end, the second half of this dissertation describes our efforts to develop a simulation framework for the modeling of Lithium-metal systems. We first describe a robust computational method to simulate Poisson Nernst Planck (PNP) models for Lithium symmetric cells characterized by thin double layers. This can be leveraged in applications where computational efficiency is of salience, such as cycling simulations and parameterization by coupling kinetic models of interest. This is demonstrated by a systems level method, enabling the quick evaluation of candidate mechanisms appropriately expressed as time-varying rate constants, making it useful for understanding the phenomena underpinning voltage transitions in Lithium symmetric cells. This is followed by a description of a preliminary electrochemical-mechanical model for Li metal interfaces, which is expected to serve as basis for more sophisticated electrochemical-mechanical models for Li metal systems operating under external pressure. We expect these approaches to advance fundamental understanding and design of Li-metal batteries, while creating accessible computational tools to complement experimental studies. Taken together, these contributions are envisaged to advance the knowledge base for model-based design as well as Battery Management Systems, particularly in anticipation of the commercialization of emerging battery chemistries.