Intelligent Lithium Ion Battery State Of Charge Soc Estimation Methods
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Intelligent Lithium Ion Battery State Of Charge Soc Estimation Methods
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Author : Shunli Wang
language : en
Publisher:
Release Date : 2023
Intelligent Lithium Ion Battery State Of Charge Soc Estimation Methods written by Shunli Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Electronic books categories.
To improve the accuracy and stability of power battery state of charge (SOC) estimation, this book proposes a SOC estimation method for power lithium batteries based on the fusion of deep learning and filtering algorithms. More specifically, the book proposes a SOC estimation method for Li-ion batteries using bi-directional long and short-term memory neural networks (BiLSTM), which overcomes the problem that long and short-term memory neural networks (LSTM) po.
Intelligent Lithium Ion Battery State Of Charge Soc Estimation Methods
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Author : Shunli Wang
language : en
Publisher:
Release Date : 2024
Intelligent Lithium Ion Battery State Of Charge Soc Estimation Methods written by Shunli Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
To improve the accuracy and stability of power battery state of charge (SOC) estimation, this book proposes a SOC estimation method for power lithium batteries based on the fusion of deep learning and filtering algorithms. More specifically, the book proposes a SOC estimation method for Li-ion batteries using bi-directional long and short-term memory neural networks (BiLSTM), which overcomes the problem that long and short-term memory neural networks (LSTM) pose, because they can only learn in one direction, resulting in poor feature extraction and memory effect. The book provides some technical references for the design, matching, and application of power lithium-ion battery management systems, and contributes to the development of new energy technology applications.
Switching Based State Of Charge Estimation Of Lithium Ion Batteries
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Author : Yingchen Su
language : en
Publisher:
Release Date : 2011
Switching Based State Of Charge Estimation Of Lithium Ion Batteries written by Yingchen Su and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Energy conservation categories.
"The objective of this thesis is to explore a switching-based approach to estimate the state of charge (SOC) of Li-ion batteries. The knowledge of SOC can be utilized to significantly enhance battery performance and longevity. The thesis first presents a brief discussion on various SOC estimation methods, such as coulomb counting, use of electrochemical model combined with Kalman Filtering and open-circuit voltage (OCV). Subsequently, emphasis is placed on the OCV-based method. The advantage of the OCV method lies in its simplicity. It obviates the need for modeling and lowers computational burden compared to model-based approaches. The method yields accurate SOC estimates if a long period of battery resting time (switch-off time) is allowed. For smaller switch-off durations, the accuracy of SOC estimation reduces. However, experiments show that Li-ion batteries could give acceptable SOC estimates due to their fast transient response during switch-off. In traditional usage scenarios, a switch-off interval may not be practical. However, in distributed power systems with multiple storage elements, a switch-off interval could be provided. Experiments are conducted to characterize the estimation error versus the switch-off time. To reduce the switch-off time to 30 second switch-off time and to increase the accuracy of SOC estimation, a method is proposed to extrapolate the OCV at infinite time from the measured OCV using a time constant. This leads to predicted OCV for infinite switch-off intervals. Experiments are conducted to confirm the improved SOC estimation using the proposed method. For experimentation, a commercially available LiFeMgPO4 battery module as well as a single cell LiFePO4 battery, is used."--Abstract.
Multidimensional Lithium Ion Battery Status Monitoring
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Author : Shunli Wang
language : en
Publisher: CRC Press
Release Date : 2022-12-28
Multidimensional Lithium Ion Battery Status Monitoring written by Shunli Wang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-28 with Technology & Engineering categories.
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.
Design And Development Of Advanced Machine Learning Algorithms For Lithium Ion Battery State Of Charge Estimation
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Author : Manjot Sidhu
language : en
Publisher:
Release Date : 2019
Design And Development Of Advanced Machine Learning Algorithms For Lithium Ion Battery State Of Charge Estimation written by Manjot Sidhu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Batteries have been becoming more and more popular because of their long life and lightweight. Accurate estimation of the SOC help in making plans in an application to conserve and further enhance battery life. State of Charge (SOC) estimation is a difficult task made more challenging by changes in battery characteristics over time and their nonlinear behavior. In recent years, intelligent schemes for the estimation of the SOC have been proposed because of the absence of the formula for calculating SOC which is hard to deduce because of the effect of external factors like temperature. As the traditional methods only considered certain aspects which with the aging and degradation of the battery results in errors. To tackle this problem several methods were proposed which made use of now evolving artificial intelligence technologies. This paper presents a new SOC estimation algorithm based on kNearest neighbor and random forest regression and a comparison study is done using four algorithms Support Vector Regression, Neural Network Regression, Random Forest Regression and kNearest Neighbor. Their performance is evaluated using data from two drive cycles.
Modeling And State Estimation Of Automotive Lithium Ion Batteries
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Author : Shunli Wang
language : en
Publisher: CRC Press
Release Date : 2024-07-16
Modeling And State Estimation Of Automotive Lithium Ion Batteries written by Shunli Wang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-16 with Science categories.
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.
2021 International Conference On Emerging Smart Computing And Informatics Esci
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Author : IEEE Staff
language : en
Publisher:
Release Date : 2021-03-05
2021 International Conference On Emerging Smart Computing And Informatics Esci written by IEEE Staff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-05 with categories.
This conference aims to present a unified platform for advanced and multi disciplinary research towards design of smart computing and informatics The theme is on a broader front focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solution to varied problems in society, environment and industries The scope is also extended towards deployment of emerging computational and knowledge transfer approaches, optimizing solutions in varied disciplines of science, technology and healthcare
Lithium Ion Battery Soc Estimation
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Author : Sepideh Afshar
language : en
Publisher:
Release Date : 2017
Lithium Ion Battery Soc Estimation written by Sepideh Afshar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Battery chargers categories.
Lithium-ion batteries are frequently used in Hybrid electric vehicles (HEVs), which are taking the place of gas-engine vehicles. An important but not measurable quantity in HEVs is the amount of charge remaining in the battery in a drive cycle. The remaining charge is normally identified by a variable called state of charge (SOC). A potential way of estimating the SOC is relating this variable with the state of a dynamical system. Afterwards, the SOC can be estimated through an observer design. As a precise model, electrochemical equations are chosen in this research to estimate the SOC. The first part of this thesis considers comparison studies of commonly-used finite-dimensional estimation methods for different distributed parameter systems (DPSs). In this part, the system is first approximated by a finite-dimensional representation; the observer dynamics is a copy of the finite-dimensional representation and a filtering gain obtained through observer design. The main outcome of these studies is comparing the performance of different observers in the state estimation of different types of DPSs after truncation. The studies are then expanded to investigate the effect of the truncated model by increasing the order of finite-dimensional approximation of the system numerically. The simulation results are also compared to the mathematical properties of the systems. A modified sliding mode observer is improved next to take care of the system's nonlinearity and compensate for the estimation error due to disturbances coming from an external input. It is proved that the modified SMO provides an exponential convergence of the estimation error in the existence of an external input. In most cases, the simulations results of the comparison studies indicate the improved performance of the modified SMO observer. Approximation and well-posedness of two general classes of nonlinear DPSs are studied next. The main concern of these studies is to produce a low-order model which converges to the original equation as the order of approximation increases. The available results in the literature are limited to specified classes of systems. These classes do not cover the lithium-ion cell model; however, the general forms presented here include the electrochemical equations as a specific version. In order to facilitate the electrochemical model for observer design, simplification of the model is considered in the next step. The original electrochemical equations are composed of both dynamical and constraint equations. They are simplified such that a fully dynamical representation can be derived. The fully dynamical representation is beneficial for real-time application since it does not require solving the constraint equation at every time iteration while solving the dynamical equations. Next, the electrochemical equations can be transformed into the general state space form studied in this thesis. Finally, an adaptive EKF observer is designed via the low-order model for SOC estimation. The electrochemical model employed here is a variable solid-state diffusivity model. Compared to other models, the variable solid-state diffusivity model is more accurate for cells with Lithium ion phosphate positive electrode, which are considered here, than others. The adaptive observer is constructed based on considering an adaptive model for the open circuit potential term in the electrochemical equations. The parameters of this model are identified simultaneously with the state estimation. Compared to the experimental data, simulation results show the efficiency of the designed observer in the existence of modeling inaccuracy.
Advanced Model Based Charging Control For Lithium Ion Batteries
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Author : Quan Ouyang
language : en
Publisher: Springer Nature
Release Date : 2023-01-01
Advanced Model Based Charging Control For Lithium Ion Batteries written by Quan Ouyang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Technology & Engineering categories.
In this book, the most state-of-the-art advanced model-based charging control technologies for lithium-ion batteries are explained from the fundamental theories to practical designs and applications, especially on the battery modelling, user-involved, and fast charging control algorithm design. Moreover, some other necessary design considerations, such as battery pack charging control with centralized and distributed structures, are also introduced to provide excellent solutions for improving the charging performance and extending the lifetime of the batteries/battery packs. Finally, some future directions are mentioned in brief. This book summarizes the model-based charging control technologies from the cell level to the battery pack level. From this book, readers interested in battery management can have a broad view of modern battery charging technologies. Readers who have no experience in battery management can learn the basic concept, analysis methods, and design principles of battery charging systems. Even for the readers who are occupied in this area, this book also provides rich knowledge on engineering applications and future trends of battery charging technologies.
Artificial Intelligence Based State Of Health Estimation Of Lithium Ion Batteries
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Author : Remus Teodorescu
language : en
Publisher:
Release Date : 2024-02-27
Artificial Intelligence Based State Of Health Estimation Of Lithium Ion Batteries written by Remus Teodorescu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-27 with Science categories.
This reprint aims to showcase manuscripts presenting efficient SOH estimation methods using AI which exhibit good performance such as high accuracy, high robustness against the changes in working conditions, and good generalization, etc. Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime due to performance degradation during usage. It is, therefore, essential to determine the battery's state of health (SOH) so that the battery management system can control the battery, enabling it to run in the best state and thus prolonging its lifetime. Artificial intelligence (AI) technologies possess immense potential in inferring battery SOH and can extract aging information (i.e., SOH features) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process.