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Prognostic of lithium-ion batteries using a combination of physical modeling and hybrid multi-layer perceptron particle filter
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The remaining useful life (RUL) prediction of lithium-ion batteries (LIBs) plays a crucial role in battery management , safety assurance, and the anticipation of maintenance needs for reliable electric vehicle (EV) operation. An efficient prediction of RUL can ensure its safe operation and prevent both internal and external failures, as well as avoid any unwanted catastrophic events. However, achieving precise RUL prediction for electric vehicles presents a challenging task due to several issues related to intricate operational characteristics and dynamic shifts in model parameters throughout the aging process, battery parameters data extraction, data preprocessing, and hyperparameters tuning of the prediction model. This phenomenon significantly impacts the advancement of electric vehicle technology. To address these challenges, this study offers a comprehensive overview of various RUL prediction methods, presenting a comparative analysis of their outcomes, advantages, drawbacks, and associated research constraints. Emphasis is placed on the necessity of a battery management system (BMS) to ensure the safe and reliable functioning of LIBs. The review delves into crucial implementation factors, including battery test bench considerations, data selection, feature extraction, data preprocessing, performance evaluation indicators, and hyperparameter tuning. Additionally, the issues and challenges related to RUL prediction approaches such as; thermal runaway, material selection, cell balancing, battery aging, relaxation impact, training algorithms, data acquisition, and hyperparameter tuning were outlined to provide an in-depth understanding of the recent situations. The outcome of this review comprehensively examines various methods for predicting the RUL of LIB in EV applications, offering insights into their advantages, limitations, and research challenges. Recommendations for future trends in LIBs technology comprise enhancing prognostic accuracy and developing robust approaches to guarantee sustainable operation and management.
Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is important to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) can be verified by independent tests. However, this paper addresses data-driven approaches to state of health monitoring of maritime battery systems based on operational sensor data. Results from various approaches to sensor-based, data-driven degradation monitoring of maritime battery systems will be presented, and advantages and challenges with the different methods will be discussed. The different approaches include cumulative degradation models and snapshot models. Some of the models need to be trained, whereas others need no prior training. Moreover, some of the methods only rely on measured data, such as current, voltage and temperature, whereas others rely on derived quantities such as state of charge (SOC). Models include simple statistical models and more complicated machine learning techniques. Different datasets have been used in order to explore the various methods, including public datasets, data from laboratory tests and operational data from ships in actual operation. Lessons learned from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.
The global demand for energy has increased enormously as a consequence of technological and economic advances. Instantaneous delivery of energy is available, but it cannot be continually supplied via the power grid to technical devices, automobiles, etc. The supply-demand mismatch of energy could be resolved with the use of a lithium-ion battery (LIB) as a power storage device. The overall performance of the LIB is mostly determined by its principal components, which include the anode, cathode, electrolyte, separator, and current collector. The materials of the battery's various components are investigated. The general battery structure, concept, and materials are presented here, along with recent technological advances. There are numerous opportunities to overcome some significant constraints to battery performance, such as improved techniques and higher electrochemical performance materials. The future research approach has been directed toward improving the stability, strength, cyclic, and electrochemical performance of battery materials in each of these fields.
This Battery Atlas aims to meet the challenges described by providing as detailed as possible an insight into the individual topics of the lithium-ion battery.
For this purpose, the Battery Atlas shows the competence carriers and classifies them on the European map. It is important to mention that the Battery Atlas cannot claim to be exhaustive, but does provide an overview that is as comprehensive as possible.
Lithium-ion (Li-ion) batteries have been utilized increasingly in recent years in various applications, such as electric vehicles (EVs), electronics, and large energy storage systems due to their long lifespan, high energy density, and high-power density, among other qualities. However, there can be faults that occur internally or externally that affect battery performance which can potentially lead to serious safety concerns, such as thermal runaway. Thermal runaway is a major challenge in the Li-ion battery field due to its uncontrollable and irreversible nature, which can lead to fires and explosions, threatening the safety of the public. Therefore, thermal runaway prognosis and diagnosis are significant topics of research. To efficiently study and develop thermal runaway prognosis and diagnosis algorithms, thermal runaway modeling is also important. Li-ion battery thermal runaway modeling, prediction, and detection can help in the development of prevention and mitigation approaches to ensure the safety of the battery system. This paper provides a comprehensive review of Li-ion battery thermal runaway modeling. Various prognostic and diagnostic approaches for thermal runaway are also discussed.
As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries.
An accurate estimation of the internal states of lithium-ion batteries is critical to improving the reliability and durability of battery systems. Data-driven methods have exhibited enormous potential for precisely capturing electric and thermal cell dynamics with a low computational cost. However, challenges remain regarding accurate and low-cost data acquisition as electrode-level states are unmeasurable with conventional sensors. This paper presents a hybrid state estimation method for lithium-ion batteries integrating physics-based and machine learning models to leverage their
respective strengths. An electrochemical-thermal model is developed and experimentally verified, which is employed to
generate a large quantity of data, i.e., voltage, current, temperature and internal electrochemical states, under a comprehensive operating condition matrix including various load profiles and temperatures. These data are fed to train a deep neural network to estimate the internal concentrations and potentials in the electrodes and the electrolyte at different spatial positions. The results show that the proposed approach is capable of bridging spatial, temporal and chemical complexity and achieves a maximum error of 2.93% for all the estimated states under new ambient temperatures, indicating high reliability and generalization ability with solid robustness to input noises and outperforming the one-dimensional network under both normal and noisy conditions.
Efficient and reliable energy storage systems are crucial for our modern society. Lithium-ion batteries (LIBs) with excellent performance are widely used in portable electronics and electric vehicles (EVs), but frequent fires and explosions limit their further and more widespread applications. This review summarizes aspects of LIB safety and discusses the related issues, strategies, and testing standards. Specifically, it begins with a brief introduction to LIB working principles and cell structures, and then provides an overview of the notorious thermal runaway, with an emphasis on the effects of mechanical, electrical, and thermal abuse. The following sections examine strategies for improving cell safety, including approaches through cell chemistry, cooling, and balancing, afterwards describing current safety standards and corresponding tests. The review concludes with insights into potential future developments and the prospects for safer LIBs.
Accurate identification of physical parameters of a lithium-ion electrochemical model is of critical importance for next-generation battery management systems. The complexity of the electrochemical model increases the difficulty of the identification process, and hence the analysis of parameter identifiability is the cornerstone for accurate parameter identification. The overarching goal of this paper is to analyze the parameter sensitivity of an electrochemical model under both the charging process and real-world driving cycles. The boundaries for the sensitivity analysis of 26 physical parameters are determined with a systematic benchmarking of published parameters for lithium Nickel-Manganese-Cobalt-Oxide/graphite cells. In particular, the sensitivity of the parameters is analyzed not only for terminal voltage but also for essential states in an electrochemical model-based battery management system, e.g., cathode bulk state of charge, cathode surface state of charge and anode potential. The sensitivity matrices of the parameters under different C-rates and depth of discharge regions clearly show their different influences on capacity-related parameters and other parameters. Furthermore, the rankings of the normalized parameter sensitivity index provide us the identifiability of the parameters, as well as the influence of parameter inaccuracy on the main functionalities in an electrochemical model-based battery management system.
Over the last decade, the electric vehicle (EV) has significantly changed the car industry globally, driven by the fast development of Li-ion battery technology. However, the fire risk and hazard associated with this type of high-energy battery has become a major safety concern for EVs. This review focuses on the latest fire-safety issues of EVs related to thermal runaway and fire in Li-ion batteries. Thermal runaway or fire can occur as a result of extreme abuse conditions that may be the result of the faulty operation or traffic accidents. Failure of the battery may then be accompanied by the release of toxic gas, fire, jet flames, and explosion. This paper is devoted to reviewing the battery fire in battery EVs, hybrid EVs, and electric buses to provide a qualitative understanding of the fire risk and hazards associated with battery powered EVs. In addition, important battery fire characteristics involved in various EV fire scenarios, obtained through testing, are analysed. The tested peak heat release rate (PHHR in kW) varies with the energy capacity of LIBs (EB in Wh) crossing different scales as PHRR=2EB0.6. For the full-scale EV fire test, limited data have revealed that the heat release and hazard of an EV fire are comparable to that of a fossil-fuelled vehicle fire. Once the onboard battery involved in fire, there is a greater difficulty in suppressing EV fires, because the burning battery pack inside is inaccessible to externally applied suppressant and can re-ignite without sufficient cooling. As a result, an excessive amount of suppression agent is needed to cool the battery, extinguish the fire, and prevent reignition. By addressing these concerns, this review aims to aid researchers and industries working with batteries, EVs and fire safety engineering, to encourage active research collaborations, and attract future research and development on improving the overall safety of future EVs. Only then will society achieve the same comfort level for EVs as they have for conventional vehicles.
Among many kinds of batteries, lithium-ion batteries have become the focus of research interest for electric vehicles (EVs), thanks to their numerous benefits. However, there are many limitations of these technologies. This paper reviews recent research and developments of lithium-ion battery used in EVs. Widely used methods of battery sorting are presented. The characteristics and challenges of estimating battery’s remaining useful life (RUL) and state-of-charge (SOC) are critically reviewed, along with a discussion of the strategies to solve these issues. A new method of sorting retired lithium-ion batteries and estimating the RUL and SOC of the retired lithium-ion batteries is proposed.
As batteries become increasingly prevalent in complex systems such as aircraft and electric cars, monitoring and predicting battery state of charge and state of health becomes critical. In order to accurately predict the remaining battery power to support system operations for informed operational decision-making, age-dependent changes in dynamics must be accounted for. Using an electrochemistry-based model, we investigate how key parameters of the battery change as aging occurs, and develop models to describe aging through these key parameters. Using these models, we demonstrate how we can (i) accurately predict end-of-discharge for aged batteries, and (ii) predict the end-of-life of a battery as a function of anticipated usage. The approach is validated through an experimental set of randomized discharge profiles.
A combined SOC (State Of Charge) and SOH (State Of Health) estimation method over the lifespan of a lithium-ion battery is proposed. First, the SOC dependency of the nominal parameters of a first-order RC (resistor-capacitor) model is determined, and the performance degradation of the nominal model over the battery lifetime is quantified. Second, two Extended Kalman Filters with different time scales are used for combined SOC/SOH monitoring: the SOC is estimated in real-time, and the SOH (the capacity and internal ohmic resistance) is updated offline. The time scale of the SOH estimator is determined based on model accuracy deterioration. The SOC and SOH estimation results are demonstrated by using large amounts of testing data over the battery lifetime.
Nonlinear non-Gaussian state-space models are ubiquitous in statistics,
econometrics, information engineering and signal processing. Particle methods,
also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical
approximations to the associated state inference problems. However, in most
applications, the state-space model of interest also depends on unknown static
parameters that need to be estimated from the data. In this context, standard
particle methods fail and it is necessary to rely on more sophisticated
algorithms. The aim of this paper is to present a comprehensive review of
particle methods that have been proposed to perform static parameter estimation
in state-space models. We discuss the advantages and limitations of these
methods and illustrate their performance on simple models.
Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and perfor-mance and to predict end of discharge (EOD) and end of use-ful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by moni-toring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batter-ies that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.
Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.
Structural damage to BGA interconnects incurred during vibration testing has been monitored in the pre-failure space using resistance spectroscopy based state space vectors, rate of change of the state variable, and acceleration of the state variable. The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss-of-functionality are too high to bear. Future state of the system has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostication health management algorithm during the vibration test has been quantified using performance evaluation metrics. The methodology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α-λ metric, sample standard deviation, mean square error, mean absolute percentage error, average bias, relative accuracy, and cumulative relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.
The accurate estimation of lithium battery state of health (SOH) is very important for the safe and stable operation of the battery. Since the user’s charging process is random, it is difficult for the user to know the battery SOH through the charging segment. In this article, we proposed a lithium battery SOH estimation method of random charging process based on convolutional gated recurrent unit (CNN-GRU). The method extracts key features adaptively from the segments of voltage, current and temperature curves in the charging process through the CNN-GRU framework to realize the lithium battery SOH estimation. Compared with traditional methods, this method does not need to manually select or construct feature information and it can achieve high precision SOH evaluation. Through experimental verification, the error of this method can reach to 0.901%.
Safety assurance is essential for lithium-ion batteries in power supply fields, and the remaining useful life (RUL) prediction serves as one of the fundamental criteria for the performance evaluation of energy and storage systems. Based on an improved dual closed-loop observation modeling strategy, an improved anti-noise adaptive long short-term memory (ANA-LSTM) neural network with high-robustness feature extraction and optimal parameter characterization is proposed for accurate RUL prediction. Then, an adaptive state parameter feedback correction strategy is constructed through multiple feature collaboration with its internal coupling mechanism characterization, which considers varying current rates, ambient temperatures, and other influencing parameters. Subsequently, a collaborative multi-parameter optimization is carried out along with the model training and meta-structure fine-tuning. Compared with other optimal existing methods, the maximum root mean square error decreases by 51.80%, the mean absolute error reduces by 26.95%, the maximum mean absolute percentage error decreases by 33.87%, and the R-squared increases by 4.11%. The established multiple-feature collaboration model realizes multi-scale parameter optimization and robust RUL prediction, thus advancing the industrial application of lithium-ion batteries.
Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions.
Lithium-ion batteries have achieved dominance in energy storage systems. Meanwhile, there is a demand for the reliability of lithium-ion batteries. Battery prognostics and health management (PHM) is a discipline that not only provides accurate, early, and online health diagnosis, but also guarantees a robust and precise prediction of the remaining useful life of lithium-ion batteries, independent of the operating conditions. This paper attempts to develop a novel PHM methodology that addresses the points mentioned above. A large dataset including thirty-eight nickel manganese cobalt oxide battery cells is used. The battery cells have been tested under various test conditions to achieve different aging patterns. Afterward, the health indicators that describe the health trajectory of the battery are extracted from partial charging voltage curves. A recurrent neural network called nonlinear autoregressive with exogenous input is developed to estimate battery state of health (SOH) based on the extracted health indicators. The estimated SOH is used as the prognostic feature to develop a remaining useful life of battery (RUL) prediction model based on the similarity-based model. The proposed methods are validated using untrained data. The results indicate that the proposed PHM methodology can estimate the SOH of untrained battery cells with a maximum RMSE of 0.61. The RUL of battery cells with different aging patterns can be predicted with a maximum absolute error of 110 cycles. It can be concluded that the proposed method has the advantages of high precision in the health diagnosis and prognosis of battery cells regardless of their aging patterns, simplicity, and generalization to untrained data. These advantages point out the feasibility of the proposed method for online prognostics and health management of lithium-ion batteries.
We are performing the digital transition of industry, living the 4th industrial revolution, building a new World in which the digital, physical and human dimensions are interrelated in complex socio-cyber-physical systems. For the sustainability of these transformations, knowledge, information and data must be integrated within model-based and data-driven approaches of Prognostics and Health Management (PHM) for the assessment and prediction of structures, systems and components (SSCs) evolutions and process behaviors, so as to allow anticipating failures and avoiding accidents, thus, aiming at improved safe and reliable design, operation and maintenance.
There is already a plethora of methods available for many potential applications and more are being developed: yet, there are still a number of critical problems which impede full deployment of PHM and its benefits in practice. In this respect, this paper does not aim at providing a survey of existing works for an introduction to PHM nor at providing new tools or methods for its further development; rather, it aims at pointing out main challenges and directions of advancements, for full deployment of condition-based and predictive maintenance in practice.
Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs). The ability to model and forecast remaining useful life of these batteries enables UAV reliability assurance. Building principled accurate models is challenging due to the complex electrochemistry that governs battery operation. Alternatively, reduced order models have the advantage of capturing the overall behavior of battery discharge, although they suffer from simplifications and residual discrepancy. This paper presents a hybrid modeling approach that directly implements physics within deep neural networks. While most of the input–output relationship is captured by reduced-order models, data-driven kernels reduce the gap between predictions and observations. A reduced-order model based on Nernst and Butler–Volmer equations represents the overall battery discharge, and a multilayer perceptron models the battery non-ideal voltage. Battery aging is characterized by time-dependent internal resistance and the amount of available Li-ions, which are modeled through an ensemble of variational Bayesian multilayer perceptrons. The approach is validated using data publicly available through the NASA Prognostics Center of Excellence website. Results showed that our hybrid battery prognosis model can be successfully calibrated, even with a limited number of observations. Moreover, the model can help optimizing battery operation by offering long-term forecast of battery capacity.
Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.
The aim of this study is that of presenting a new diagnostic and prognostic method aimed at automatically detecting deviations from the expected degradation dynamics of the batteries due to changes in the operating conditions, or, possibly, anomalous behaviors, and predicting their remaining useful life (RUL) in terms of their state-of-life (SOL), without needing to derive any complex physics-based models and/or gather huge amounts of experimental data to cover all possible operative/fault conditions. The proposed method in fact exploits the real time framework offered by particle filtering and resorts to neural networks in order to build a suitable parametric measurement equation, which provides the algorithm with the capability of automatically adjusting to different battery's dynamic behaviors. The results of this study demonstrate the satisfactory performances of the algorithm in terms of adaptability and diagnostic sensibility, with reference to suitably identified case studies based on actual Lithium-Ion battery capacity data taken from the prognostics data repository of the NASA Ames Research Center database and of the CALCE Battery Group.
Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning the long-term dependencies among the capacity degradations. This paper investigates deep-learning-enabled battery RUL prediction. The long short-term memory (LSTM) recurrent neural network (RNN) is employed to learn the long-term dependencies among the degraded capacities of lithium-ion batteries. The LSTM RNN is adaptively optimized using the resilient mean square back-propagation method, and a dropout technique is used to address the overfitting problem. The developed LSTM RNN is able to capture the underlying long-term dependencies among the degraded capacities and construct an explicitly capacity-oriented RUL predictor, whose long-term learning performance is contrasted to the support vector machine model, the particle filter model, and the simple RNN model. Monte Carlo simulation is combined to generate a probabilistic RUL prediction. Experimental data from multiple lithium-ion cells at two different temperatures are deployed for model construction, verification, and comparison. The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods.
Lithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database.
Accurate prediction of battery's remaining useful life (RUL) is significant for the reliability and the cost of systems. This paper presents a new Particle Filter (PF) framework for lead-acid battery's RUL prediction by incorporating the battery's electrochemical model. An electrochemical model that simulates the charging and discharging of lead-acid battery is introduced. The effectiveness of both the model and parameter identification is validated through both synthetic and experimental data. In the new PF framework, model parameters that reflect the degradation of battery are seen as state variables, the procedure of capacity simulation and the fitting equations of known state variables are measurement model and process model respectively. Aging experiment is depicted and applied to validate the effectiveness of the method. RUL predictions are made with two different beginning points, the results of which show that the new electrochemical-model-based PF has better state variable stability and prediction accuracy than the traditional data-driven PF.
Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods which have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods.
Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods.
An iterative algorithm is proposed for nonlinearly constrained optimization calculations when there are no derivatives. Each iteration forms linear approximations to the objective and constraint functions by interpolation at the vertices of a simplex and a trust region bound restricts each change to the variables. Thus a new vector of variables is calculated, which may replace one of the current vertices, either to improve the shape of the simplex or because it is the best vector that has been found so far, according to a merit function that gives attention to the greatest constraint violation. The trust region radius ρ is never increased, and it is reduced when the approximations of a well-conditioned simplex fail to yield an improvement to the variables, until ρ reaches a prescribed value that controls the final accuracy. Some convergence properties and several numerical results are given, but there are no more than 9 variables in these calculations because linear approximations can be highly inefficient. Nevertheless, the algorithm is easy to use for small numbers of variables.
An algorithm, the bootstrap filter, is proposed for implementing
recursive Bayesian filters. The required density of the state vector is
represented as a set of random samples, which are updated and propagated
by the algorithm. The method is not restricted by assumptions of
linearity or Gaussian noise: it may be applied to any state transition
or measurement model. A simulation example of the bearings only tracking
problem is presented. This simulation includes schemes for improving the
efficiency of the basic algorithm. For this example, the performance of
the bootstrap filter is greatly superior to the standard extended Kalman
filter
Increasingly, for many application areas, it is becoming important
to include elements of nonlinearity and non-Gaussianity in order to
model accurately the underlying dynamics of a physical system. Moreover,
it is typically crucial to process data on-line as it arrives, both from
the point of view of storage costs as well as for rapid adaptation to
changing signal characteristics. In this paper, we review both optimal
and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking
problems, with a focus on particle filters. Particle filters are
sequential Monte Carlo methods based on point mass (or "particle")
representations of probability densities, which can be applied to any
state-space model and which generalize the traditional Kalman filtering
methods. Several variants of the particle filter such as SIR, ASIR, and
RPF are introduced within a generic framework of the sequential
importance sampling (SIS) algorithm. These are discussed and compared
with the standard EKF through an illustrative example
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
Global EV outlook 2023
- Iea