Remaining Useful Life Prediction for Lithium-ion Batteries Based on Capacity Estimation and Box-Cox Transformation

IEEE Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in intelligent battery management systems (BMSs). The current RUL prediction methods are mainly developed based on offline training, which are limited by sufficiency and reliability of available data. To address this problem, this paper presents a method for RUL prediction based on the capacity estimation and the Box-Cox transformation (BCT). Firstly, the effective aging features (AFs) are extracted from electrical and thermal characteristics of lithium-ion batteries and the variation in terms of the cyclic discharging voltage profiles. The random forest regression (RFR) is then employed to achieve dependable capacity estimation based on only one cells degradation data for model training. Secondly, the BCT is exploited to transform the estimated capacity data and to construct a linear model between the transformed capacities and cycles. Next, the ridge regression algorithm (RRA) is adopted to identify the parameters of the linear model. Finally, the identified linear model based on the BCT is employed to predict the battery RUL, and the prediction uncertainties are investigated and the probability density function (PDF) is calculated through the Monte Carlo (MC) simulation. The experimental results demonstrate that the proposed method can not only estimate capacity with errors of less than 2%, but also accurately predict the battery RUL with the maximum error of 127 cycles and the maximum spans of 95% confidence of 37 cycles in the whole cycle life

A critical review of online battery remaining useful lifetime prediction methods.

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

Battery Calendar Life Estimator Manual Modeling and Simulation

The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

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

State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems

The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring. Doi: 10.28991/ESJ-2023-07-03-02 Full Text: PD

A decision-making model for retired Li-ion batteries

The growth of electric vehicles (EVs) has raised concerns about the disposition of their batteries once they reach their end of life. Currently, recycling is regarded as the potential solution for retired Li-ion batteries (LIBs). However, these LIBs still retain around 80% of their original capacity, which can be repurposed for other energy storage system (ESS) applications in their "second life" before recycling. Yet, there is no guidance for deciding whether to reuse or recycle them. Here, we propose developing a decision-making model that evaluates retired batteries from both technical and economic perspectives. We develop data-driven models and combine them with an equivalent circuit model (ECM) to build module-level aging models. Simulations show that limiting the State of Charge (SOC) operating range and charge current in second life applications can extend the lifetime of LIBs. Upon when and how to use the battery in second life, the simulated lifetime is between 1-6 years..

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School of Energy and Chemical Engineering (Chemical Engineering)Rechargeable batteries have attracted a lot of attention owing to their wide applicability, such as portable/consumer electronics, electric vehicles, and grid-scale applications. Over the past two decades, significant advances have been made in battery technologies. However, advancement in various technologies necessitates batteries that are more efficient because the current levels of performance are inadequate. This has encouraged researchers to design and discover new battery materials to meet future demands. In this context, a fundamental understanding of the polymorphism and charge storage mechanism of battery materials can provide design principles and promote the discovery of novel materials. To achieve this, the multiscale simulation method has been used to study physicochemical phenomena or properties of different time and space scales. In this dissertation, we introduced theoretical studies on polymorphism and charge storage mechanism of battery materials. Specifically, we discussed three newly designed electrode materials, a conventional binder material, and a separator material. In Chapter 1, we provide an overview and the challenges of rechargeable batteries. We then present a general background of the charge storage mechanism and polymorphism phenomenon and their importance in the study and design of rechargeable battery materials. Finally, we describe the modern multiscale computational techniques for rechargeable battery materials such as the density functional theory calculation, density functional tight binding calculation, molecular dynamics simulation, and Monte Carlo simulation. In Chapter 2, we present a theoretical study on the polymorphism and charge storage mechanism of contorted hexabenzocoronene (c-HBC) as a new type of anode material for Li-ion batteries. In this study, the packing polymorphism was demonstrated by disclosing the crystal structure of polymorph ??????, which is the metastable R-3 crystal phase, using computational polymorph prediction. It was also revealed that polymorph ??? was not a polymorph of c-HBCinstead, it is the P31 (or P32) crystal phase of c-HBC with Pd atoms. Moreover, our investigation on the lithium storage mechanism showed that the c-HBC anode exhibited a single-stage Li-ion insertion behavior without voltage penalty, which was attributed to the 3D-ordered empty pores originating from the contorted structure of c-HBC. In Chapter 3, we present a theoretical study on the polymorphism and charge storage mechanism of fluorinated-contorted hexabenzocoronene (F-cHBC) as a potential electrochemical organic electrode material. Based on Monte Carlo computational study, it was revealed that the crystal structure of polymorph I was the energetically stable P21/c crystal phase. Furthermore, theoretical investigation on lithium/sodium storage mechanism showed that Li- and Na-ions could be stored in two distinct sites surrounded by electronegative fluorine atoms and a negatively charged bent edge aromatic ring. In Chapter 4, we present a theoretical study on the polymorphism and charge storage mechanism of the redox-active covalent triazine framework (rCTF) as a promising organic anode material for Li-ion batteries. The potential energy analysis suggested that the rCTF can potentially exhibit packing polymorphism for two energy-minimum packing modes, namely, AB and slipped-parallel packing modes. The most stable was the slipped-packing mode. Furthermore, we revealed that the rCTF provided a theoretical capacity of up to 1200 mAh g???1 using quinone, triazine, and benzene rings as the redox-active sites. The structural deformation of rCTF during activation allowed more redox-active sites to be accessible, especially the benzene rings. In Chapter 5, we present a theoretical study on poly(vinylidene fluoride) (PVDF), which is a conventional polymeric binder material for rechargeable batteries. Although it is rarely considered in the battery field, PVDF is a semicrystalline polymer with various polymorphs that have different polarization characteristics. In this study, the effect of the crystal phases of PVDF, specifically ??- and ??-PVDFs, on battery performance was investigated. We showed that compared to negligible polarization of the paraelectric ??-PVDF, the strong polarization generated by the ferroelectric ??-PVDF can effectively transport electrons and Li-ions, leading to reduction in the charge transfer resistance and mitigation of the concentration polarization in the Li-ion battery system. In Chapter 6, we present a theoretical study on polymorphism of chitin separator material and its interaction with electrolyte. As a semicrystalline biopolymer, chitin can exist in two polymorphs, ??- and ??-phase. These crystals have different molecular conformation and arrangement, resulting in different polarization characteristics. Based on density functional theory calculations and molecular dynamics simulations, we revealed that both polymorphs of chitin had excellent electrolyte-uptaking property and high physicochemical affinity to Li-ions with binding reversibility.ope

Multidimensional prognostics for rotating machinery: A review

open access articleDetermining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and avail- ability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidi- mensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and draw- backs and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment

Combined classification and queuing system optimization approach for enhanced battery system maintainability, A

2022 Spring.Includes bibliographical references.Battery systems are used as critical power sources in a wide variety of advanced platforms (e.g., ships, submersibles, aircraft). These platforms undergo unique and extreme mission profiles that necessitate high reliability and maintainability. Battery system failures and non-optimal maintenance strategies have a significant impact on total fleet lifecycle costs and operational capability. Previous research has applied various approaches to improve battery system reliability and maintainability. Machine learning methodologies have applied data-driven and physics-based approaches to model battery decay and predict battery state-of-health, estimation of battery state-of-charge, and prediction of future performance. Queuing theory has been used to optimize battery charging resources ensure service and minimize cost. However, these approaches do not focus on pre-acceptance reliability improvements or platform operational requirements. This research introduces a two-faceted approach for enhancing the overall maintainability of platforms with battery systems as critical components. The first facet is the implementation of an advanced inspection and classification methodology for automating the acceptance/rejection decision for batteries prior to entering service. The purpose of this "pre-screening" step is to increase the reliability of batteries in service prior to deployment. The second facet of the proposed approach is the optimization of several critical maintenance plan design attributes for battery systems. Together, the approach seeks to simultaneously enhance both aspects of maintainability (inherent reliability and cost-effectiveness) for battery systems, with the goal of decreasing total lifecycle cost and increasing operational availability

NMR and neutron total scattering studies of silicon-based anode materials for lithium-ion batteries

Silicon (in the form of lithium silicides) has almost ten times the theoretical charge storage capacity of graphite, the anode material used in most commercially-available lithium-ion batteries. Replacing graphite with silicon therefore promises a substantial improvement over the state-of-the-art in electrochemical energy storage. However, it has proved difficult to realise this high theoretical capacity in a practical electrochemical cell and maintain it over repeated charge-discharge cycles. This dissertation presents experimental work probing the changes in local structure occurring during the electrochemical reactions of lithium with silicon, using neutron total scattering and nuclear magnetic resonance, together with novel processing methodologies for analysing the resulting data, in the hope of suggesting ways of improving the performance of silicon-based lithium-ion batteries. Neutron total scattering patterns were obtained from silicon-based anode materials extracted from cells at various states of charge. These samples were composed of a heterogeneous mixture of amorphous, crystalline and disordered crystalline materials. Reverse Monte Carlo is a technique for obtaining structural information from experimental data (particularly total scattering patterns) from amorphous and disordered crystalline materials. However, previously existing Reverse Monte Carlo software could only handle homogeneous materials. Therefore, the RMCprofile software package was extended to handle data from heterogeneous samples. The improved RMCprofile was applied to the aforementioned total scattering patterns, but the much stronger scattering from the other components (themselves not well-characterised) swamped that from the lithium silicide. Future work should attempt to reduce the scattering from the inactive components, particularly the hard-to-model incoherent scattering. NMR data were acquired in situ from silicon-nanowire-based lithium-ion batteries during repeated charge-discharge cycles, achieving much better electrochemical performance than had been seen in previous in situ experiments with silicon. Owing to the large quantities of data obtained, an automated, model-free dimensionality reduction technique was needed. The NMR data were processed using principal component analysis and a variant of non-negative matrix factorisation. With both of these methods, one of the components was found to be associated with high voltages vs. ${Li \vert{} Li^{+}}$ (i.e. a fully discharged anode). This region has seen very little interest by comparison with the low voltage (high levels of lithiation) region of the charge-discharge cycle, so this discovery suggests a new avenue for future research.Cambridge Nano Science and Technology Doctoral Training Centr