Useful Life Of Battery Research Articles

BATERIAS DE CARROS ELÉTRICOS: MANUTENÇÕES E DESCARTES

The rapid growth in the adoption of electric vehicles brings to light challenges related to the maintenance and disposal of batteries, which are essential components for the operation of these vehicles. The central issue addressed is the sustainability of the battery life cycle, considering both the efficiency of their maintenance and the environmental impacts resulting from inadequate disposal. The context involves the transition to a greener economy and the demands for clean technologies, which makes it essential to discuss appropriate practices to extend the useful life of batteries and ensure their safe disposal. The main objective of this study is to analyze the maintenance practices of electric car batteries and disposal strategies, focusing on minimizing environmental impacts and optimizing the life cycle of components. The research seeks to identify effective methods to extend the useful life of batteries, in addition to evaluating existing recycling policies and processes. The methodology used includes a literature review of current battery maintenance and disposal practices, in addition to the analysis of case studies that exemplify different approaches and results in international contexts. The research also incorporates interviews with industry experts, aiming to obtain a comprehensive and updated view of best practices and challenges faced. This work contributes to the discussion on sustainability in the automotive sector and to the formulation of more efficient policies for the management of technological waste.

Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries

AbstractTo ensure long and reliable operation of lithium‐ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li‐ion batteries, using physical model‐based methods for accurate description is challenging. Therefore, building data‐driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short‐term model and the remaining useful life of batteries using a long‐term iterative model. The validity of this method is verified using the open‐source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions.

Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron

Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model.

Prediction of Remaining Useful Life of Battery Using Partial Discharge Data

Lithium-ion batteries are cornerstones of renewable technologies, which is why they are used in many applications, specifically in electric vehicles and portable electronics. The accurate estimation of the remaining useful life (RUL) of a battery is pertinent for durability, efficient operation, and stability. In this study, we have proposed an approach to predict the RUL of a battery using partial discharge data from the battery cycles. Unlike other studies that use complete cycle data and face reproducibility issues, our research utilizes only partial data, making it both practical and reproducible. To analyze this partial data, we applied various deep learning methods and compared multiple models, among which ConvLSTM showed the best performance, with an RMSE of 0.0824. By comparing the performance of ConvLSTM at various ratios and ranges, we have confirmed that using partial data can achieve a performance equal to or better than that obtained when using complete cycle data.

Remaining useful life prediction of lithium-ion batteries combined with SVD-SDAE and support vector quantile regression

Lithium-ion batteries are important energy storage materials, and the prediction of their remaining useful life has practical importance. Since traditional feature extraction methods depend on parameter settings and have poor adaptability, singular value decomposition was used to extract 15 health indicators from the degradation data of lithium-ion batteries. To eliminate redundancy among the extracted health indicators, Spearman correlation analysis was subsequently used to determine the most appropriate health indicators. On this basis, the selected health indicators were processed by the denoising stack autoencoder, and a fusion health indicator was obtained. Finally, the support vector quantile regression model was used to predict the battery capacity interval by the fusion health indicator. The National Aeronautics and Space Administration battery dataset and Massachusetts Institute of Technology battery dataset were used to verify the validity and generalizability of our proposed model, and our proposed model was compared with the existing four classical prediction models. The experimental results showed that our proposed prediction model had higher prediction accuracy and better robustness than the other models and could effectively improve the prediction effect of the remaining useful life of batteries. The mean value of the root mean square error of the predicted results using our proposed model remained within 1.3%, and the mean value of the coefficient of determination was above 0.97.

A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression

The battery will inevitably deteriorate in its service life. Therefore, accurate prediction of the remaining useful life (RUL) is critical to the durability of the battery energy storage systems (ESS). The RUL prediction framework of Gaussian process regression(GPR) based on automatic stack autoencoder (SAE) and improved whale optimization algorithm (WOA) is proposed in this research paper. Firstly, the SAE and gray relation analysis are proposed to extract indirect health indicators (HIs) from battery degradation data. Secondly, the Gaussian process regression (GPR) approach combined with the WOA method to establish the RUL prediction framework. Besides, aiming at the disadvantage that WOA is prone to fall into local optimization, the WOA based on the sine cosine algorithm of decentralized foraging strategy is proposed to optimize the prediction ability of the GPR. Finally, compared with the prediction results of existing approaches under the same conditions, the simulation results indicate that the RUL prediction errors based on the proposed algorithm are controlled within 2 %, which has good advantages and prediction ability.

Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms

Prediction of state-of-health and remaining useful life is crucial to the safety of lithium-ion batteries. Existing state-of-health and remaining useful life prediction methods are not effective in revealing the correlation among features. Establishing the correlation can help identify features with high similarities and aggregate them to improve the accuracy of predictive models. Moreover, existing methods, such as recurrent neural networks and long short-term memory, have limitations in state-of-health and remaining useful life predictions as they are not capable of using the most relevant part of time-series data to make predictions. To address these issues, a two-stage optimization model is introduced to construct an undirected graph with optimal graph entropy. Based on the graph, the graph convolutional networks with different attention mechanisms are developed to predict the state-of-health and remaining useful life of a battery, where the attention mechanisms enable the neural network to use the most relevant part of time series data to make predictions. Experimental results have shown that the proposed method can accurately predict the state-of-health and remaining useful life with a minimum root-mean-squared-error of 0.0104 and 5.80, respectively. The proposed method also outperforms existing data-driven methods, such as gradient-boosting decision trees, long short-term memory, and Gaussian process.

The effect of fast charging and equalization on the reliability and cycle life of the lead acid batteries

Three-wheeled e-rickshaws driven by lead-acid batteries are a common means of transport in Asian countries due to their low cost, and durability. Conventional charging is time-consuming and lacks proper controls for the termination of the charge. Though fast charging can significantly reduce the charging time to about one-third or less, it is prone to temperature rise, excessive gassing, and reduction in the useful life of the battery. A new fast-charging procedure along with periodic equalizing is proposed in this paper. A multistage constant current fast charger with depolarization pulse was used to charge a 48 V e-rickshaw battery pack in 3 h 37 min from 20 % to 80 % state of charge. The effect of the said fast charging procedure on the coulombic efficiency, end voltage pattern, capacity degradation, reliability, and useful life of the lead-acid batteries is investigated. Experimental results for 150 charging-discharging cycles show a temperature rise up to 5–6 °C, average coulombic efficiency of 93 %, and a maximum top-of-charge voltage of 2.6 Volts Per Cell (VPC). Attainment of such a low-temperature rise coupled with high coulombic efficiency during fast charge is the most competitive result reported for commercial batteries.A regression model and best-fit probability distribution (Weibull 3-parameter distribution) are used to estimate the degradation rate, and Cycle-To-Failure (CTF). The expected life of the batteries subjected to such a fast charging and equalizing charge is predicted to be 1296 cycles, which is about 2 times the current life of the battery. The proposed fast charger along with periodic equalizing is, therefore, a promising and sustainable option for charging lead-acid batteries.

Remaining useful life prediction of Lithium-ion battery using improved support vector regression and fuzzy information granulation

With the widespread use of lithium-ion batteries in various fields, battery Prognostics and Health Management (PHM) technologies are gaining more and more attention. Repeated use of batteries can lead to degradation of battery performance and thus affect battery life. Accurate prediction of the remaining useful life (RUL) of batteries is crucial and is the most central issue in battery PHM. In this paper, a method based on a combination of fuzzy information granulation (FIG) and support vector regression with artificial bee colony optimization (ABC-SVR) is proposed to estimate the RUL of Lithium-ion batteries. First, the capacity degradation data are divided into several windows using the FIG method. Second, the maximum and minimum values of each window are predicted separately using the ABC-SVR algorithm to obtain the information of the prediction window. Finally, the missing values of the prediction windows are complemented by the linear interpolation method to obtain the complete capacity prediction values, and the remaining useful life of the battery can be calculated according to the failure threshold. The results show that the proposed method obtains the RUL value with high accuracy.

Online Prediction of Remaining Useful Life for Li-Ion Batteries Based on Discharge Voltage Data

The state of health and remaining useful life of lithium-ion batteries are key indicators for the normal operation of electrical devices. To address the problem of the capacity of lithium-ion batteries being difficult to measure online, in this paper, we propose an online method based on particle swarm optimization and support vector regression to estimation the state of health and remaining useful life. First, a novel health indicator is extracted from the discharge voltage to characterize the capacity of lithium-ion batteries. Then, based on the capacity degradation characteristics, support vector regression is used to predict the remaining useful life of these batteries, and particle swarm optimization is selected to optimize the parameters of the support vector regression, which effectively enhances the predictive performance of the model. Validated for the NASA battery aging dataset, when training with the first 40% of the dataset, the maximum error of the predicted remaining useful life was four cycles, and when training with the first 50% of the dataset, the maximum error of the predicted remaining useful life was only one cycle. When comparing to a deep neural network, support vector regression, long short-term memory algorithms and existing similar methods in the literature, the particle swarm optimization and support vector regression method can obtain more accurate prediction results.

Developing an online data-driven approach for prognostics and health management of lithium-ion batteries

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.

A Novel Prediction Process of the Remaining Useful Life of Electric Vehicle Battery Using Real-World Data

In modern society, environmental sustainability is always a top priority, and thus electric vehicles (EVs) equipped with lithium-ion batteries are becoming more and more popular. As a key component of EVs, the remaining useful life of battery directly affects the demand of the EV supply chain. Accurate prediction of the remaining useful life (RUL) benefits not only EV users but also the battery inventory management. There are many existing methods to predict RUL based on state of health (SOH), but few of them are suitable for real-world data. There are several difficulties: (1) battery capacity is not easy to obtain in the real world; (2) most of these methods use the individual data for each battery, and the computing processes are difficult to perform in the cloud; (3) there is a lack of approaches for real-time SOH estimating and RUL predicting. This paper adopts several statistical methods to perform the prediction and compars the results of different models on experimental data (NASA dataset). Then, real-world data were implemented for an online process of RUL prediction. The main finding of this research is that the required CPU time was short enough to meet the daily usage after the real-world data was implemented for an online process of RUL prediction. The feasibility and precision of the prediction model can help to support the frequency control in power systems.

State of Health Prediction of Lithium-Ion Batteries Using Accelerated Degradation Test Data

Lithium-ion batteries are the core equipment of electric vehicles (EVs) and they are expected to spread out through smart power systems [e.g., smart grids (SGs)]. Batteries are subject to degradation over time, which gradually reduces the supplied voltage and capacity until they fail to meet minimum requirements. Battery prognostic is mandatory in order to keep track of the degradation over time and to schedule maintenance and replacement before failures, which can cause severe damages and harmful effects on EVs and SGs. However, the estimation of the state of health and the remaining useful life of batteries is not trivial, since neither can be directly measured. In this article, multiple linear regression models are developed within deterministic and probabilistic frameworks to predict the SoH of batteries based on historical charge/discharge data. The models exploit informative data collected for the battery under investigation and stored data of batteries characterized by accelerated degradation tests. Numerical experiments based on actual data are presented to validate the performance of the proposal.

Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression

Lithium-ion batteries have become an integral part of our lives, and it is important to find a reliable and accurate long-term prognostic scheme to supervise the performance degradation and predict the remaining useful life of batteries. In the perspective of information fusion methodology, an interacting multiple model framework with particle filter and support vector regression is developed to realize multi-step-ahead estimation of the capacity and remaining useful life of batteries. During the multi-step-ahead prediction period, the support vector regression model with sliding windows is used to compensate the future measurements online. Thus, the interacting multiple model with particle filter can relocate the particles and update the capacity estimation. The probability distribution of the remaining useful life is also obtained. Finally, the proposed method is compared and validated with particle filter model using the benchmark data. The experimental results prove that the proposed model yields stable forecasting performance and narrows the uncertainty in remaining useful life estimation.

Predicting the state of charge and health of batteries using data-driven machine learning

Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future. Predicting the properties of batteries, such as their state of charge and remaining lifetime, is crucial for improving battery manufacturing, usage and optimisation for energy storage. The authors discuss how machine learning methods and high-throughput experimentation provide a data-driven approach to this problem, and highlight challenges in building models which provide fast and accurate battery state predictions.

Real‐time implementation of battery bank charge–discharge based on neural inverse optimal control

In a renewable energy generation system, the batteries are one of the main components for energy storage. To maximise the useful life of batteries, it is important to ensure a safe and effective rate in the process of battery charging and discharging. To control these processes, different electronic circuits can be used, of which, the most commonly implemented is the DC–DC buck–boost converter. Two different topologies with their corresponding controllers are needed because the energy transfer is bidirectional. This work develops a unique neural inverse optimal controller with online identification for both charge and discharge processes of the battery bank. The main feature of the proposed controller is that it does not present dependence on the converter parameter variations; for this reason, it can be applied for systems with different power requirements without considerable changes in its application. This study discusses the development and real-time operation of a neural controller based on the inverse optimal control algorithm for charge–discharge of a battery bank.

Extreme Learning Machine Based Prognostics of Battery Life

This paper presents a prognostic scheme for estimating the remaining useful life of Lithium-ion batteries. The proposed scheme utilizes a prediction module that aims to obtain precise predictions for both short and long prediction horizons. The prediction module makes use of extreme learning machines for one-step and multi-step ahead predictions, using various prediction strategies, including iterative, direct and DirRec, which use the constant-current experimental capacity data for the estimation of the remaining useful life. The data-driven prognostic approach is highly dependent on the availability of high quantity of quality observations. Insufficient amount of available data can result in unsatisfactory prognostics. In this paper, the prognostics scheme is utilized to estimate the remaining useful life of a battery, with insufficient direct data available, but taking advantage of observations available from a fleet of similar batteries with similar working conditions. Experimental results show that the proposed prognostic scheme provides a fast and efficient estimation of the remaining useful life of the batteries and achieves superior results when compared with various state-of-the-art prediction techniques.

Gaussian process regression for forecasting battery state of health

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.

Comparison of prognostic algorithms for estimating remaining useful life of batteries

The estimation of remaining useful life (RUL) of a faulty component is at the centre of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. RUL prediction needs to contend with multiple sources of errors, like modelling inconsistencies, system noise and degraded sensor fidelity, which leads to unsatisfactory performance from classical techniques like autoregressive integrated moving average (ARIMA) and extended Kalman filtering (EKF). The Bayesian theory of uncertainty management provides a way to contain these problems. The relevance vector machine (RVM), the Bayesian treatment of the well known support vector machine (SVM), a kernel-based regression/classification technique, is used for model development. This model is incorporated into a particle filter (PF) framework, where statistical estimates of noise and anticipated operational conditions are used to provide estimates of RUL in the form of a probability density function (pdf). We present here a comparative study of the above-mentioned approaches on experimental data collected from Li-ion batteries. Batteries were chosen as an example of a complex system whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions.

Storing batteries in airtight tube found to prolong battery life

Batteries have been essential to the operation of hearing aids since they were first produced in the late 1800s and early 1900s. Vacuum tube hearing aids required two batteries, while carbon hearing aids needed one or more batteries in series, and the user had to wear a separate battery pack. The zinc-oxide batteries used in these devices were expensive and needed to be stored in a cool place to prolong their life. This type of battery discharged at a constant rate, requiring that the user continually turn up the hearing aid's volume as the battery became depleted. Mercury batteries were first produced during World War II. They were smaller and their voltage did not decrease markedly until they were almost depleted. This relieved the user of the need to adjust the volume. In the 1950s, silver-oxide batteries were developed and began to replace the more toxic, environmentally hazardous mercury batteries. However, these batteries were expensive due to the silver content of this type of cell. Rechargeable batteries were tried, but without much commercial success. Today, most hearing aids are powered by zinc-air batteries. This type of battery offers clear advantages over both mercury (less negative impact on the environment) and silver (less expensive to manufacture). Zinc-air batteries require oxygen to produce an electric current. Once purchased, the airtight seal attached to each battery is removed. This allows air (including oxygen) to enter the battery where it stimulates the chemical reaction that produces an electric current. The removable airtight seal allows the battery to be stored for long periods of time with very little loss of power, as long as the seal remains attached. Zinc-air batteries cost an average of $1 apiece, with a useful life that ranges from a few days with a digital CIC to more than a month with a digital BTE instrument. EXTENDING BATTERY LIFE Hearing aid wearers tend to complain about poor battery life if they are required to replace the batteries more than once every week or two. We sought to address this dilemma. Since zinc-air batteries require oxygen to function, we reasoned that depriving the battery of oxygen during periods when it was not in use could extend its useful life. If a zinc-air battery was stored in an air-tight container with minimal dead volume so that the oxygen necessary for the battery to function was kept out, we theorized that the chemical reaction that produces current (and drains the battery) would come to a halt, a halt that could be easily reversed by exposing the battery to air again. To evaluate the effectiveness of this strategy, we used a voltmeter to measure the voltage of two fresh, size 10 zinc-air batteries, labeled “A” and “B.” The two batteries were used in a pair of digital CIC hearing aids during the day. At night or any other time when the hearing aids were not being used, battery A was removed and stored next to the aid while battery B was placed in a small, sealed tube with just enough space to contain the battery (Nalge Nunc CryoTube, part #65234). We repeated this procedure every day for 7 days. After a week, we measured the voltage again. Results showed an increase of approximately 20%-30% in the useful life of battery B. We repeated this experiment for several sets of batteries, and the results consistently showed increased life for batteries stored in the sealed tube. The same procedure was repeated with battery sizes 312, 13, and 675, with similar results. The question of whether a typical hearing aid user would go to the trouble of removing the battery every night and placing it in a cryotube has been considered. One of the authors (SG) is a hearing aid user and has been using this technique for many years. She is currently wearing a pair of CIC style hearing aids. She finds it convenient to remove the batteries at night and seal them in the cryotube, which is then placed in the carrying case along with the hearing aids (whose battery doors are left open so any collected moisture can evaporate). She has been using the technique with the last three models of hearing aids she has owned and finds that the increase in battery life more than compensates for the simple additional step required. In conclusion, the use of small cryogenic tubes, which are inexpensive and readily available, can significantly prolong the life of zinc-air batteries, resulting in reduced expense for hearing aid wearers.