Incremental Capacity Research Articles

Degradation characteristics investigation for lithium-ion cells with NCA cathode during overcharging

This paper explores the major degradation characteristics of commercial lithium-ion battery cells with nickel–cobalt-aluminum-oxide (NCA) electrode during cyclic overcharging, and proposes non-destructive methods for detecting overcharging degradation failure. The experimental results show that battery capacity drops significantly with increasing overcharge depth and number of cycles especially during the first three cycles and when the charging termination voltage is set to 5 V. At the same time, the cell overcharge tolerance decreases with the cyclic overcharging. The combination of the electrochemical impedance spectroscopy and the incremental capacity and differential voltage analysis is used to diagnose cell degradation during cyclic overcharging. Three main degradation modes are identified and quantified by extracting characteristic parameters such as internal resistance and peak, valley, and curve position changes of incremental capacity curves. It is concluded that loss of lithium inventory and loss of active materials are the most dominant degradation modes during cyclic overcharging. Besides, the sharp increase of the third peak on incremental capacity curves has been identified as a unique feature of overcharging degradation, which can be used for diagnosing cyclic overcharging-induced degradation for batteries with NCA cathode.

Physics-Based Methods and Tools for Rapid Classification, Quantification, and Forecasting of Lithium-Ion Battery Aging Modes and Life

Physics-based methods and tools for rapid classification, quantification, and forecasting of lithium-ion battery aging modes and life Sangwook Kim,1 Zonggen Yi,1 Ross R. Kunz,1 Eric J. Dufek,1 Tanvir Tanim,1 Kevin L. Gering1, Bor-Rong Chen,1 Peter Weddle, 2 Kandler Smith, 2 1 Energy and Environmental Science and Technology, Idaho National Laboratory, Idaho Falls, Idaho 83415 USA 2 Center for Energy Conversion & Storage Systems, National Renewable Energy Laboratory, Golden, CO 80401, USA 242nd ECS Meeting, Atlanta, GA, Oct. 9 - 13, 2022 Symposium: A03 – Lithium Ion Batteries Lithium-ion batteries play a central role in powering electric vehicles, stationary energy storage systems, and consumer electronics. Lately, machine learning and deep learning (DL) has been successfully used to gain insights into battery degradation during battery operation and predict lifetime in battery research and development community. Fast and robust classification and quantification of battery aging (e.g., Loss of Lithium Inventory (LLI) and Loss of active Material (LAM)) and accurate long-term forecasting of battery life enable more proactive planning of battery management and preemptive actions of modified operating conditions to achieve safe operations and prolong battery life.Here, we present the development of Incremental Capacity (IC)-DL framework for fast charging conditions as a diagnostic tool for aging mode classification and quantification (Figure 1). The classification and quantification of dominant battery aging modes is conducted using a synthetic-data-based DL modeling framework. Over 6000 initial conditions and 26,000 different aging conditions are generated using IC model to train DL. We applied trained DL classification and quantification algorithm to 22 Gr/NMC532 pouch cells with a different loading and charging protocol tested up to 600 cycles. Besides the analysis of reference performance test data at C/20, cycle-by-cycle data at higher C-rate is analyzed. This IC-DL framework as a diagnostic tool is also used in different battery chemistries, such as Gr/NMC811and LTO/LMO. IC-DL framework enables unique, rapid identification and quantification of the dominant aging modes at different C-rates in different battery chemistries. Additionally, a prognostic tool, Sigmoidal Rate Expression (SRE)-type mathematics are employed to evaluate the capacity loss and aging mode (i.e., LLI). SREs are robust engines that contain three variables that capture the thermodynamic and kinetic “thumbprint” of the mechanism progression within the context of a batch reactor scenario [1].We show two different methods by which SRE parameters can be early assessed based on quantified values from IC-DL framework; (1) extrapolative techniques using specialized functions to determine SRE parameter convergence and (2) a technique based on deep learning and Monte Carlo framework. Overall results from both methods confirm that we can predict capacity loss and LLI at the end of test (i.e., 600 cycles) within 1-2% absolute error using three weeks of testing data (or 125 cycles). We believe the IC-DL framework combined with SRE-base prognostics will reduce lithium-ion batteries development cycle as well as shorten the turnover time for validation, fulfilling the demands of rapidly growing battery market. References K. Gering, Electrochimica Acta, 228(20), 636-651 (2017) Figure 1

Determination of Leakage Currents Via Voltage Hold and Voltage Relaxation Method Using High Precision Coulometry - a Comparison and Optimization Study

Parasitic side reactions on the surface of the anode and the cathode of lithium-ion batteries contribute significantly to calendar and cyclic aging [1, 2]. In order to investigate these parasitic side reactions, such as solid electrolyte interface growth, this study focuses on two methods broadly utilized to determine leakage currents: the voltage hold and the voltage relaxation method. Regarding the voltage relaxation method, the open circuit voltage (OCV) decay is observed over weeks without allowing active electrode de-/lithiation [3] and subsequently, a small pulse is performed to calculate the leakage current [4]. For the voltage hold method, a defined voltage is kept constant, which compensates the parasitic side effects and allows active electrode de-/lithiation to maintain the state of charge (SoC) [5, 6].To compare these methods, different results are found in literature. On the one hand, both methods were reported to deliver individual results [3, 7], while other research activities [8] found the variance only at 100% SoC.Therefore, voltage hold and voltage relaxation were compared in this study, utilizing high precision coulometry (HPC). The measurements were conducted on 16 commercial LGChem INR18650MJ1 cylindrical cells at 25 °C, 40 °C and 55 °C and different SoCs of 10%, 50%, 90%, and 100% SoC, respectively. The cells were preconditioned to each SoC and were subsequently stored for 30 days to minimize relaxation and anode overhang effects. Afterwards, voltage hold and voltage relaxation measurements were carried out for 21 days at each temperature and SoC. In addition to the discharge pulse, an incremental capacity analysis (ICA) was conducted for all three temperatures through the whole voltage range to compare and validate the results obtained.The measurements of this study delivered similar results for the voltage hold and the voltage relaxation method, especially at 10% SoC and 50% SoC. Consequently, the voltage hold did not contribute to additional parasitic side reactions from allowing active de-/lithiation of the electrode. Minor deviations were found for 90% SoC and 100% SoC, for which one possible explanation may be the flat shape of the OCV curve, among others. In addition, the results show a strong dependency on the pulse length and strength.This study was part of the project ExZellTUM III, funded by the German Federal Ministry of Education and Research (BMBF) under grant number 03XP0255, supervised by Project Management Jülich (PTJ).Literature [1] Smith, A.; Burns, J.; Dahn, J.: A high precision study of the coulombic efficiency of Li-ion batteries, In: Electrochemical and Solid-State Letters 13, p. A177, 2010 [2] Birkl, C. R.; Roberts, M. R.; McTurk, E.; Bruce, P. G.; Howey, D. A.: Degradation diagnostics for lithium ion cells, In: Journal of Power Sources 341, p. 373-386, 2017 [3] Zilberman, I.; Sturm, J.; Jossen, A.: Reversible self-discharge and calendar aging of 18650 nickel-rich, silicon-graphite lithium-ion cells, In: Journal of Power Sources 425 (9), p. 217-226, 2019 [4] Schmidt, J. P.; Weber, A.; Ivers-Tiffée, E.: A novel and fast method of characterizing the self-discharge behaviour of lithium-ion cells using a pulse-measurements technique, In: Journal of Power Sources 274, p.1231-1238, 2015 [5] Lewerenz, M.; Käbitz, S.; Knips, M.; Münnix, J.; Schmalstieg, J.; Warnecke, A.; Uwe Sauer, D.: New method evaluating currents keeping the voltage constant for fast and highly resolved measurement of Arrhenius relation and capacity fade, In: Journal of Power Sources 353, p.144-151, 2017 [6] Vadivel, N. R.; Ha, S.; He, M.; Dees, D.; Trask, S.; Polzin, B.; Gallagher, K. G.: On leakage current measured at high cell voltages in lithium-ion batteries, In: Journal of The Electrochemical Society, 164 (2), p. 508-A517, 2017 [7] Theiler, M.; Endisch, C.; Lewerenz, M.: Float Current Analysis for Fast Calendar Aging Assessment of 18650 Li(NiCoAl)O2/Graphite Cells, In: Batteries 7 (2), p. 22–22, 2021 [8] Käbitz, S.; Gerschler, J. B.; Ecker, M., Yurdagel, Y.; Emmermacher, B.; André, D.; Mitsch, T.; Uwe Sauer, D.: Cycle and calendar life study of a graphite LiNi1/3Mn1/3Co1/3O2 Li-ion high energy system. Part A: Full cell characterization, In: Journal of Power Sources 239, p. 572-583, 2013

Battery State-of-Health estimation based on multiple charge and discharge features

In this work we propose and evaluate the performance of several linear multifeature models for battery State-of-Health estimation. The models combine high current incremental capacity and dynamic resistance features, which can be obtained during partial constant current charge and discharge, respectively. We construct the models by including fixed sets of features or by applying features selection procedures based on statistical criteria. The proposed models are fitted and evaluated with data from three publicly available battery datasets, including batteries cycled using driving, randomised and fast charging profiles. During the test process, we assess the estimation improvement introduced by each multifeature model by evaluating the reduction of the mean squared error in the State-of-Health estimation with respect to two reference single-feature models already used in recent literature. The collinearity is quantified through the variance inflation factor to indicate the prediction reliability of each model. As main result of this analysis, we propose a simple two-features model as the best compromise between estimation improvement with respect single feature models, and collinearity reduction.

Best practices for incremental capacity analysis

This publication will present best practices for incremental capacity analysis, a technique whose popularity is growing year by year because of its ability to identify battery degradation modes for diagnosis and prognosis. While not complicated in principles, the analysis can often feel overwhelming for newcomers because of contradictory information introduced by ill-analyzed datasets. This work aims to summarize and centralize good practices to provide a strong baseline to start a proper analysis. We will provide general comments on the technique and how to avoid the main pitfalls. We will also discuss the best starting points for the most common battery chemistries such as layered oxides, iron phosphate, spinel or blends for positive electrodes and graphite, silicon oxide, or lithium titanate for negative electrodes. Finally, a set of complete synthetic degradation maps for the most common commercially available chemistries will be provided and discussed to serve as guide for future studies.

Comparative Study of Incremental Capacity Curve Determination Methods for Lithium-Ion Batteries Considering the Real-World Situation

The incremental capacity analysis (ICA) method is a promising method in battery state of health (SOH) estimation studies. The incremental capacity (IC) curve determination is one of the critical parts of the ICA method. However, the uncertain and incomplete charging conditions of real-world electric vehicles (EVs) significantly limit the IC curve determination. This article provides a thorough analysis of the practicality and limitations of four IC curve determination methods based on the datasets collected from real-world operating EVs with a comprehensive comparison scheme. The Lorentz function fitting method is improved by the differential evolution algorithm, breaking the limitation of fixed parameter boundary constraints. A novel PCHIP method is further proposed to determine the IC curve, with higher robustness to realistic uncertain and incomplete charging conditions. The proposed method is validated by real-world data from 40 EVs with low sampling frequency. The results show the extracted features from the IC curves, determined by the proposed method have a stronger correlation with the SOH, allowing the accurate SOH estimation with a 2% error. With less computational resources and sampling frequency requirements, this method shows great potential for the realistic battery management system implementation.

A reliable data-driven state-of-health estimation model for lithium-ion batteries in electric vehicles

The implementation of a precise and low-computational state-of-health (SOH) estimation algorithm for lithium-ion batteries represents a critical challenge in the practical application of electric vehicles (EVs). The complicated physicochemical property and the forceful dynamic nonlinearity of the degradation mechanism require data-driven methods to substitute mechanistic modeling approaches to evaluate the lithium-ion battery SOH. In this study, an incremental capacity analysis (ICA) and improved broad learning system (BLS) network-based SOH estimation technology for lithium-ion batteries are developed. First, the IC curves are drawn based on the voltage data of the constant current charging phase and denoised by the smoothing spline filter. Then, the Pearson correlation coefficient method is used to select the critical health indicators from the features extracted from the IC curves. Finally, the lithium-ion battery SOH is assessed by the SOH estimation model established by an optimized BLS network, where the BLS network is formed through its L2 regularization parameter and the enhancement nodes’ shrinkage scale filtrated by a particle swarm optimization algorithm. The experimental results demonstrate that the proposed method can effectively evaluate the SOH with strong robustness as well as stability to the degradation and disturbance of in-service and retired lithium-ion batteries.

Joint state-of-health and remaining-useful-life prediction based on multi-level long short-term memory model prognostic framework considering cell voltage inconsistency reflected health indicators

Battery-health prognostics for the state-of-health (SOH) and remaining-useful-life (RUL) are necessary to ensure the safety and reliability of system operation. However, the aging of an actual battery pack is related to internal electrochemical reactions in the constituent cells and the inter-cell inconsistency effect, which cannot be measured directly. Thus, reasonable feature selection and an effective prognostic framework are indispensable for achieving feasible and accurate results in actual systems. This paper proposes a prognostic framework for the joint prediction of the SOH and RUL of a battery pack. The proposed method utilizes indirect health indicators (HIs) during the charging stage. Moreover, Pearson correlation analysis and incremental capacity analysis are used to recognize the inconsistency effect and optimize the sampling interval of the HIs that can be adapted to the battery pack. A multi-level long short-term memory (LSTM) model is designed to jointly predict the SOH and RUL for a battery pack. The first-level LSTM model is applied to short-term SOH estimation using the optimized HIs. For the RUL prediction, the second-level LSTM is established iteratively to extend the degradation pattern from the current step to end of life using the SOH estimation result and previous output. The robustness of the extracted features and predictive capability of the multi-level LSTM model are demonstrated by accelerated aging tests for series battery packs. The experimental results confirm that the proposed prognostic framework can provide robust, reliable, and accurate SOH and RUL predictions.

SIGNALLING THEORY STUDY: PREDICT FUTURE CASH FLOWS WITH EARNINGS AND CASHFLOWS IN CONSUMER GOODS INDUSTRIES BEFORE PANDEMIC ERA

This study aims to determine which of the cash flows and earnings have a better ability to predict future cash flows in the two years after the study period. This research was conducted on consumer goods industries listed on the Indonesia Stock Stock Trade in 2016 – 2019 employing a purposive examining procedure. (Dechow, Kothari, & Watts, 1997) found that earnings have a better ability to predict future cash flows than operating cash flows themselves. Another study conducted by (Dahler & febrianto, 2007) utilizing numerous straight relapse models found that operating cash streams for the current year have a better ability than profit in anticipating future working cash streams for both bunches of companies that have positive and negative benefits. Utilizing the same model, this study concludes that earnings have a better ability to predict future cash flows than cash flows themselves. However, cash flows still have a significant ability to predict future cash flows. In expansion, this consider too succeeded in demonstrating that profit has an incremental capacity to anticipate future cash streams.

State of health estimation for lithium-ion batteries based on incremental capacity analysis under slight overcharge voltage

Accurate and reliable estimation of state of health (SOH) for lithium-ion batteries under slight overcharge voltage cycling has great significance for battery management systems. In this study, commercial lithium-ion phosphate batteries are investigated under slight overcharge voltage cycling. The aging mechanism is discussed based on incremental capacity analysis and differential voltage analysis. Moreover, the syncretic health indicator is obtained from the incremental capacity curves based on principal component analysis. Specifically, the capacity retention and Coulombic efficiency are analyzed under slight overcharge voltage cycling. The incremental capacity peaks (i.e., peak B and peak C) are discussed to extract potential health indicators, and a syncretic health indicator is adopted based on principal component analysis. Finally, the Gaussian process regression is established for accuracy SOH estimation with a 95% confidence interval under small data of slight overcharge cycling. In comparison with the traditional methods, the proposed method exhibits higher accuracy with a 95% confidence interval, and the error is limited to 3%.

Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms

Accurate capacity estimation of lithium-ion batteries is of great significance to guarantee their reliability and safety operation. In this paper, a fused capacity estimation method is devised via the co-operation of multi-machine learning algorithms. First, the peak value of incremental capacity curve is extracted as the health feature, and the support vector machine is engaged in data processing and mitigation of the noise-induced unfavorable interference. Then, the preliminary remaining capacity values are estimated based on the incorporation of support vector machine, long short-term memory network and Gaussian process regression with the support of the abstracted health feature. Finally, the random forest algorithm is employed to supply more accurate capacity estimation to fuse the preliminary remaining capacity values. The experimental validations showcase that the advanced algorithm enables to fuse the advantages of individual learners and improve the estimation accuracy. The results indicate that the proposed method can estimate the remaining capacity with the root mean square error of less than 2.4%. In addition, the robustness to noise corruption and the generality to different battery cells are also verified.

Degradation modes and mechanisms analysis of lithium-ion batteries with knee points

The capacity of lithium-ion batteries decreases during usage (cycling) and storage (rest). After some initial charge-discharge cycling, the capacity fade rate has been observed to increase, and the capacity fade curve visibly bends, the onset of which is described as a knee point. The occurrence of the knee point above the end-of-life capacity threshold leads to a shorter life than expected based on the initial capacity fade rate. Although various degradation mechanisms and their effects on lithium-ion batteries are generally known, the degradation mechanisms for the knee point phenomenon have been in contention in the literature. In this paper, aging tests are conducted on custom three-electrode lithium-ion pouch cells to distinguish the contribution of all degradation modes to the cell's capacity fade and determine the one that causes the knee point, which is the acceleration of cathode resistance increase due to the increased degradation rate in cathode interfacial kinetics. Incremental capacity analysis and destructive analysis were further conducted to determine the degradation mechanisms that lead to the acceleration of cathode resistance increase.

An Adaptive Battery Capacity Estimation Method Suitable for Random Charging Voltage Range in Electric Vehicles

Accurately estimating the capacity of lithium-ion batteries in electric vehicles (EVs) is critical for making correct management decisions. However, the randomness of the charging voltage range of EVs can lead to missing observations or reduced accuracy of capacity estimation methods. This article proposes an adaptive battery capacity estimation method suitable for arbitrary charging voltage range based on incremental capacity (IC) analysis and data-driven techniques. All charging conditions of EVs are divided into three categories according to the charging voltage range. Three data-driven estimation submethods with sequential application priority are designed for the three charging conditions separately, including back-propagation neural network with IC peak coordinates as input, ensemble learning with local high IC curve as input, and linear regression with ampere-hour coordinate transformation. The method is based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge to select a suitable estimation submethod under different charging conditions, so as to improve the adaptability. Experimental data is collected from eight commercial lithium-ion battery modules for model establishment and verification. Over 250 000 experimental samples at different states of health and random charging ranges show that the method can accurately estimate battery capacity under arbitrary charging conditions, with a maximum error of 2%.

Characterization of aging mechanisms and state of health for second-life 21700 ternary lithium-ion battery

This paper focuses on the identification of aging mechanisms and the estimation of the state of health (SOH) for second-life 21700 nickel–cobalt–aluminum (NCA) lithium-ion batteries. NCA battery is aged at 1/2 C-rate in the laboratory until its SOH value reaches about 60%. Incremental capacity (IC) and different voltage (DV) and probability density function (PDF) analyses are used to explore the aging mechanisms of NCA battery and build SOH estimation models with and without multicollinearity. The results show that the capacity decays almost linearly throughout the aging cycle and the loss of lithium inventory (LLI) and the loss of active material (LAM) on anode are the main aging mechanisms. The equivalence between PDF and IC/DV analyses is proved by NCA battery. Peak (valley) features from IC, DV and PDF curves are extracted as health factors (HFs), which have good correlation with battery SOH. Principal component analysis (PCA) is employed to reduce the multicollinearity and more accurate SOH evaluation models are built based on principal component regression (PCR). The verification results show that the ICA-PCR, DVA-PCR and PDF-PCR models all have good evaluation accuracy.

Comparison of Li-ion battery chemistries under grid duty cycles

Li-ion batteries are the most widely deployed battery energy storage system (BESS) today but understanding the benefits and cost-effectiveness for a wide range of grid services needs to be fully validated to further expand the market. Hence, various Li-ion battery chemistries currently deployed must be evaluated and compared in terms of performance, lifecycle, economics, and safety under grid services using standardized testing protocols. In this work, commercially available cylindrical cells with four different chemistries from major Li-ion battery manufacturers are subjected to standardized tests developed by the U.S. Department of Energy Office of Electricity (DOE-OE) for frequency regulation, peak shaving, and electric vehicle drive cycles. The results of 15 months of continuous grid service tests on different chemistries are presented, analyzed, and compared in terms of capacity retention, resistance, open-circuit voltage (OCV), cyclic voltammetry (CV), incremental capacity (dQ/dV), differential voltage (dV/dQ), and AC impedance.

SOH prediction of lithium battery based on IC curve feature and BP neural network

Precise battery SOH (state of health) prediction and monitoring are of extreme importance for the future intelligent battery management system (BMS). In this paper, battery discharge experiments at different temperatures were carried out. A battery SOH prediction model based on incremental capacity analysis and BP neural network is proposed to predict battery SOH at different ambient temperatures. By analyzing the correlation between the characteristics of IC curve and SOH, the mapping relationship between temperature and IC curve characteristics is established by using the least square method, and the SOH prediction model at different temperatures is obtained. At the same time, combined with ICA, an online real-time correction prediction model is established, and the characteristic data is continuously updated to ensure the SOH prediction accuracy under different aging states. Finally, the feasibility of the prediction method proposed in this paper is verified by comparing the model test results and experimental results, the average error of the model prediction results is 1.16%. Thus, by establishing the relationship between temperature and IC curve characteristics, the battery SOH at different temperatures can be predicted.

A novel health indicator for online health estimation of lithium-ion batteries using partial incremental capacity and dynamic voltage warping

Real-time state of health (SoH) estimation is essential to guarantee the reliability and safety of a lithium-ion battery system. However, the incomplete charge/discharge process, which is encountered inevitably as diverse customer habits and complex operation conditions, severely limits the use of traditional health indicators (HIs) in a battery management system (BMS). In this study, a novel HI, namely incremental capacity–dynamic voltage warping (IC–DVW), is extracted from partial online discharge voltage and current data. Ideally, IC–DVW shows a strong correlation with the actual capacity fading state as well as good robustness and adaptability to incomplete discharge data. The linear correlation between IC–DVW and capacity is further enhanced by adopting the Box–Cox transformation. In addition, to improve the performance of the partial IC curve, we propose a novel interpolation method, namely keep-raw-data interpolation (KRDI), and combine it with the Savitzky–Golay filter to obtain clear and smooth IC curves. Finally, we verify the robustness of the proposed HI to different voltage ranges, known cycle data, and battery samples. Experimental studies confirm the high accuracy and adaptability of the proposed HI in degradation modeling and online health estimation, especially under frequently occurring incomplete discharge conditions.

Efficacy of 12weeks oral beta-alanine supplementation in patients with chronic obstructive pulmonary disease: a double-blind, randomized, placebo-controlled trial.

BackgroundBeta‐alanine (BA) supplementation increases muscle carnosine, an abundant endogenous antioxidant and pH buffer in skeletal muscle. Carnosine loading promotes exercise capacity in healthy older adults. As patients with chronic obstructive pulmonary disease (COPD) suffer from elevated exercise‐induced muscle oxidative/carbonyl stress and acidosis, and from reduced muscle carnosine stores, it was investigated whether BA supplementation augments muscle carnosine and induces beneficial changes in exercise capacity, quadriceps function, and muscle oxidative/carbonyl stress in patients with COPD.MethodsIn this double‐blind, randomized, placebo (PL)‐controlled trial (clinicaltrials.gov identifier: NCT02770417), 40 patients (75% male) with COPD (mean ± standard deviation: age 65 ± 6 years; FEV1% predicted 55 ± 14%) were assigned to 12 weeks oral BA or PL supplementation (3.2 g/day). The primary outcome, i.e. muscle carnosine, was quantified from m. vastus lateralis biopsies obtained before and after intervention. Co‐primary outcomes, i.e. incremental and constant work rate cycle capacity, were also assessed. Linear mixed model analyses were performed. Compliance with and side effects of supplement intake and secondary outcomes (quadriceps strength and endurance, and muscle oxidative/carbonyl stress) were also assessed.ResultsBeta‐alanine supplementation increased muscle carnosine in comparison with PL in patients with COPD (mean difference [95% confidence interval]; +2.82 [1.49–4.14] mmol/kg wet weight; P < 0.001). Maximal incremental cycling capacity (VO2peak: +0.5 [−0.7 to 1.7] mL/kg/min; P = 0.384, Wpeak: +5 [−1 to 11] W; P = 0.103) and time to exhaustion on the constant work rate cycle test (+28 [−179 to 236] s; P = 0.782) did not change significantly. Compliance with supplement intake was similar in BA (median (quartile 1–quartile 3); 100 (98–100)%) and PL (98 (96–100)%) (P = 0.294) groups, and patients did not report side effects possibly related to supplement intake. No change was observed in secondary outcomes.ConclusionsBeta‐alanine supplementation is efficacious in augmenting muscle carnosine (+54% from mean baseline value) without side effects in patients with COPD in comparison with PL. However, accompanied beneficial changes in exercise capacity, quadriceps function, and muscle oxidative/carbonyl stress were not observed.

Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis

Accurate prediction of remaining useful life (RUL) and management for sodium-ion batteries have great significance, since they are promising for implementation as large-scale energy storage plants in renewable energy systems. In this paper, 18650 sodium-ion batteries are investigated. The observed data from the cycle life test has been used to examine the oxidation process and aging mechanisms based on incremental capacity analysis (ICA). Moreover, the Gaussian process regression (GPR) is established for accurate RUL prediction. The negative electrode half-cell with hard carbon, positive electrode half-cell with Na3·2V1·8Zn0·2(PO4)3, coin cells with Na3·2V1·8Zn0·2(PO4)3 vs hard carbon, and 18650 cells with Na3·2V1·8Zn0·2(PO4)3 vs hard carbon are analysed based on ICA. The oxidation process of vanadium (V3+→V4+) corresponding to the incremental capacity peak is selected to extract six potential health indicators. To reduce redundant information among various features, the principal component analysis is utilized to obtain the syncretic health indicator. The GPR is established for reliable prediction with a 95% confidence interval. When compared to the traditional methods, the proposed method can achieve higher accuracy in RUL prediction with a root mean square error below 1.16%.

Development of an Innovative Procedure for Lithium Plating Limitation and Characterization of 18650 Cycle Aged Cells for DCFC Automotive Applications

Since lithium-ion batteries seem to be the most eligible technology to store energy for e-mobility applications, it is fundamental to focus attention on kilometric ranges and charging times. The optimization of the charging step can provide the appropriate tradeoff between time saving and preserving cell performance over the life cycle. The implementation of new multistage constant current profiles and related performances after 1000 cycles are presented and compared with respect to a reference profile. A physicochemical (SEM, XRD, particle size analysis, etc.) and electrochemical (incremental capacity analysis, internal resistance measurements) characterization of the aged cells is shown and their possible implementation on board is discussed.