Online Battery Research Articles

Two sides of the same coin? Empirical examination of two proposed characterizations of acute suicidal crises: Suicide crisis syndrome and acute suicidal affective disturbance

Two proposed suicide-specific diagnoses, with accumulating research support, characterize the phenomenology of acute suicidal crises: Suicide Crisis Syndrome (SCS) and Acute Suicidal Affective Disturbance (ASAD). Despite conceptual overlap and some similar criteria, the two syndromes have never been compared empirically. The present study addressed this gap by examining SCS and ASAD utilizing a network analysis approach. A sample of 1568 community-based adults (87.6% cisgender women, 90.7% White, Mage = 25.60 years, SD = 6.59) in the United States completed an online battery of self-report measures. SCS and ASAD were first examined in individual network models, followed by a combined network to determine changes in network structure, as well as identify bridge symptoms that connected SCS and ASAD. The proposed criteria of SCS and ASAD formed sparse network structures that were largely unaffected by the influence of the other syndrome in a combined network. Social disconnection/withdrawal and manifestations of overarousal—particularly agitation, insomnia, and irritability—emerged as bridge symptoms that may connect SCS and ASAD. Our findings indicate the network structures of SCS and ASAD exhibit patterns of independence, alongside interdependence between overlapping symptom domains (i.e., social withdrawal, overarousal). Future work should examine SCS and ASAD prospectively to better understand their temporal dynamics and predictive utility in relation to imminent suicide risk.

Computerised cognitive assessment in patients with traumatic brain injury: an observational study of feasibility and sensitivity relative to established clinical scales.

Online technology could potentially revolutionise how patients are cognitively assessed and monitored. However, it remains unclear whether assessments conducted remotely can match established pen-and-paper neuropsychological tests in terms of sensitivity and specificity. This observational study aimed to optimise an online cognitive assessment for use in traumatic brain injury (TBI) clinics. The tertiary referral clinic in which this tool has been clinically implemented typically sees patients a minimum of 6 months post-injury in the chronic phase. Between March and August 2019, we conducted a cross-group, cross-device and factor analyses at the St. Mary's Hospital TBI clinic and major trauma wards at Imperial College NHS trust and St. George's Hospital in London (UK), to identify a battery of tasks that assess aspects of cognition affected by TBI. Between September 2019 and February 2020, we evaluated the online battery against standard face-to-face neuropsychological tests at the Imperial College London research centre. Canonical Correlation Analysis (CCA) determined the shared variance between the online battery and standard neuropsychological tests. Finally, between October 2020 and December 2021, the tests were integrated into a framework that automatically generates a results report where patients' performance is compared to a large normative dataset. We piloted this as a practical tool to be used under supervised and unsupervised conditions at the St. Mary's Hospital TBI clinic in London (UK). The online assessment discriminated processing-speed, visual-attention, working-memory, and executive-function deficits in TBI. CCA identified two significant modes indicating shared variance with standard neuropsychological tests (r=0.86, p<0.001 and r=0.81, p=0.02). Sensitivity to cognitive deficits after TBI was evident in the TBI clinic setting under supervised and unsupervised conditions (F (15,555)=3.99; p<0.001). Online cognitive assessment of TBI patients is feasible, sensitive, and efficient. When combined with normative sociodemographic models and autogenerated reports, it has the potential to transform cognitive assessment in the healthcare setting. This work was funded by a National Institute for Health Research (NIHR) Invention for Innovation (i4i) grant awarded to DJS and AH (II-LB-0715-20006).

Mindfulness and COVID-19-Related Stress: Staying Present During Uncertain Times.

The SARS-CoV-2 (COVID-19) pandemic is recognized as a mass traumatic event in which COVID-19-related stress (CS) can indicate other trauma- and/or stressor-related disorder. The facets of mindfulness (observing, describing, acting with awareness, nonjudging, andnonreacting) have been linked to reductions in stress-related symptoms and thus may protect against CS. We extended previous research by evaluating mindfulness facets as resilience skills negatively related to CS. Undergraduate students (n = 495) completed an online battery of questionnaires. A subsample of students endorsing clinically elevated CS (n = 165) was also evaluated. We utilized hierarchical regression to account statistically for the mindfulness facets in addition to indicators of psychological distress (e.g., negative affect, neuroticism, dissociation) and social desirability. We performed analyses twice, once in the overall sample, and once in the high CS subsample. Less observing and greater nonjudging related to reduced CS while other study variables were controlled for in the overall sample. In contrast, acting with awareness and nonjudging negatively related to CS in the subsample, but were not related to CS when we accounted for psychological-distress variables that positively related to CS in the analysis. Although variables indicative of psychological distress robustly contribute to CS, observing, acting with awareness, and nonjudging may be mindfulness skills that can be targeted to buffer clinically significant CS. This study was not pre-registered.

Online Parameter Identification for Lithium-Ion Batteries: An Adaptive Moving Window Size Design Methodology for Least Square Fitting

Online state-of-charge estimations are critical to lithium-ion batteries in electric vehicles for safe and reliable operations. To ensure accurate online state estimations, an online battery parameter identification algorithm, such as the least square fitting approach, along with a shifting moving window, was developed and implemented, while the decision of the moving window size for general purpose is a technical issue not yet been well-addressed. This paper proposes an adaptive moving window size design methodology that adjusts the moving window size considering three important factors: load profile, state-of-charge to open-circuit voltage profile, and condition of data. The simulation results show that the proposed methodology helps to identify the parameters of the battery electric circuit model with high accuracy, where the results are compatible to the results using fine-tuned fixed window sizes. Also, the proposed methodology is general to driving profiles from moderate to severe without extra algorithm redesign. In the three standard driving profile cases, the parameter identification error is reduced by 59.15%, 69.02%, and 75.14%, respectively, compared to the worst case in experiments, which are compatible with the results after fine-tuning the fixed window sizes. With accurately identified parameters, the modeling error is minimized, and three typical linear SOC estimation algorithms thus render accurate SOC estimation, rendering an average of 13.4%, 64.43% 57.48% reduction from the worst case in experiments, respectively.

Differential voltage analysis for battery manufacturing process control

Voltage-based battery metrics are ubiquitous and essential in battery manufacturing diagnostics. They enable electrochemical “fingerprinting” of batteries at the end of the manufacturing line and are naturally scalable, since voltage data is already collected as part of the formation process which is the last step in battery manufacturing. Yet, despite their prevalence, interpretations of voltage-based metrics are often ambiguous and require expert judgment. In this work, we present a method for collecting and analyzing full cell near-equilibrium voltage curves for end-of-line manufacturing process control. The method builds on existing literature on differential voltage analysis (DVA or dV/dQ) by expanding the method formalism through the lens of reproducibility, interpretability, and automation. Our model revisions introduce several new derived metrics relevant to manufacturing process control, including lithium consumed during formation and the practical negative-to-positive ratio, which complement standard metrics such as positive and negative electrode capacities. To facilitate method reproducibility, we reformulate the model to account for the “inaccessible lithium problem” which quantifies the numerical differences between modeled versus true values for electrode capacities and stoichiometries. We finally outline key data collection considerations, including C-rate and charging direction for both full cell and half cell datasets, which may impact method reproducibility. This work highlights the opportunities for leveraging voltage-based electrochemical metrics for online battery manufacturing process control.

Recursive ARMAX-Based Global Battery SOC Estimation Model Design using Kalman Filter with Optimized Parameters by Radial Movement Optimization Method

Precise estimating battery state-of-charge (SOC) is an important factor in determining vehicle range in an electric or hybrid vehicle. However, the parameters of battery are highly dependent on environmental conditions such as temperature. One of the main drawbacks in battery SOC estimation methods by using existing approaches is inability to adapt to the variable environmental operating conditions. In this study, a hybrid technique which combines the Kalman filter and recursive autoregressive exogeneous moving average model is proposed for the online battery parameters estimation. To design robust SOC estimation to varying battery parameters, the parameters of Kalman filter are optimized with the radial movement optimization metaheuristic algorithm. The proposed approach is implemented considering both different temperatures (0 °C, 25 °C, and 40 °C) and driving test cycles, UDDS, LA-92, and US06. Second-order RC battery equivalent model-based approach are compared with the proposed method, and the result shows that the proposed method is better than the conventional method in the aspects of average statistical parameters for all the driving test cycles.

A novel state-of-health estimation method for fast charging lithium-ion batteries based on an adversarial encoder network

Deep learning based on big data has been widely implemented with cloud and edge computing for lithium-ion battery online health diagnostics, where enhancing the diagnostics' accuracy, robustness, and real-time application are current research issues. Most research has concentrated on state-of-health estimation methods for diagnostics, but the difference in the degradation feature trajectories of batteries between the training and testing domains has been neglected, which further affects the estimation precision of the trained model. To overcome this issue, a strategy based on adversarial learning and feature selection is proposed. First, the adversarial encoder approach creates a lower-dimensional feature vector to the aggregated domain between the source and target batteries. Then, the feature vector is sent into the estimation module, which consists of a one-dimensional convolutional neural network (1DCNN) to accurately estimate the battery lifespan. The results on the 1C and 6C experimental datasets demonstrate that the adversarial feature selection strategy significantly enhances the estimation performance of the deep learning-based estimation method. Combining an adversarial autoencoder with the prediction module yields statistically superior battery lifetime estimation results.

End-Cloud Collaboration Approach for State-of-Charge Estimation in Lithium Batteries Using CNN-LSTM and UKF

The accurate estimation of the state of charge (SOC) plays a crucial role in ensuring the range of electric vehicles (EVs) and the reliability of the EVs battery. However, due to the dynamic working conditions in the implementation of EVs and the limitation of the onboard BMS computational force, it is challenging to achieve a reliable, high-accuracy and real-time online battery SOC estimation under diverse working scenarios. Therefore, this study proposes an end-cloud collaboration approach of lithium-ion batteries online estimate SOC. On the cloud-side, a deep learning model constructed based on CNN-LSTM is deployed, and on the end-side, the coulomb counting method and Kalman’s filter are deployed. The estimation results at both sides are fused through the Kalman filtering algorithm, realizing high-accuracy and real-time online estimation of SOC. The proposed approach is evaluated with three dynamic driving profiles and the results demonstrate the proposed approach has high accuracy under different temperatures and initial errors, where the root means square error (RMSE) is lower than 1.5% and the maximum error is lower than 5%. Furthermore, this method could achieve high-accuracy and real-time SOC online estimation under the cyber hierarchy and interactional network (CHAIN) framework and can be extended to multi-state collaborative online estimation.

A Network Analysis Approach on the Psychological Flexibility/Inflexibility Model

Acceptance and Commitment Therapy (ACT) is purported to work via targeting six interrelated processes summarized as the Psychological Inflexibility/Psychological Flexibility (PI/PF) model. However, the theoretical structure and interconnections of this model have not been sufficiently explored. Lacking are examinations of the interrelations among its components. Network Analysis (NA) can model PI/PF as a system of interconnected variables. We aimed at exploring the role and associations of the PI/PF model’s components using NA in two different samples and sets of scales, and compare its structure across sub-samples. Sample 1 consisted of 501 individuals, who completed an online battery of questionnaires including the Multidimensional Psychological Flexibility Inventory, and Sample 2 consisted of 428 people, who completed an online set of six ACT measures, each assessing a component of the PI/PF model. NA could not verify the six ACT dimensions as distinct components. Values and Committed Action components were found to be strongly associated and combined in a group in both sets of measures and samples. Interestingly, Acceptance and Defusion were not the most central components as purported in some ACT conceptualizations, whereas Self-as-Context had a key role on both sets of measures and its items were often merged with Present Moment Awareness items. No significant differences were found in the comparison of networks across different subsamples and sets of scales. After combining different sets of scales, the six ACT components could not be completely verified as distinct entities, which might reflect problems with the theoretical model, or with the scales used. All components had critical roles in the model, particularly Self-as-Context, which reflects the need to redirect research towards this understudied construct. Findings point towards considerations of a triflex instead of a hexaflex ACT model.

Factor structure of the modified Rivermead Post-concussion Symptoms Questionnaire (mRPQ): an exploratory analysis with healthy adult simulators

ABSTRACT Objective Persistent postconcussion symptoms (PPCS) are challenging to diagnose. An improved diagnostic process could consider typical and atypical postconcussion symptoms. This study examined the structure of a modified Rivermead Post-concussion Symptoms Questionnaire (mRPQ) with both symptom types. Method 298 adult volunteers were randomized into groups: honest responders, mild traumatic brain injury (mTBI) simulators (MS), and biased mTBI simulators (BMS). Both mTBI simulating groups were coached about mTBI and primed about the simulation context (compensation evaluation). The BMS group was also encouraged to bias (exaggerate) symptoms. The participants completed an online battery of tests, including the mRPQ. Results An exploratory factor analysis of the mRPQ (full sample) revealed a three-factor solution, including a separate dimension for atypical symptoms (all item loadings >0.45, ~4% of explained variance). The overall and group analyses of the standard RPQ items (typical symptoms) found a one- or two-factor solution, as did the analyses of atypical symptoms. Conclusions Consistent with prior RPQ research, a unidimensional or bifactor structure was measurable from standard RPQ symptoms. Whilst this study did not find support for domain-level symptom scores for either typical or atypical symptoms, the findings support the use of an overall atypical symptoms score.

Lyapunov Optimization in Online Battery Energy Storage System Control for Commercial Buildings

The high instantaneous discharging capability of battery energy storage systems (BESSs) make them ideal candidates for reducing peak loads in commercial buildings. An efficient online BESS control algorithm can be beneficial for reducing the monthly electricity bill of individual commercial buildings. Conventional model-based BESS control algorithms rely heavily on accurate long-horizon net load forecast, which is difficult to obtain. To address this problem, we develop a Lyapunov optimization-based online BESS control algorithm and derive its theoretical performance bound. Comprehensive numerical study results using real world commercial building smart meter data in southern California show that our proposed Lyapunov optimization-based online control algorithm with time-varying weighting parameters yields higher savings in electricity cost and requires less computation time in comparison to the state-of-the-art baseline algorithms.

Online Monitoring of Lithium-Ion Battery Internal Temperature Using PWM Switching Oscillations

Online monitoring of Lithium-ion (Li-ion) battery internal temperature by electrochemical impedance spectrum (EIS) is important for the system safe and reliable operation. However, it is challenging in the high temperature region due to the limited thermal sensitivity. Moreover, additional signal injection or disturbance for online EIS measurement may interact with the operating system. To address these challenges, a Li-ion battery internal temperature continuous monitoring method using the self-excited ultrahigh frequency (UHF) PWM switching oscillations (e.g., several hundred kHz) is proposed in this article. First, the UHF EIS characteristics of a Li-ion battery are explored to find a novel thermal sensitive electric parameter applicable for high temperature measurement. Then, an PWM switching oscillation based online battery temperature monitoring scheme is presented, including the UHF oscillation sensor, resonance impedance extraction algorithm, and battery internal temperature estimation procedure. Finally, experimental work was carried out, and the results demonstrate an excellent performance, such as high sensitivity over 90 °C, ms-level fast response, and noncontract continuous monitoring.

A Bayesian optimization hyperband-optimized incremental deep belief network for online battery behaviour modelling for a satellite simulator

Simulation tools play crucial roles in the stable implementation of space missions during satellite operations; they are typically utilized for satellite behaviour monitoring by comparing obtained telemetry values and predicted values according to pretrained prediction models. However, as telemetry data streams arrive in a chunk-by-chunk manner, a common practice is to retrain the employed simulation tool based on the newly arrived data, which results in the consumption of many computing resources and time lags. Therefore, an incremental learning algorithm is required to achieve accurate and fast satellite behaviour prediction. This paper proposes a Bayesian optimization hyperband-optimized incremental learning-based deep belief network (BOHB-ILDBN) to reproduce battery voltage behaviours, where the BOHB algorithm is utilized to obtain a group of optimal hyperparameter configurations to initialize a DBN model, the DBN model is incrementally updated by a fine-tuning process, and the variance difference between the actual and forecasted values is taken as the criterion for determining the completion of model training. Finally, the effectiveness and robustness of the model are verified on telemetry data obtained from an on-orbit sun-synchronous remote sensing satellite, the China–Brazil Earth Resources Satellite (CBERS-4A).

Reliability, Validity, and User-Experience of Remote Unsupervised Computerized Neuropsychological Assessments in Community-Living 55- to 75-Year-Olds.

Self-administered computerized neuropsychological assessments (CNAs) provide lower cost, more accessible alternatives to traditional in-person assessments but lack critical information on psychometrics and subjective experience of older adults in remote testing environments. We used an online brief battery of computerized tasks selected from the Cogstate Brief Battery (CBB) and Cambridge Brain Sciences (CBS) to 1) determine test-retest reliability in an unsupervised setting; 2) examine convergent validity with a comprehensive 'gold standard' paper-and-pencil neuropsychological test battery administered in-person; and 3) explore user-experience of remote computerized testing and individual tests. Fifty-two participants (mean age 65.8±5.7 years) completed CBB and CBS tests on their own computer, unsupervised from home, on three occasions, and visited a research center for an in-person paper-and-pencil assessment. They also completed a user-experience questionnaire. Test-retest reliabilities varied for individual measures (ICCs = 0.20 to 0.83). Global cognition composites showed excellent reliability (ICCs > 0.8 over 1-month follow-up). A strong relationship between a combination of CNA measures and paper-and-pencil battery was found (canonical correlation R = 0.87, p = 0.04). Most tests were rated as enjoyable with easy-to-understand instructions. Ratings of general experience with online testing were mostly favorable; few had difficulty concentrating (17%) or using the computer for tasks (10%), although over one-third experienced performance anxiety (38%). A combined brief online battery selected from two CNAs demonstrated robust psychometric standards for reliability (global composite), and convergent validity with a gold standard battery, and mostly good usability and acceptability in the remote testing environment.

Underscoring the F in FoMO: How does anxiety sensitivity contribute to fear of missing out?

ABSTRACT Fear of missing out (FoMO) is a prevalent phenomenon associated with a range of mental health symptoms, such as depression and anxiety. To our knowledge, the question of whether FoMO can be explained by other well-known mechanistic variables—namely, loneliness, rumination, and anxiety sensitivity (AS) – has not been previously evaluated. The current study investigated the predictive power of loneliness, rumination, and AS for explaining variance in FoMO within two independent samples of undergraduate students at a large Northeastern university. Participants completed an online battery of questionnaires. In Study 1, it was found that loneliness and rumination offered significant prediction of FoMO when AS was not considered in the model; however, when these three predictors were considered together, only AS offered significant, non-redundant prediction. Study 2 revealed that both rumination and AS offered significant prediction of FoMO, with AS offering stronger unique prediction. Such findings provide a new frame for understanding the nature of the relatively new concept of FoMO, and in particular, suggest that it may be important to consider AS and rumination in future studies.

Online Model Parameter and State of Charge Estimation of Li-Ion Battery Using Unscented Kalman Filter Considering Effects of Temperatures and C-Rates

This paper proposed a technique for online modeling and state of charge (SoC) estimation of Li-ion batteries considering the effects of operating temperatures and C-rates. The proposed battery model, which is updated in real-time, is incorporated with the unscented Kalman filter (UKF) for accurate and online SoC estimation. In the proposed technique, Thevenin-based equivalent circuit model of Li-ion battery is used. Each of the RC networks of the Thevenin model is represented as a first-order linear time-invariant (LTI) system. The offline RC parameters are extracted as the parameters of the first-order LTI system using the final value theorem. Then the offline RC parameters are converted to the online model parameters compensating the effects of operating temperatures and C-rates. Finally, the UKF technique is augmented with the proposed online battery model for higher accurate and online SoC estimation. An experimental setup has been developed in the LabVIEW platform to validate the proposed battery model and apply it for online SoC estimation. The proposed online battery model and UKF are developed in Matlab and then integrated with the LabVIEW program using the Matlab-LabVIEW interface called Math Script. The proposed technique has been rigorously validated in the laboratory under hybrid pulse power characterization (HPPC) and federal urban driving schedule (FUDS) tests under wide range of varying operating temperatures and C-rates. In the HPPC test, the SoC estimated using the proposed technique has a maximum absolute relative percentage error (ARPE) of less than 2%, whereas the battery model that does not account for temperature and C-rate effects has a maximum ARPE of 8.79 percent, which is 4.30 times higher than the proposed technique. The proposed approach yielded mean ARPE of 0.30%, 1.02%, and 0.81% in the FUDS test at 25 °C, 0 °C, and 40 °C, respectively, which is performed in dynamic identical C-rates. Furthermore, higher accuracy and effectiveness of the proposed technique have been validated under the presence of white and coloured measurement noises.

Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies

The online battery management system (BMS) is very critical for the safe and reliable operation of electric vehicles (EVs) and renewable energy storage applications. The primary responsibility of BMS is data assembly, state monitoring, state management, state safety, charging control, thermal management, and information management. The algorithm and control development for smooth and cost-effective functioning of online BMS is challenging research. The complexity, stability, cost, robustness, computational cost, and accuracy of BMS for Li-ion batteries (LiBs) can be enhanced through the development of algorithms. The model-based and non-model-based data-driven methods are the most suitable for developing algorithms and control for online BMS than other methods present in the literatures. The performance analysis of algorithms under different current, thermal, and load conditions have been investigated. The objective of this review is to advance the experimental design and control for online BMS. The comprehensive overview of present techniques, core issues, technical challenges, emerging trends, and future research opportunities for next-generation BMS is covered in this paper with experimental and simulation analysis.

Optimal online battery power control of grid-connected energy-stored quasi-impedance source inverter with PV system

This study presents an optimal online control that implements a biogeography-based optimization (BBO) algorithm on a battery energy system (BES) integrated into an energy-stored quasi-impedance source inverter (qZSI) that connects a photovoltaic (PV) power plant to the grid. The BBO algorithm was used to tune the PI regulator in the BES current control loop by minimizing the integral time absolute error (ITAE). Two different options for the BBO are compared in this application:1) a PI controller with online self-tuning based on BBO, and 2) a PI controller with offline tuning using BBO. Moreover, the BBO-based PI controllers were compared with a third controller tuned online using the particle swarm optimization (PSO) algorithm. To evaluate and compare the controllers, a PV power plant with a battery energy-stored qZSI was simulated under different operating conditions, such as step changes in the BES current reference, different sun irradiance, and a grid voltage sag. The results demonstrate better control of the BES current with the online tuning techniques (BBO and PSO) than with the offline tuning procedure, and similar results between the two online tuning algorithms. Nevertheless, throughout the simulation, the time of use of the BBO algorithm was almost 2.5 times smaller than the PSO algorithm. Therefore, the online BBO-based PI controller is considered the most suitable option.

Fault detection of a Li-ion battery using SVM based machine learning and unscented Kalman filter

The focal aim of the current paper is to present a unique support vector machine (SVM) based fault diagnosis technique for a Lithium ion battery. This paper basically objectifies the derivative free estimator paradigm for the assessment of State of Charge (SOC) for a Lithium-ion (Li-ion) battery. The estimation error has been taken as the references for support vector machine (SVM) based learning for fault detection. The model of Li-ion battery which is instrumented here is basically the lumped model. One of the primary drawbacks of this type of battery is the estimation of the state of charge (SOC) perfectly because of the electrochemical topographies. These features alters with alteration of physical constraints which causes the increase of process and measurement noise levels while updating the online battery model. These drawbacks also create some fault condition in battery management system. The short circuit current due to abrupt change in state of charge cause short circuit fault which is not desirable. Unscented Kalman Filter obviates the noise level by minimizing estimation error and fault can be diagnosed accurately using machine learning with great accuracy and less number of false alarms. In one word the main novelty of this paper is to detect fault of state of charge of Li-ion battery using SVM based machine learning where the vector machine is trained by the error value deduced by the UKF. It is proven from the simulation studies that machine learning can detect fault faster with higher range coverage than other statistical methods.

Open-circuit voltage curve reconstruction for degrading lithium-ion batteries utilizing discrete curve fragments from an online dataset

A complete open-circuit voltage (OCV) curve plotted against the state of charge (SOC) for degrading batteries, as a core indicator for battery state estimation and health diagnostics, is very important for whole-life management of battery. Unfortunately, such a curve is almost impossible to obtain in online battery management systems. Due to the uncontrollable OCV sample opportunities, only a series of isolated curve fragments consisting of scarce and discrete OCV-SOC points can be collected. Due to the unavoidable SOC estimation error, the relative position between fragments is uncertain. In order to reconstruct a complete OCV-SOC curve utilizing these isolated OCV curve fragments, an online and training-free curve reconstruction method is developed in this paper. Using this method, all isolated fragments from an online dataset are adaptively rearranged and uniquely located based on the positional interlock between different fragments, and fragments with abnormal state of health (SOH) or measurement errors are screened out. The test results demonstrated that the reconstruction method has a good stability, rapidity, and accuracy. The root mean square error of the curve reconstruction is well controlled within 5 mV throughout the battery's entire lifetime.