State Of Health Assessment Research Articles

Evaluating Frequency Domain Reflectometry as a Tool for Lithium-Ion Battery Health Prognosis

Monitoring battery aging is crucial for maintaining reliability and performance. This study investigates Frequency Domain Reflectometry (FDR) as a tool for monitoring lithium-ion battery State-of-Health (SoH). While FDR has been applied in battery research, the existing literature fails to address SoH assessment and lacks studies on larger battery samples to provide more meaningful results. In this work, nineteen cells initially underwent Electrochemical Impedance Spectroscopy (EIS) to assess their degradation levels during cyclic aging. This work evaluates FDR’s effectiveness in monitoring battery health indicators, such as capacity and equivalent series resistance (ESR), by correlating these with FDR-measured impedance between 300 kHz and 1 GHz. Analytical comparison between impedance measured before and after de-embedding processes were presented. The results show FDR reactance within 300 kHz–40 MHz correlates with EIS-measured ESR, suggesting its potential as a SoH indicator. However, reduced sensitivity and accuracy, particularly after de-embedding, may limit practical applicability. Additionally, resonance-based analysis was conducted to explore the relationship between changes in circuit resonance and cell dielectric permittivity. Despite having the lowest sensitivity, the method showed that the resonance frequencies of cells remain relatively constant, mirroring behaviours associated with changes in resistive properties. Overall, this study provides insights into FDR’s potential for battery diagnostics while highlighting avenues for future research to enhance effectiveness in real-world scenarios.

A hybrid multilayerperceptron-extremegradientboost approach for precise state of charge and state of health assessment

Lithium ion batteries are popularly used in various energy storage systems. Accurate battery's State of Charge (SoC) and State of Health (SoH) estimate is essential for energy storage system's safety. This manuscript proposes a hybrid approach for estimating SoC and SoH of lithium ion battery. The proposed hybrid technique is the joint execution of both the Multi-Layer Perceptron (MLP) with Extreme Gradient boosting (XGBoost) algorithm. Hence, it is named as MLP-XG Boost Algorithm. The major objective of the proposed system is to improve the safety of energy storage systems and to achieve the highly accurate SoH. The MLP algorithm is utilized to analyze and trained the battery data. The XG Boosting algorithm is utilized to preprocessed and estimate the SoC and SoH of a lithium-ion battery. The proposed method's effectiveness is assessed using the MATLAB platform and contrasted with other methods that are currently in use. In every system now in use, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), the proposed approach produces better results. From the result it is concluded that the proposed method based Mean Square Error and Mean Absolute Error Root is less than 1 at various temperatures such as 0∘C, 25∘Cand 45∘C and it is better and efficient.

A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator

Accurately assessing battery state of health (SOH) is crucial for ensuring the safety of lithium-ion batteries. However, current SOH evaluation methods suffer from inconsistent criteria and limited accuracy in prediction models. This paper introduces a novel SOH prediction and assessment strategy that relies on multiple indicators to address these challenges. First, multifaceted health factors are extracted based on charge cycle data, including battery charging time, incremental capacity, and d V /d t curve. Subsequently, a support vector regression model optimized by the sparrow search algorithm is proposed to predict SOH. The results show that MAE, RMSE, and MAPE are less than 0.037%, 0.047%, and 0.04%, respectively. Meanwhile, the Kalman filtering method is used to identify the second-order RC model online, and the relative SOH curves are obtained by defining the SOH through the internal resistance. Finally, by analyzing the effects of capacity and internal resistance changes on SOH, a new strategy for SOH assessment is proposed, which considers various factors and selects an appropriate judgment mechanism according to the characteristics exhibited by the battery at different life stages. The strategy is more conservative and reliable, providing a solid guarantee for the safe operation of mining equipment.

Physics-Informed Design of Hybrid Pulse Power Characterization Tests for Rechargeable Batteries

Industry-standard diagnostic methods for rechargeable batteries, such as hybrid pulse power characterization (HPPC) tests for hybrid electric vehicles, provide some indications of state of health (SoH), but lack a physical basis to guide protocol design and identify degradation mechanisms. We develop a physics-based theoretical framework for HPPC tests, which are able to accurately determine specific mechanisms for battery degradation in porous electrode simulations. We show that voltage pulses are generally preferable to current pulses, since voltage-resolved linearization more rapidly quantifies degradation without sacrificing accuracy or allowing significant state changes during the measurement. In addition, asymmetric amounts of information gain between charge /discharge pulses are found from differences in electrode kinetic scales. We demonstrate our approach of physics-informed HPPC on simulated Li-ion batteries with nickel-rich cathodes and graphite anodes. Multivariable optimization by physics-informed HPPC rapidly determines kinetic parameters that correlate with degradation phenomena at the anode, such as solid-electrolyte interphase (SEI) growth and lithium plating, as well as at the cathode, such as oxidation-induced cation disorder. If validated experimentally, standardized voltage protocols for HPPC tests could play a pivotal role in expediting battery SoH assessment and accelerating materials design by providing new electrochemical features for interpretable machine learning of battery degradation.

A Novel Feature Engineering-Based SOH Estimation Method for Lithium-Ion Battery with Downgraded Laboratory Data

Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery’s aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%.

Hybrid Random Forest Regression and Artificial Neural Networks for Modelling and Monitoring the State of Health of Li-Ion Battery

Lithium-ion batteries play a significant role in modern energy storage systems for a number of applications, including renewable energy installations, electric cars, and portable devices. In order to guarantee the long-term dependability and safety of these battery packs, comprehensive modelling and monitoring approaches are required for accurate State of Health (SoH) evaluation. This research suggests an innovative strategy that combines the strengths of Random Forest Regression (RFR) and Artificial Neural Networks (ANN) for reliable Li-ion battery SoH modelling and monitoring. The research starts with the collection of thorough Li-ion battery data, which includes voltage, current, temperature, and cycling characteristics. The battery dataset's nonlinear relationships are captured utilising RFR as the main modelling method. In terms of feature selection and prediction precision, RFR's ensemble of decision trees shines, making it a strong contender for modelling complex battery behaviour. The SoH prediction is then enhanced by the use of ANN, a deep learning framework renowned for its ability to extract detailed patterns from data. The characteristics of RFR are complemented by ANN's capacity to discover hidden features and nonlinear correlations, improving the overall prediction performance. The suggested hybrid approach, that combines RFR and ANN, is trained and verified utilising a diverse database acquired from Li-ion battery sources under operational circumstances. To assess the prediction accuracy of a suggested framework measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values are employed. The outcomes show that the hybrid RFR-ANN model works better than the individual RFR and ANN models, producing better SoH predictions. A viable solution for real-time Li-ion battery health monitoring is provided by this combination of conventional ML and deep learning approaches, which demonstrates the synergy among complexity and interpretability. This research advances battery SoH assessment and paves the way for the practical incorporation of precise monitoring systems into applications like electric vehicles, where timely and accurate SoH data is essential for maximising battery performance and extending operational lifespan.

State of health (SOH) assessment for LIBs based on characteristic electrochemical impedance

With the continuous development of high energy density cell technology, the electric transport industry proposed higher requirements for the accuracy and real-time performance of automotive lithium batteries (LIBs) health status assessment. A state of health (SOH) assessment is suggested based on electrochemical impedance at characteristic frequency in the study. The distribution of relaxation time is used to deconstruct the internal electrochemical reaction of the battery and the impedance value of electrochemical process is quantified as a feature to avoid the effect of state of charge (SOC) in the evaluation. The imaginary part of impedance feature is applied to characterize the internal temperature of the lithium battery based on the analysis of impedance differences. Thus, the correlation between the state of health and impedance characteristics is examined to obtain the SOH assessment model of lithium battery in the full SOC and wide temperature spectrum. In conclusion, the assessment demonstrates an exceptional goodness-of-fit, with an accuracy of 0.99 in evaluating the SOH of LIBs and internal temperature, thereby indicating the robustness and reliability of the approach.

Assessing the Impact of First-Life Lithium-Ion Battery Degradation on Second-Life Performance

The driving and charging behaviours of Electric Vehicle (EV) users exhibit considerable variation, which substantially impacts the battery degradation rate and its root causes. EV battery packs undergo second-life application after first-life retirement, with SoH measurements taken before redeployment. However, the impact of the root cause of degradation on second-life performance remains unknown. Hence, the question remains whether it is necessary to have more than a simple measure of state of health (SoH) before redeployment. This article presents experimental data to investigate this. As part of the experiment, a group of cells at around 80% SoH, representing retired EV batteries, were cycled using a representative second-life duty cycle. Cells with a similar root cause of degradation in the first life (100–80% SoH) exhibited the same degradation rate in second life after being cycled with the same duty cycle during the second life. When the root cause of degradation in the first life is different, the degradation rate in the second life may not be the same. These findings suggest that the root cause of a cell’s first-life degradation impacts how it degrades in its second life. Postmortem analysis (photographic and SEM images) reveals the similar physical condition of negative electrodes which have similar degradation rates in their second life cycle. This demonstrates that cells with a similar first life SoH and root cause of degradation indeed experience a similar life during their second life. The experimental results, along with the subsequent postmortem analysis, suggest that relying solely on SoH assessment is insufficient. It is crucial to take into account the root causes of cell degradation before redeployment.

A novel health indicator by dominant invariant subspace on Grassmann manifold for state of health assessment of lithium-ion battery

The precise estimation of the state of health (SoH) in Lithium-ion batteries (LiBs) relies heavily on a reliable health indicator (HI). Conventional indicators are often constructed by directly concatenating features from multiple sources. It overlooks significant non-linear and correlative information inherent in raw signals. To address this limitation, this paper introduces an innovative approach for SoH estimation in LiBs. Deep features extracted from signals of various sensors are obtained using denoising auto-encoders (DAEs). Then the dominant invariant subspaces (DIS) are calculated through the non-linear transformation of multi-source features on the Grassmann manifold. It can preserve essential and robust characteristics. The health indicator quantifies the geodesic distance of DIS using a projection metric. It provides a more comprehensive inclusion of nonlinear and correlation information. Consequently, this indicator offers heightened precision in discerning differences in health states. Validation of the proposed method is conducted using the NASA dataset. The result demonstrates its effectiveness on the SoH assessment and superiority to the state-of-the-art method.

Li-Ion Battery State of Health Estimation Based on Short Random Charging Segment and Improved Long Short-Term Memory

Lithium-ion batteries have been used in a wide range of applications, including electrochemical energy storage and electrical transportation. In order to ensure safe and stable battery operation, the State of Health (SOH) needs to be accurately estimated. In recent years, model-based and data-driven methods have been widely used for SOH estimation, but due to the uncertainty of battery charging conditions in practice, it is difficult to obtain a fixed local segment. In this paper, the charging curve is first divided into several equal voltage difference segments based on charging segment voltage difference ΔV in order to solve the random charging segment problem. Time interval of equal charge voltage difference of the voltage curve, coefficient of variation and euclidean distance of the charging capacity difference curve are extracted as health features. The improved flow direction algorithmlong short term memory-based SOH assessment method is proposed and verified by the Oxford battery degradation dataset and experimental battery degradation dataset with a maximum error of 0.6%.

Estimating the state of health of lithium-ion batteries based on a probability density function

Accurate estimation of the state of health (SOH) of lithium-ion batteries is important to ensure safe operation. Although SOH models that extract health features (HFs) from incremental capacity (IC) curves have proven to be effective methods of evaluating battery SOH, the use of smooth IC curves that rely heavily on complicated algorithms reduces the reliability of SOH assessment to a certain extent. In this paper, a probability density function (PDF) method is integrated with Gaussian process regression (GPR) and used to build highly optimised SOH evaluation models for three different types of batteries. The PDF peak position and regional charging time are extracted from charging voltage data in the form of HFs using the PDF method. The proposed SOH estimation model shows good performance when these two HFs are both adopted. Our SOH estimation models for both cells and modules show good robustness for LiCoO2 (LCO), LiNi0.8Co0.15Al0.05O2 (NCA) and lithium iron phosphate (LFP) batteries.

Accurate and Efficient SOH Estimation for Retired Batteries

There will be an increasing number of retired batteries in the foreseeable future. Retired batteries can reduce pollution and be used to construct a battery cycle ecosystem. To use retired batteries more efficiently, it is critical to be able to determine their State of Health (SOH) precisely and speedily. SOH can be estimated accurately through a comprehensive and inefficient charge-and-discharge procedure. However, the comprehensive charge and discharge is a time-consuming process and will make the SOH assessment for many retired batteries unrealistic. This paper proposes an accurate and efficient SOH Estimation (SOH-E) method using the actual data of retired batteries. A battery data acquisition system is designed to acquire retired batteries’ comprehensive discharge and charge data. The acquired discharge data are separated into various time interval-segregated sub-data. Then, the specially designed features for SOH-E are extracted from the sub-data. Neural Networks (NNs) are trained using these sub-data. The retired batteries’ SOH levels are then estimated after the NNs’ training. The experiments described herein use retired lead–acid batteries. The batteries’ rated voltage and capacity are 12 V and 90 Ah, respectively. Different feature value extractions and time intervals that might affect the SOH-E accuracy and are tested. The Backpropagation NN (BPNN) and Long-Short-Term-Memory NN (LSTMNN) are designed to estimate SOH in this paper. The experimental results indicate that SOH can be calculated in 30 min. The Root-Mean-Square Errors (RMSEs) are less than 3%. The proposed SOH-E can help decrease pollution, extend the life cycle of a retired battery, and establish a battery cycle ecosystem.

State-of-health estimation of retired lithium-ion battery module aged at 1C-rate

This research provides a novel estimation model for the state of health (SOH) of retired battery module at 1C-rate with the sampling frequency of 1/60 Hz. The retired 15P4S battery module from Chery S18B electric vehicle is aging at 1C-rate in the range of 0% - 100% SOC with the sampling frequency of 1/60 Hz until the SOH reduces to less than 60%. The regional capacity analysis (RCA) and differential voltage analysis (DVA) are employed for the modeling and validation of module SOH evaluation. The results show that the regional capacity from RCA is an effective SOH evaluation index for battery module. The terminal slope from DVA as a novel and effective indicator is linearly negatively correlated with module SOH. There are good linear negative correlations between ST and SOH. The R2 value of ST - SOH model during discharge is greater than that during charge. The maximum relative errors of evaluated SOH from the ST - SOH models are both smaller than those from the Cˆ - SOH models in the validation group whether charging or discharging. Among them, the ST during discharging is the most effective health factor for module SOH assessment.

State-of-Health of Li-Ion Battery Estimation Based on the Efficiency of the Charge Transfer Extracted from Impedance Spectra

Several studies show that impedance spectroscopy is a suitable method for online battery diagnosis and State-of-Health (SoH) estimation. However, the most common method is to model the acquired impedance spectrum with equivalent circuits and focus on the most sensitive parameters, namely the charge-transfer resistance. This paper introduces first a detailed model of a battery cell, which is then simplified and adapted to the observable spectrum behavior. Based on the physical meaning of the model parameters, we propose a novel approach for SoH assessment combining parameters of the impedance spectrum by building the ratio of the solid electrolyte interphase (SEI) resistance to the total resistance of SEI and the charge transfer. This ratio characterizes the charge-transfer efficiency at the electrodes’ surfaces and should decrease systematically with SoH. Four different cells of the same type were cycled 400 times for the method validation, and impedance spectroscopy was performed at every 50th cycle. The results show a systematic correlation between the proposed ratio and the number of cycles on individual cell parameters, which build the basis of a novel online method of SoH assessment.

State-of-Health Assessment for Aero-Engine Based on Density-Distance Clustering and Fuzzy Bayesian Risk

State-of-health (SOH) assessment for aero-engine can effectively reduce maintenance cost and operational risk, and is also a significant part of the prognostics and health management (PHM) system. However, the current SOH assessment is usually closely coordinated with other parts of PHM to achieve specific functions. This is not conducive to generalizing the function of SOH assessment. Therefore, this paper proposes a data-driven framework of SOH assessment that mainly includes data preprocessing, pseudo label generation, weight assignment and feature selection, and assessment, which enhances the systematicness of SOH assessment. A combination model based on density-distance clustering and fuzzy Bayesian risk models is designed to generate a pseudo label, select optimal parameter subset, and assign weight. Then, two assessment indicators including state membership degree and health degree are produced based on two fuzzy models for horizontal and vertical comparisons. These two indicators expand the dimensions and perspectives of SOH measurement, which can more comprehensively characterize the health state of the engine. Finally, the correctness and effectiveness of the proposed methodology are verified by the widely used Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset.

State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking

Electrified vehicles users may expect their vehicle to have a steady autonomy range and available power throughout the lifetime of their cars. The health assessment of Lithium-ion batteries (LIBs), in that regard, represents a critical point for performance evaluation and lifetime prediction. Reliable state-of-health (SoH) assessment is essential to ensure cautious and suitable use of LIBs. To that end, several embedded solutions are proposed in the literature. In this paper, two new aging indicators are developed to enrich the existing diagnosis-based (DB-SoH) solutions. These indicators are based on collected data during charging (CDB-SoH) and driving (DDB-SoH) events overtime. The data are comprised of variables such as distance, speed, temperature, charging power, and more. Both solutions produce reliable state-of-health SoH assessment with a significantly good estimation error. Additionally, a data-driven battery aging prediction using the random forest (RF) algorithm is introduced using actual users’ behavior and ambient conditions. The proposed solution produced an SoH estimation error of 1.27%. Finally, a method for aging factors ranking is proposed. The obtained order is consistent with known aging root causes in the literature and can be used to mitigate fast LIB aging for electrified vehicle applications.

Optimisation of hybrid energy systems for maritime vessels

The decarbonisation agenda in maritime transport requires that asset owners and operators adopt greener technologies within their existing and new vessels. The primary drivers within this agenda relate to improved environmental metrics, efficient energy performance, and improved asset management. However, the integration of new technologies always presents technical and financial risks. Here, utilising energy and environmental monitoring from real vessels, the authors propose an energy system optimisation architecture, hybrid fusion energy management system (HyFES), that optimises the key performance indicators of energy performance, reduction of diesel engine nitrogen oxide (NOx), and particulate matter (PM), and prognostic state of health assessment of energy storage technologies. Using state of the art machine-learning techniques, the authors are able to determine the on-board lithium-ion and lead acid batteries' state of health with accuracy > 8 and 4%, respectively. Dependent on the mode of operation, optimisation of energy performance indicates fuel saving of between 70 and 80% for the vessel operator. Future research will focus on the integration of more assets into the optimisation architecture and increased vessel journey use cases.

Fractional-order model identification for state of health assessment of solid-oxide fuel cells

Solid-oxide fuel cells (SOFCs) represent a group of electrochemical conversion devices that utilise hydrogen rich fuels and are characterised by their high efficiency of energy conversion. For optimal exploitation of SOFCs, accurate and online state-of-health (SoH) assessment is of utmost importance. SoH assessment is usually performed through the frequency domain analysis by characterising the changes of the Nyqvist curves, a process also known as electrochemical impedance spectroscopy. Such an approach is time consuming and suffers from low accuracy particularly at low frequencies. Methodologies for time-domain identification of fractional-order systems offer a way of resolving these issues. Using an algebraic approach to identification, the complete fractional-order transfer function is identified from a series of several step responses. The SoH can be estimated from the identified parameters of the fractional-order transfer function either in a form of a Nyqvist curve or by directly linking particular parameter values to electrochemical processes. The proposed approach was validated on a 300 W SOFC using pure hydrogen fuel.

An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter

As one of the key functions in lithium-ion battery management system, the state-of-health (SOH) estimation is of great significance to ensure the safe and reliable operation and reduce the maintenance cost of the battery energy storage system. Unscented particle filter (UPF) algorithm is becoming a promising method for battery state estimation since it combines the latest measurement information to give the proposal distribution which is closer to the true posterior distribution. At the same time, UPF algorithm is able to represent the uncertainty involved in the estimation results, which makes great significance for battery SOH estimation. On the other hand, it is difficult to measure the battery actual capacity in practice despite the capacity is a direct indicator of battery SOH. In this paper, an on-line health indicator (HI) is extracted from the measurable parameters while battery is working. The mapping model between the extracted HI and battery SOH is established and applied as the observation in the state-space model. An on-line estimator based on UPF algorithm is developed for battery SOH assessment. The maximum estimation error based on battery cycling test data is less than 5%. This indicates that the proposed method has a good adaptability for lithium-ion battery degradation with non-linear and non-Gaussian characteristics. Additionally, the experiments on different types of lithium-ion battery show the good robustness and applicability of this approach.

Cycling capacity recovery effect: A coulombic efficiency and post-mortem study

The analysis of lithium-ion battery aging relies on correct differentiation between irreversible and reversible capacity changes. Anode overhang regions have been observed to influence Coulombic Efficiency (CE) measurements through lithium diffusion into and out of these areas, complicating precise capacity determination. This work presents an analysis of the extent of graphite anode overhang lithiation after calendar storage by means of local X-ray diffraction (XRD), CE measurements, and color change analysis. We found LiC12 lithiation of the anode overhang area after 20 month storage at 40 °C at high state of charge (SoC) and partial lithiation (LiC18) at medium SoC storage at 40 °C and 25 °C. Graphite color changes in the overhang areas are observed and consistent with the state of lithiation measured by XRD. Coulombic efficiencies greater than unity and increasing capacity during 1200 h of cycling are detected for high SoC storage cells. The capacity difference between high and low storage SoC batteries decreases by up to 40 mAh (3.6% of nominal capacity) after cycling compared to tests directly after storage. Consequently, the size of the anode overhang areas as well as the battery storage temperature and duration need to be considered in CE analysis and state of health assessment.