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Incremental capacity–differential voltage is a powerful tool for transforming raw voltage data from battery cycling data into curves with distinguishable peaks and valleys. These peaks and valleys have been claimed as useful health features in the literature for providing non-invasive, comprehensive insights into a battery's health and age. Although extensive studies exist on this topic, no standardized approach for generating these curves has been established. This paper analyzes various calculation methodologies and different post-processing filters employed in the literature. These methods are validated using three datasets: two publicly available datasets from Oxford University and a publication from Nature, along with a dataset collected from Loughborough University. The findings highlight the effectiveness of specific calculation methodologies and filters through the differences in the curves produced. Based on the results and analysis, a recommended operational procedure for generating incremental capacity curves is proposed. This standardized procedure aims to enhance the reliability and consistency of producing incremental capacity curves for state-of-health assessments for batteries. [ABSTRACT FROM AUTHOR]

The last decade has witnessed significant progress towards zero-emission electric aviation. Lithium-ion batteries are at the centre of this technological transformation. However, electric flight requires suitable considerations for safety concerns associated with Lithium-ion batteries. Fault diagnostic approaches aimed at validating the safety of batteries before every flight have emerged as an attractive prospect. In this pursuit, a three-paper series proposing a novel fault diagnosis algorithm is presented. The algorithm is capable of diagnosing faults in an aircraft battery using the data collected during charging and was previously validated for a particular cell type under steady charging conditions. In this paper, two extensional aspects of the algorithm are investigated: cell-to-cell transferability and disparate charging conditions. While crucial for practical implementation, these aspects were overlooked in previous literature. Through experiments conducted at module-level, it was revealed that the algorithm could diagnose faults for a different cell type with minimal preliminary cell-level characterisation, thus demonstrating its ease of transferability. Moreover, modifications introduced in the algorithm enabled it to perform fault diagnosis under unsteady charging conditions for an aerospace module despite significant internal temperature gradients. Thus, the successful working of the algorithm in the considered aspects, which are unprecedented in literature, prove its feasibility for real-life application.

The second life use of lithium-ion batteries has gained significant attention in recent years, driven by the potential to repurpose cells from electric vehicles for less demanding applications. A critical aspect of this repurposing is accurately estimating the Remaining Useful Life (RUL) of the batteries. Traditional techniques often rely on data from the battery’s first life, which may not be available in practical scenarios. To address this issue, we propose a data-driven method for RUL estimation that does not depend on first-life information. Our approach considers a realistic scenario where an aged battery cell, lacking previous usage data, is evaluated for second life use through a limited number of test cycles. We compute features such as incremental capacity curves, and other health indicators from the measured voltage and current waveforms of the used cell. These features are automatically processed by deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This methodology achieves an average error of only 62 cycles for cells with a lifespan of up to 1200 cycles and a RUL error of less than 10% for deeply aged batteries. These results outperform state-of-the-art algorithms that utilize data from the cell’s entire lifespan, demonstrating the efficacy and robustness of this approach.

The successful integration of Li-ion batteries in the automotive industry paved the way for their integration into a new generation of aircraft with zero emission. However, potential hazards associated with Li-on batteries are a major concern for the safety-critical aerospace sector. Removing or reducing the hazards from Li-ion batteries while preferable, requires fundamental changes to the battery chemistry. Another way to address this is the implementation of fault diagnostics and prognostics for forecasting or detecting the presence of faults before an aircraft is airborne. This article, part of a three-part series, presents a novel algorithm exploiting the data collected while charging a battery pack for diagnosing faults. The algorithm works off-board, thus not adding any computational burden or weight to the aircraft while minimising aircraft turn-around time, which is crucial for commercial flight operation. This paper presents the underlying method of the algorithm involving the computation of resistance, charge energy and incremental capacity curve using the charging data followed by a combined horizontal and vertical comparative scheme for diagnosis of faults. The performance of the proposed algorithm was validated via module-level experiments considering three cell inactivation faults. Results indicated that the algorithm successfully segregated faulty and unfaulty cells in multiple test scenarios with different number of faults in a laboratory-assembled module. Upon testing the algorithm in experiments conducted using a real-life aerospace battery module, there was a slight reduction in the performance, attributed to greater thermal variation. Nevertheless, the results indicated promising prospects for practical implementation in eVTOL batteries.

Incremental capacity–differential voltage is a powerful tool for transforming raw voltage data from battery cycling data into curves with distinguishable peaks and valleys. These peaks and valleys have been claimed as useful health features in the literature for providing non-invasive, comprehensive insights into a battery’s health and age. Although extensive studies exist on this topic, no standardized approach for generating these curves has been established. This paper analyzes various calculation methodologies and different post-processing filters employed in the literature. These methods are validated using three datasets: two publicly available datasets from Oxford University and a publication from Nature, along with a dataset collected from Loughborough University. The findings highlight the effectiveness of specific calculation methodologies and filters through the differences in the curves produced. Based on the results and analysis, a recommended operational procedure for generating incremental capacity curves is proposed. This standardized procedure aims to enhance the reliability and consistency of producing incremental capacity curves for state-of-health assessments for batteries.

The Incremental Capacity (IC) is a rich source of data for the state‐of‐health estimation of lithium‐ion batteries. This data is typically collected during a low C‐rate (dis)charge of the battery which is not representative of many real‐world applications outside the research laboratories. Here, this limitation is showcased to be mitigated by employing a new feature‐extraction technique applied to a large dataset including 105 batteries with cycle lives ranging from 158 to 1637 cycles. The state‐of‐health of these batteries is successfully predicted with a mean‐absolute‐percentage error below 0.7 % by using three regression models of support vector regressor, multi‐layer perceptron, and random forest. The methodologies proposed in this work facilitate the development of accurate IC‐based state‐of‐health predictors for lithium‐ion batteries in on‐board applications. [ABSTRACT FROM AUTHOR]

Micro short circuit (MSC) fault diagnosis is thought functional in preventing thermal runaway of lithium-ion battery packs. Inconsistencies in the initial state-of-charge and aging state inevitably exist among cells of a battery pack. The existing method for MSC diagnosis disregards the symptoms originating from cell-to-cell inconsistency, which may lead to misdiagnosing inconsistent cells as MSC cells and vice versa. This work presents a method for detecting and quantitatively diagnosing MSC faults in lithium-ion battery packs, while taking cell inconsistency into consideration. Initially, the median incremental capacity (IC), derived based on ranking the terminal voltages of cells, is used as a benchmark representing the state of normal cells. Subsequently, the correlation coefficients between the ICs of individual cells and their median IC are calculated in both the time and frequency domains, as to distinguish the normal, inconsistent, and MSC cells. After detecting the MSC cell, an algorithm, which is based on a recursive least squares algorithm with forgetting factor and an adaptive H∞ Kalman filtering, is designed to calculate the short-circuit resistance online. The experimental results demonstrate that the short-circuit resistance estimated by the proposed algorithm exhibits rapid convergence to the actual values, thereby confirming the utility of the proposed algorithm in real-life contexts.

The SOH (State of Health) of lithium-ion batteries is an important feature that characterizes the reliable operation of energy storage systems. In this study, according to the IC (Incremental Capacity) curve and data-driven method, an SOH estimation method based on WPCA (Weight Principal Component Analysis) data dimensionality reduction and SW-TSVR (Structural Weighted Twin Support Vector Regression) was proposed to address the problem of model over-fitting due to redundant information present in the extraction of aging features of lithium-ion batteries. In addition, this study attempted to address issues in model underfitting caused by the small number of aging samples for small sample data as well as the difficulty in extracting effective aging features. Accordingly, WPCA was able to fully excavate aging characteristics from a limited battery aging sample and greatly reduce the interference of redundant information on the accuracy of the model, thereby improving the accuracy of SOH estimation. Using SW-TSVR, the structural and weight information of battery aging samples were shown to be fully integrated into TSVR, for which a strong model generalization ability was constructed for small samples. Finally, 750 aging cycles were conducted with 0.5C–1C current to verify the above method. This study’s findings demonstrated that the proposed method was able to achieve accurate SOH estimation in cases of small data samples.

This paper presents battery aging models based on high-current incremental capacity features in the presence of battery cycling profiles characterized by fast charging conditions. In particular, the main peak area under the incremental capacity graph is proposed as a capacity indicator. A dataset from the Toyota Research Institute is analyzed. Batteries' cycling data are characterized by various single- or double-step fast charges in constant current to reach 80% of the battery state of charge; the remaining charge process is performed by a 1C charge. Depending on the battery, a linear or logarithmic model was identified as the best suitable for representing the capacity–peak area relationship. The generalization capabilities of the proposed models are evaluated by performing an inference analysis of the fitting results over groups of batteries. Finally, we evaluated the prediction performance of the models by adopting a cross-validation approach. [ABSTRACT FROM AUTHOR]

Data-driven-based State of Health (SOH) estimation methods are popular for their ability to avoid considering the battery's complex aging mechanisms. However, aging features extracted from specific voltage intervals and the SOH estimation models established under specific conditions may no longer be applicable to other battery applications. This work first reveals the necessary conditions for constructing effective aging features in a partial voltage interval, and specifically proposes an applicable method for determining the voltage interval of constructing aging features based on the incremental capacity curve and the voltage curve's shape. The proposed method can be adapted to different battery applications and is suitable for partial charging conditions. Given the different aging patterns present in different battery types and applications, a feature self-scaling -based online learning strategy is developed to reduce the model's reliance on aging datasets and enable it to adapt to real-time battery aging patterns. The proposed methods are validated through both an actual aging experiment and three publicly available aging datasets, showcasing their effectiveness in extending the applicability of the proposed SOH estimation methods for diverse battery applications. • Reveal the necessary conditions that the voltage range for constructing effective aging features must satisfy. • Design a specific voltage range determination strategy under partial charging conditions. • Develop a feature self-scaling-based SOH online estimation strategy. [ABSTRACT FROM AUTHOR]

Accurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are crucial to large-scale commercial use for electric vehicles. The data-driven method lately has drawn great attention in this field due to efficient machine learning, but it remains an ongoing challenge in the feature extraction related to battery lifespan. Some studies focus on the features only in the battery constant current (CC) charging phase, regardless of the joint impact including the constant voltage (CV) charging phase on the battery aging, which can lead to estimation deviation. In this study, we analyze the features of the CC and CV phases using the optimized incremental capacity (IC) curve, showing the strong relevance between the IC curve in the CC phase as well as charging capacity in the CV phase and battery lifespan. Then, the life prediction model based on automated machine learning (AutoML) is established, which can automatically generate a suitable pipeline with less human intervention, overcoming the problem of redundant model information and high computational cost. The proposed method is verified on NASA's LIBs cycle life datasets, with the MAE increased by 52.8% and RMSE increased by 48.3% compared to other methods using the same datasets and training method, accomplishing an obvious enhancement in online life prediction with small-scale datasets. [ABSTRACT FROM AUTHOR]

The growing market for electric vehicles and portable electronics requires the reliability assessment of Li-ion batteries, especially in terms of nonlinear capacity degradation. For this purpose, a flexible health prognostic approach for nonlinear battery aging is proposed by leveraging temporal transfer learning. In contrast to existing prediction methods, our approach provides an unsupervised segmentation approach that characterizes the temporal distribution of nonlinear aging trajectories, which is proven to be associated with the loss of active materials. Subsequently, the temporal domain features are aligned with adaptively adjusted weight alongside the capacity decay trend, followed by the domain loss calculation and nonlinear aging prediction. Three battery datasets based on different cell types and conditions undertake the validation of model adaptability. The experiments demonstrate outstanding performance in accurate health prognostic when collaboratively learning short-term incremental capacity and temporal distribution. The proposed approach utilizes temporal transfer learning for health prognosis and promises to be flexibly extended to other battery management systems. [Display omitted] • The domain division separates battery cycle ranges for nonlinear aging transfer learning. • Automatically weighted temporal transfer learning is proposed to forecast battery nonlinear aging. • Diverse cell types and conditions undertake validation of model effectiveness and adaptability. • The model is flexible among divergent energy devices for nonlinear aging prediction. [ABSTRACT FROM AUTHOR]

Accurate online estimation of state of charge (SOC) and state of health (SOH) is crucial for safe operation and reasonable planning of battery management system (BMS). However, battery internal states are unmeasurable directly and coupled with each other, which results in serious difficulties on multi-states accurate estimation. Considering SOC estimation is deeply influenced by SOH, this paper proposes a collaborative estimation method combined machine learning algorithm with simple physical rule. Integrated the merits of the above methods, it can not only improve the estimating accuracy but also low computational burden to a large extent. Firstly, SOH acts as input of SOC in collaboration, which means SOH should have been already known before SOC estimation operation. Thus, SOH is creatively predicted based on gate recurrent unit network in ahead of SOC estimation. Subsequently, the well-trained SOC data-driven model is combined with the ampere hour (Ah) integration, which act as observation and state equation respectively in the particle filtering algorithm to realize the final estimation of SOC. This way, the sole data-driven model is well improved under physics restriction without any other computational procedure, such as online parameter identification. Simultaneously, the predicted SOH is used to fill the Ah integration expression to realize the collaboration between SOC and SOH. What's more, the SOH is further corrected by the incremental capacity peak at the checkpoint, thereby further reducing the estimation deviation. The experimental result on battery shows that the SOH estimated error is below 0.3Ah (the rated capacity is 32.5Ah) and SOC error is less than 1.5 %. What's more, the generalization capacity of the proposed method is verified on different battery types based on public datasets. Obviously, the proposed method has fairly satisfactory property and performance and can totally support its promotion into state online evaluation of battery energy storage. • Clarify the coupling relationship between SOC and SOH. • Based on the coupling relation, SOH estimation takes single-step prediction and checkpoints correction with GRU network. • SOC estimation takes data and physics fusion method via acting as observation and state equation in PF respectively. • The co-estimation framework is verified both on laboratory measured data and public dataset. [ABSTRACT FROM AUTHOR]

An incremental capacity parametric model for batteries is proposed. The model is based on Verhulst's logistic equations and distributions in order to describe incremental capacity peaks. The model performance is compared with polynomial models and is demonstrated on a commercial lithium-ion cell. Experimental data features low-current discharges performed at temperatures ranging from −20 °C to 55 °C. The results demonstrate several advantages of the model compared to empirical models. The proposed model enables a clear description of the geometric features of incremental capacity peaks. It also doubles as an open circuit voltage model as the voltage curve can be fully recovered from parameterization on incremental capacity curves. The study of temperature sensitivity show that peak geometric parameters can be modelled as a function of temperature. An example of practical application is then displayed by using the model to estimate battery state-of-charge from voltage and temperature measurements. This example can expand to other practical applications for battery management systems such as state-of-health monitoring. [ABSTRACT FROM AUTHOR]

This paper presents battery aging models based on high-current incremental capacity features in the presence of battery cycling profiles characterized by fast charging conditions. In particular, the main peak area under the incremental capacity graph is proposed as a capacity indicator. A dataset from the Toyota Research Institute is analyzed. Batteries’ cycling data are characterized by various single- or double-step fast charges in constant current to reach 80% of the battery state of charge; the remaining charge process is performed by a 1C charge. Depending on the battery, a linear or logarithmic model was identified as the best suitable for representing the capacity–peak area relationship. The generalization capabilities of the proposed models are evaluated by performing an inference analysis of the fitting results over groups of batteries. Finally, we evaluated the prediction performance of the models by adopting a cross-validation approach.

The safe operation of battery energy storage systems (BESSs) has become one of the research priorities in this industry. And it is usually threated by various faults caused by design flaws, environmental conditions, and operating conditions et al. Among these faults, the internal short circuit (ISC) faults pose a significant threat to the safety of BESSs. Relevant studies focus on ISC fault diagnosis itself and ignore the impact of battery aging within the pack on fault diagnosis. To solve this problem, this paper proposes an ISC fault diagnosis method based on incremental capacity (IC) curves. And a qualitative differentiation between ISC batteries and aging ones is first achieved by leveraging the characteristic variations of IC curves. Then, an equivalent circuit model is constructed for ISC batteries. Further, a joint estimation of ISC resistance and SOC of the faulty battery is performed by combining Extended Kalman Filtering (EKF) and Forgetting Factor Recursive Least Squares (FFRLS). Finally, an experimental platform is established to verify the proposed method. Results show the proposed method can effectively differentiate between ISC batteries and aging batteries. Moreover, the estimation errors of SOC are less than 0.26% and the estimation accuracy of ISC resistance is more than 99.42%. • New method for ISC fault diagnosis based on IC curve considering the effect of aging. • New insights into the distinguish between internal short circuit battery and aging battery. • An equivalent circuit model is established to quantify the internal short circuit resistance. • A joint estimation algorithm to capture the battery SOC and the degree of ISC faults. [ABSTRACT FROM AUTHOR]

Accurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are crucial to large-scale commercial use for electric vehicles. The data-driven method lately has drawn great attention in this field due to efficient machine learning, but it remains an ongoing challenge in the feature extraction related to battery lifespan. Some studies focus on the features only in the battery constant current (CC) charging phase, regardless of the joint impact including the constant voltage (CV) charging phase on the battery aging, which can lead to estimation deviation. In this study, we analyze the features of the CC and CV phases using the optimized incremental capacity (IC) curve, showing the strong relevance between the IC curve in the CC phase as well as charging capacity in the CV phase and battery lifespan. Then, the life prediction model based on automated machine learning (AutoML) is established, which can automatically generate a suitable pipeline with less human intervention, overcoming the problem of redundant model information and high computational cost. The proposed method is verified on NASA’s LIBs cycle life datasets, with the MAE increased by 52.8% and RMSE increased by 48.3% compared to other methods using the same datasets and training method, accomplishing an obvious enhancement in online life prediction with small-scale datasets.

An incremental capacity parametric model for batteries is proposed. The model is based on Verhulst’s logistic equations and distributions in order to describe incremental capacity peaks. The model performance is compared with polynomial models and is demonstrated on a commercial lithium-ion cell. Experimental data features low-current discharges performed at temperatures ranging from −20 °C to 55 °C. The results demonstrate several advantages of the model compared to empirical models. The proposed model enables a clear description of the geometric features of incremental capacity peaks. It also doubles as an open circuit voltage model as the voltage curve can be fully recovered from parameterization on incremental capacity curves. The study of temperature sensitivity show that peak geometric parameters can be modelled as a function of temperature. An example of practical application is then displayed by using the model to estimate battery state-of-charge from voltage and temperature measurements. This example can expand to other practical applications for battery management systems such as state-of-health monitoring.

Abstract Lithium-ion cells currently power almost all electronic devices and power tools; they are a key enabling technology for electric vehicles and are increasingly considered to be the technology of choice for grid storage. In line with this increased applicability, there is also an increase in the development of new commercial lithium-ion cell technologies that incorporate innovative functional components (electrode material compositions and electrolyte formulations) and designs, leading to a diverse range of performance characteristics. The uniqueness of each technology in-turn gives rise to unique evolutions of cell performance as the cell degrades because of usage. Non-destructively measuring and subsequently tracking the evolution of lithium-ion cell characteristics is valuable for both industrial engineers and academic researchers. To proceed in this regard, stakeholders have often devised their own procedures for characterising lithium-ion cells, typically without considering unification, comparability or compatibility. This makes the comparison of technologies complicated. This comprehensive review for the first time has analysed and discusses the various international standards and regulations for the characterisation and electrical testing of lithium-ion cells, specifically for high-power automotive and grid applications. [ABSTRACT FROM AUTHOR]

Abstract Lithium-ion batteries with Li 4 Ti 5 O 12 (LTO) negative electrodes have been recognized as a promising candidate over graphite-based batteries for the future energy storage systems (ESS), due to its excellent performance in rate capability, cycle life and inherent safety. Accurate identification of battery degradation mechanisms is of great significance for reliable management and sustainable capability of ESS. Unfortunately, only a few quantitative analysis regarding LTO batteries are drawn at extreme conditions including designed overcharge and cycling at high temperatures. In this paper, cyclic aging behaviors of commercial LTO batteries are investigated at room temperature (RT) by applying incremental capacity and differential voltage techniques. A typical sketch of regional divisions among the voltage curve of a LTO battery is presented to explain the effects induced by different degradation modes on battery performance. The results imply that the loss of active material in the positive electrode occupies at least 83% and 81% of the total capacity loss under cycling at 10C and 5C respectively, while pure loss of lithium inventory is detectable when cycling at 5C rather than at 10C. Depth of discharge is further considered as the dominant stress of LTO batteries during the high-rate operation at RT. Highlights • Study of cyclic aging of commercial 10 Ah LTO batteries at room temperature. • Illustration of LTO batteries performance upon high-rate cycling. • Impact analysis of different aging mechanisms on battery behaviors. • Identification and quantitative analysis of major mechanism for capacity fade of battery. • Detailed discussion of C-rate effect on battery aging phenomenon. [ABSTRACT FROM AUTHOR]

Degradation in lithium-ion batteries is traditionally characterized with the pseudo open-circuit voltage (pOCV) or incremental capacity (IC) but these methods have hours-long diagnostics times and cannot easily measure impedance change. It is shown here that a pulse with amplitude 1 C-rate can perform both IC and impedance characterization in just 2 min. Pulses and C/20 pOCV from 6 lithium-ion cells at 1328 unique combinations of state of charge, state of health, and temperature are evaluated using the convolution-defined diffusion equivalent circuit model, ridge regression, and neural networks. Ridge regression of the IC extrema and the pulse harmonics predicts SoH and nominal SoC with less than 1% and 6% error, respectively. Individual contributions of the ohmic, charge transfer, and diffusion overpotentials, as well as open-circuit voltage or hysteresis, are quantified for the charge pulse. Neural networks reconstruct IC extrema from the pulse harmonics with less than 1% error. The pulse response therefore reflects internal kinetic parameters and electrode phase transitions which are best uncovered using neural networks. Our results extend the uses of pulsing and suggest novel methods for degradation diagnostics in battery management systems. • Pulse injection aided machine learning (PIAML) based battery estimation. • PIAML based estimation of overpotentials and incremental capacity. • Pulse harmonics are good predictors of battery degradation. • Results link ohmic, charge transfer, and diffusion overpotentials with SoC and SoH. • Open-circuit voltage and hysteresis effects contribute significantly to the pulse voltage. [ABSTRACT FROM AUTHOR]

Cover Feature: An Incremental Capacity Analysis-based State-of-health Estimation Model for Lithium-ion Batteries in High-power Applications (Batteries & Supercaps 9/2023) Battery, incremental capacity, state of health Keywords: battery; incremental capacity; state of health EN battery incremental capacity state of health 1 1 1 09/11/23 20230901 NES 230901 B The Cover Feature b illustrates the crucial role of the battery storage systems in the energy transition towards the smart cities. [Extracted from the article]

The estimation of lithium ion capacity fade and impedance rise on real application is always a challenging work due to the associated complexity. This work envisages the study of the battery charging profile indicators (CPI) to estimate battery health indicators (capacity and resistance, BHI), for high energy density lithium-ion batteries. Different incremental capacity (IC) parameters of the charging profile will be studied and compared to the battery capacity and resistance, in order to identify the data with the best correlation. In this sense, the constant voltage (CV) step duration, the magnitudes of the IC curve peaks, and the position of these peaks will be studied. Additionally, the behaviour of the IC curve will be modeled to determine if there is any correlation between the IC model parameters and the capacity and resistance. Results show that the developed IC parameter calculation and the correlation strategy are able to evaluate the SOH with less than 1% mean error for capacity and resistance estimation. The algorithm has been implemented on a real battery module and validated on a real platform, emulating heavy duty application conditions. In this preliminary validation, 1% and 3% error has been quantified for capacity and resistance estimation.

High coulombic efficiency (CE) usually indicates a long battery cycle life. However, the relationship between long-term CE evolution and battery degradation is not fully understood yet. This paper explores the behavior of long-term CE and clarifies its relationship with capacity degradation. Cycle life tests are conducted on two types of mainstream commercial lithium-ion batteries. An incremental capacity (IC) analysis is then employed to identify battery aging mechanisms. Experimental observations along with in-depth discussions are presented regarding battery degradation, aging mechanisms, and CE evolution. From the experimental results, two typical degradation patterns are recognized. From the IC analysis, we observed that, in addition to a loss of lithium inventory, a loss of active material accelerates battery degradation and brings down CE values. From an electrochemical perspective, this paper establishes the relationship between CE evolution and capacity degradation. This relationship can help develop battery degradation models, estimate battery health states, and provide early failure warnings for a battery management system. [ABSTRACT FROM AUTHOR]

This paper proposes an advanced state of health (SoH) estimation method for high energy NMC lithium-ion batteries based on the incremental capacity (IC) analysis. IC curves are used due to their ability of detect and quantify battery degradation mechanism. A simple and robust smoothing method is proposed based on Gaussian filter to reduce the noise on IC curves, the signatures associated with battery ageing can therefore be accurately identified. A linear regression relationship is found between the battery capacity with the positions of features of interest (FOIs) on IC curves. Results show that the developed SoH estimation function from one single battery cell is able to evaluate the SoH of other batteries cycled under different cycling depth with less than 2.5% maximum errors, which proves the robustness of the proposed method on SoH estimation. With this technique, partial charging voltage curves can be used for SoH estimation and the testing time can be therefore largely reduced. This method shows great potential to be applied in reality, as it only requires static charging curves and can be easily implemented in battery management system (BMS). [ABSTRACT FROM AUTHOR]

The uneven dispersion of MnO easily leads to local volume change, resulting in the crushing and pulverizing of electrode. Herein, a spongy three-dimensional (3D) porous MnO/C composite was fabricated by pyrolysis of the double-network (DN) aerogel composed of agar (AG) and Mn-crosslinked sodium alginate (SA). The spongy MnO/C composite has homogeneous distribution and uniform size ultrafine MnO nanoparticles embedded into 3D porous carbon network and shows high BET surface area of 363 m2 g−1, which can not only improve the electrical conductivity, but effectively confine the expulsion of MnO and restrain the crushing and pulverizing of electrode. As anode for LIBs, the spongy MnO/C composite exhibits superior rate capability (574 mAh g−1, 2 A g−1) and excellent cycling stability. The capacity gradually increases during the cycling process, reaching 1270 mAh g−1 after 500 cycles at 2 A g−1. The rising capacity can be attributed to the following reasons: 1) more Mn2+ was oxidated to higher valence, 2) The enhanced amorphism of MnO improves Li+ diffusion kinetics, 3) the reversible form and decomposition of SEI. • A spongy three-dimensional porous MnO/C composite was fabricated. • MnO/C composite has homogeneous ultrafine MnO nanoparticles and high BET surface area. • MnO/C composite exhibits excellent rate capability and cycling stability. • MnO/C composite is a potential anode material. [ABSTRACT FROM AUTHOR]

The recognition of failed batteries in a battery pack has long been a time-consuming task. Thus there is an urgent need for an on-board method implementation to identify failed batteries for the safe operation of electric vehicles. In this paper, a novel method to identify failed batteries with insufficient capacities is proposed. The properties of the incremental capacity curve are studied. Six features are extracted from the partial incremental capacity curve of each battery and a shrinkage method called the elastic net is used to select two variables that are most relevant to the capacity fade. A classification model based on linear discriminant analysis is established which can assign a given battery into two classes, namely “good” and “bad”. The effect of prior probability for each class of battery and the configuration to minimize true loss are discussed. This proposed method is relatively easy to implement with high accuracy, thus having high practicability. [ABSTRACT FROM AUTHOR]

This paper presents a released capacity analysis for the power delivery system of the distribution grid, and the computation of the incremental capacity addition provided by an optimally deployed photovoltaic (PV) system in the IEEE 13 and 34 test feeders. The intrinsic strong correlation that exists between network capacity and reliability gives the inclusion of renewable distribution generation (DG) and the evaluation of their additional load serving capacity a prime place in the emerging active distribution system planning (ADSP). Also, this article presents a method capable of measuring risk and upgrade deferral in the evolving ADSP using two energy quantities referred to as the energy exceeding normal (EEN) and unserved energy (UE). The evaluation of these metrics is used to assess system capacity usage, and as a planning tool to identify required system upgrade and measure the effectiveness of the applied DG incremental solution. This analysis is pivotal as the load growth continues to outpace new feeder construction and network expansion becomes difficult and costly. Therefore, utilizing DG to provide peak load relief and incremental capacity solution is developing into a viable alternative. In addition, results presented in this article show that apart from optimal positioning and sizing, maximizing the value attributes of PV-DG also, depends on its ability to produce power at proper times and levels. Also, since energy is the quantity being sold, this article further presents a simple economic analysis based on EEN and loss savings which can be used to compare alternative power generation sources. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for automated state of health estimation that offers similar advantages to the adaptive methods without being computation intensive. The onboard diagnosis uses a look-up table compiling the evolution of selected features of interest under any possible degradation paths. The look-up table is built from simulations of the impact of degradation on the cell electrochemical behavior. This multi-step method only requires intensive calculations prior to deployment. This approach is validated by experimental data from cells that underwent normal aging as well as plating and overcharge. Additional validation via modeling showed that the method is able to diagnose cells undergoing any degradation scenario automatically in close to 90% of cases. [ABSTRACT FROM AUTHOR]

Lithium plating is considered one of the most detrimental phenomenon in lithium ion batteries (LIBs), as it increases cell degradation and might lead to safety issues. Plating induced LIB failure presents a major concern for emerging applications in transportation and electrical energy storage. Hence, the necessity to operando monitor, detect and analyze lithium plating becomes critical for safe and reliable usage of LIB systems. Here, we report in situ lithium plating analyses for a commercial graphite||LiFePO 4 cell cycled under dynamic stress test (DST) driving schedule. We designed a framework based on incremental capacity (IC) analysis and mechanistic model simulations to quantify degradation modes, relate their effects to lithium plating occurrence and assess cell degradation. The results show that lithium plating was induced by large loss of active material on the negative electrode that eventually led the electrode to over-lithiate. Moreover, when lithium plating emerged, we quantified that the loss of lithium inventory pace was increased by a factor of four. This study illustrates the benefits of the proposed framework to improve lithium plating analysis. It also discloses the symptoms of lithium plating formation, which prove valuable for novel, online strategies on early lithium plating detection. [ABSTRACT FROM AUTHOR]

In a review of literature on operating conditions, such as temperature, materials, cutoff voltage, and current rate, state-of-charge (SOC) interval has attracted less attention regarding research on cell fading. Lithium-ion cells as power source practically applied to electric vehicles (EVs) are seldom operated in the entire SOC interval, which means that lithium-ion cells are employed in a certain SOC interval. However, the SOC interval that facilitates the increase of the life span of the cell remains unknown. In the current study, several cells are tested over different SOC intervals and discharge rates. In-depth analysis is performed on the influence of the two factors on cell fading. The incremental capacity and incremental resistance methods are used to analyze capacity fading and resistance evolution. The electrochemical mechanism analysis of cell fading is studied and discussed, showing that SOC interval influences the rate of cell degradation. Results show that a low SOC interval prolongs the life span of cell use if other operating conditions are similar. Therefore, the study provides a theoretical framework and reference for the cell maintenance of EVs. [ABSTRACT FROM AUTHOR]

• SVM is proposed to construct the relationship between voltage and capacity. • The peak value of incremental capacity curve is extracted as the health feature. • The preliminary capacity values are achieved based on the SVM, LSTM and GPR. • The random forest algorithm is employed to fuse the preliminary capacity values. 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. [ABSTRACT FROM AUTHOR]

There are many types of high-power batteries used in HEVs, and their durabilities and degradation mechanisms are different. In this paper, four types of commercial high-power batteries, including two types of LTO/NCM lithium-ion battery from two different manufacturers, a C/LMO battery and a supercapacitor (SC), are studied. A durability test with a realistic current profile for an HEV is used so that the durability results more closely reflect real operating conditions than a general cycle life test. Incremental capacity (IC) curves are used to qualitatively analyze the degradation mechanism. To compensate for defects in the IC method, a prognosis model, using a genetic algorithm to reconstruct constant current charge voltage curves, is adopted to quantitatively identify the battery aging mechanism. [ABSTRACT FROM AUTHOR]

Accurate on board state of health estimation is a key battery management system function to provide optimal management of the battery system under control. In this regard, this paper presents an extensive study and comparison of three of commonly used supervised learning methods for state of health estimation in Graphite/Nickel Manganese Cobalt oxide cells. The three methods were based from the study of both incremental capacity and differential voltage curves. According to the ageing evolution of both curves, features were extracted and used as inputs for the estimation techniques. Ordinary Least Squares, Multilayer Perceptron and Support Vector Machine were used as the estimation techniques and accurate results were obtained while requiring a low computational effort. Moreover, this work allows a deep comparison of the different estimation techniques in terms of accuracy, online estimation and BMS applicability. In addition, estimation can be developed by partial charging and/or partial discharging, reducing the required maintenance time. [ABSTRACT FROM AUTHOR]

This work depicts the calendar aging results of four Li-ion battery technologies. The differences in the chemistry of Li-ion batteries was studied and revealed that cathodes containing manganese are more sensitive to state-of-charge and temperature increase than lithium–iron-phosphate or lithium–nickel–cobalt–aluminum batteries. The first step in presenting the differences in technology of the Li-ion battery is through the study of the battery voltage evolution versus the amount of charge at various states of health. This study revealed a significant increase in resistance on lithium–nickel–manganese–cobalt and lithium–manganese-oxide cells; a result which was confirmed through impedance spectroscopy measurements. Finally, a study of the comparison of the different types of Li-ion batteries was undertaken, based on the analysis of the evolution of energy efficiency with respect to aging. [ABSTRACT FROM AUTHOR]

A major challenge that limits fast charging of Li-ion batteries is lithium (Li) plating on the graphite electrode. However, it remains challenging to detect and diagnose Li plating in operando during charging. In this work, incremental capacity (IC) analysis is applied while charging graphite-NMC pouch cells over a range of rates from C/2 to 4C. Three-electrode pouch cell measurements and post-mortem SEM imaging was performed to demonstrate that the onset of Li plating is correlated with a specific IC peak. IC analysis was also applied to study the fast-charge performance of multi-layer pouch cells with 3-D anode architectures. The results demonstrate that: 1) IC curves have a characteristic peak that is an indicator of Li plating during fast charging, which grows in magnitude as charging rate increases; 2) the plating IC peak correlates with the voltage minimum of the graphite anode, indicating a transition from intercalation to plating; 3) the plating IC peak is sensitive to small amounts of Li plating; 4) IC analysis can be applied to study Li plating in novel cell architectures; 5) the plating IC peak evolves during extended fast-charge cycling, which is a result of reduced Li plating as the Li inventory decreases. • A peak in the IC curves of Li-ion batteries indicates Li plating during fast charge. • The plating peak correlates with the voltage minimum of the graphite anode. • Microscopy results show that the peak is sensitive to small amounts of Li plating. • IC analysis can be applied to study Li plating in novel cell architectures. • The plating peak evolves during extended fast charge cycling. [ABSTRACT FROM AUTHOR]

Abstract: When lithium-ion batteries age with cycling, the battery capacity decreases and the resistance increases. The aging mechanism of different types of lithium-ion batteries differs. The loss of lithium inventory, loss of active material, and the increase in resistance may result in battery aging. Generally, analysis of the battery aging mechanism requires dismantling of batteries and using methods such as X-ray diffraction and scanning electron microscopy. These methods may permanently damage the battery. Therefore, the methods are inappropriate for the battery management system (BMS) in an electric vehicle. The constant current charging curves while charging the battery could be used to get the incremental capacity and differential voltage curves for identifying the aging mechanism; the battery state-of-health can then be estimated. This method can be potentially used in the BMS for online diagnostic and prognostic services. The genetic algorithm could be used to quantitatively analyze the battery aging offline. And the membership function could be used for onboard aging mechanism identification. [Copyright &y& Elsevier]

Early internal short circuit (ISC) diagnosis is critical for a battery management system (BMS) to prevent the thermal runaway of lithium-ion batteries. However, it is difficult to be diagnosed owing to few obvious electric and thermoelectric characteristics in the early stage of ISC. In this study, a novel ISC diagnosis method based on the incremental capacity (IC) curves is proposed. Different charging rates on four ISC situations are carried out on a cell to verify the proposed method. The leakage current of the ISC battery can be obtained by the area difference between the normal cell and the ISC cell, and it can be converted into the ISC resistance. The experiments of different initial charging states of charge (SOC) in a series-connected battery pack are conducted to verify the method in the real EVs working environment. The diagnosis results of the battery cell and battery pack indicate the proposed method is feasible and effective to quantitatively diagnose the ISC. • A novel internal short circuit diagnosis method based on the peak area of the IC curve is proposed. • The battery cell and series-connected battery pack charging experiments are designed and conducted. • The influence of internal short circuit on IC curves is revealed. • The experimental results show that the proposed approach is feasible and effective. [ABSTRACT FROM AUTHOR]

Abstract: Degradation phenomena and inference of their underlying mechanisms during 2C cycle aging in a cell design comprising {LiMn1/3Ni1/3Co1/3O2 +LiMn2O4} composite positive electrode are studied and reported in this work. We describe how aging phenomena in the cells were studied and incremental capacity analysis applied to infer cell degradation mechanisms in the cycle aging process. Two stages of degradation were observed in the life cycle under this aging regime. In the first stage, we conclude that loss of lithium inventory was the cause of capacity fade. As a result of such parasitic loss, the cell further suffered from loss of active materials in the second stage, in which the positive electrode kinetics was hampered and the capacity loss accelerated. [Copyright &y& Elsevier]

Abstract: Evaluating commercial Li-ion batteries presents some unique benefits. One of them is to use cells made from established fabrication process and form factor, such as those offered by the 18650 cylindrical configuration, to provide a common platform to investigate and understand performance deficiency and aging mechanism of target chemistry. Such an approach shall afford us to derive relevant information without influence from processing or form factor variability that may skew our understanding on cell-level issues. A series of 1.9Ah 18650 lithium ion cells developed by a commercial source using a composite positive electrode comprising {LiMn1/3Ni1/3Co1/3O2 +LiMn2O4} is being used as a platform for the investigation of certain key issues, particularly path-dependent aging and degradation in future plug-in hybrid electric vehicle (PHEV) applications, under the US Department of Energy''s Applied Battery Research (ABR) program. Here we report in Part I the initial characterizations of the cell performance and Part II some aspects of cell degradation in 2C cycle aging. The initial characterizations, including cell-to-cell variability, are essential for life cycle performance characterization in the second part of the report when cell-aging phenomena are discussed. Due to the composite nature of the positive electrode, the features (or signature) derived from the incremental capacity (IC) of the cell appear rather complex. In this work, the method to index the observed IC peaks is discussed. Being able to index the IC signature in details is critical for analyzing and identifying degradation mechanism later in the cycle aging study. [Copyright &y& Elsevier]

The Incremental Capacity (IC) analysis is used to characterise the capacity and the battery state of health, aged by cycling patterns with randomly selected pulsed current levels and duration. The batteries are periodically characterised at 1C current, which is a high value with respect to the typical IC tests in pseudo-equilibrium condition. The high-current IC curves generation from raw voltage/current data includes two filtering stages, one for the input voltage and one for the incremental capacity curve smoothing, which are optimised for the application on the basis of the data characteristics. The correlations between the IC main peak features and the battery full capacity for 28 Lithium–Cobalt oxide batteries with 18650 packaging were evaluated, finding that the main peak area is a general feature to evaluate the state of health under high current tests and random usage pattern, and, therefore, it can be used as a battery health indicator in practical applications. The effects of the computational parameters on the relationship between the peak area and the battery capacity are also investigated. The results are confirmed by a further analysis performed over an additional set of cells with different technology, aged with a fixed cycling pattern. Additionally, the performance of the peak area as a health indicator was compared with an ohmic resistance-based estimation approach. [Display omitted] • Batteries aged with randomised cycling analysed with incremental capacity (IC). • The IC analysis is performed at high current in charging phase. • The peak features are discussed as battery capacity indicator. • The battery capacity is well correlated to the IC curve peak area. • The peak area shows very low sensitivity to computational parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • The orderly stacked graphene supporting SnO 2 is prepared for the first time. • The orderly stacked graphene could inhibite the aggregation of SnO 2. • The SnO 2 @OS-rGO shows enhanced reversible capacity and long cycling life. • Analyze the relationship between structure and electrochemical performance. The large capacity fading and short lifespan of SnO 2 -based anodes originate from the serious coarsening of Sn nanoparticles caused by structural reorganization of SnO 2 during repeated cycling. Herein, a hybrid structure composed of SnO 2 supported on orderly stacked graphene sheets (SnO 2 @OS-rGO) is constructed by freeze-casting method to address the above issues. The orderly stacked graphene anchors SnO 2 firmly depending on the π-π interactions, thus improves the discharge capacity and cycling stability of SnO 2. Besides, the ordered graphene promotes the reaction kinetics of SnO 2 @OS-rGO by shorten the transmission path of lithium ions. As a result, the obtained SnO 2 @OS-rGO delivers an unconventional discharge capacity up to 1080 mAh g−1 at 0.2 A g−1 for over 500 cycles, which increased by ~ 200% compared with the 1st cycle. The gradually increasing capacity originates from the improvement of the conversion reaction. This study provides a new strategy for developing long cycle stable SnO 2 anodes by introducing orderly stacked graphene. [ABSTRACT FROM AUTHOR]