•Accuracy of ICA shown to reduce at higher C-rates through quantitative peak analysis.•High C-rate ICA is less accurate in aged NCA cells compared to fresh cells.•C/6 is optimum C-rate for ...maintaining accurate ICA in electric vehicle charge time.•Automated peak voltage shift introduced using current interrupt during charging.
Incremental capacity analysis (ICA) is a widely used method of characterising state of health (SOH) in secondary batteries through the identification of peaks that correspond to active material phase transformations. For reliable ICA, cells are cycled under low constant currents to minimise resistance and diffusion effects, making deployment into applications such as electric vehicle charging unfeasible.
In this work, the influence of charge/discharge rate on ICA is quantitively analysed through peak detection algorithms on two lithium-ion cells with different positive electrodes. Based on these results, a new robust method for faster ICA is introduced which corrects peak shift through SOC dependant resistance measurements using current interrupt. The new technique is evaluated through degradation tests on a Li(NiCoAl)O2/graphite cell. Results demonstrate that ICA during a 6-hour (C/6) charge represents an ideal compromise between diagnostic accuracy and realistic application charge times. ICA at C/6 can predict peak location within 0.59% of a 48-hour charge (C/48) using resistance correction, compared to 1.90% without correction. Under ageing, the C/6 charge was able to correctly identify the trend of each peak compared to C/24 charge and maintain peak location to within 2.0%.
At rates higher than C/6, the number of identifiable peaks in the ICA reduce, most noticeably in aged cells. After 200 cycles, only one identifiable peak was seen at 1C charge compared to four at C/6 and five at C/24.
Efficient battery capacity estimation is of utmost importance for safe and reliable operations of electric vehicles (EVs). This paper proposes a battery capacity estimation framework based on ...real-world EV operating data collected from forty electric buses of the same model operating in two cities. First, a reference capacity calculation method is presented by combining the Coulomb counting method with the incremental capacity analysis method. Then the impacts of temperature, current, and State-of-Charge on battery degradation are quantitatively analyzed. Using the historical probability distributions as battery health features, a hybrid deep neural network model that combines a convolutional neural network with a fully-connected neural network is proposed for battery capacity estimation. The validation results show that the proposed model outperforms the state-of-the-art methods and reaches a mean absolute percentage error of 2.79<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula>, while maintaining low computational cost.
The accurate estimation of the state-of-health (SOH) is vital to the life management of lithium-ion batteries (LIBs). In this article, we propose a fusion-type SOH estimation method by combining the ...model-based feature extraction and data-based state estimate. Particularly, a novel model-based voltage construction method is proposed to eliminate the unfavorable numerical condition and reshape the disturbance-free incremental capacity (IC) curves. Leveraging the modified IC curves, a set of informative features-of-interest is extracted and evaluated, while eventually several cautiously selected ones are used to estimate the SOH of LIBs accurately. Furthermore, the impact of model order on the estimation performance is scrutinized to give insights into the parameterization in practical applications. Long-term cycling tests on different types of LIB cells are used for evaluation. The proposed method is validated with a good robustness to the cell inconsistency, temperature uncertainty, noise corruption, and a satisfied generality to different battery chemistries.
Accurately assessing degradation and detecting abnormalities of overcharged lithium-ion batteries is critical to ensure the health and safe adoption of electric vehicles. This paper proposed a ...data-driven lithium-ion battery degradation evaluation framework. First, a multi-level overcharge cycling experiment was conducted. Second, the battery degradation behaviours and features were analyzed and extracted using incremental capacity analysis and pearson correlation coefficient. Above all, a data-driven lithium-ion battery degradation evaluation method based on machine learning and model integration method was developed. The proposed integrated model was compared with other state-of-the-art methods and reached a mean squared error of 1.26×10 -4 . Finally, based on prediction results, rate of degradation was calculated and classified to different degrees, and overcharged cells can be effectively identified. Moreover, to verify the feasibility of the proposed overall framework, the paper carried out an experiment by connecting overcharge-induced degraded cell and fresh cells in series to simulate the real-world battery assembly and function of battery management systems. Based on the proposed scheme, the overcharged batteries in the battery series can be detected efficiently likewise.
An accurate battery state-of-health (SOH) monitoring is crucial to guarantee safe and reliable operation of electric vehicles (EVs). In this paper, an incremental capacity analysis (ICA) method for ...battery SOH estimation is proposed. This uses grey relational analysis in combination with the entropy weight method. First, an interpolation method is employed to obtain incremental capacity (IC) curves. The health indexes are then extracted from the partial IC curves for grey relational analysis, and the entropy weight method is used to evaluate the significance of each health index. The battery SOH is assessed by calculating the grey relational degree between the reference and comparative sequences. Experimental tests are conducted on two battery cells with the same specifications to verify the efficacy of the proposed method. The results show that the maximum estimation error is limited to within 4%, thus proving its effectiveness.
•An interpolation method is proposed to fit the incremental capacity curve.•Health performance indicators (HPIs) are extracted from partial incremental capacity curve.•A novel battery state of health estimation method based on partial incremental capacity curve is proposed.•The entropy weight method is employed to evaluate the significance of battery evaluation indexes.•The grey relational analysis is used to indicate the state of health of degradation battery.
The incremental capacity analysis (ICA) method is widely used in battery state of health (SOH) estimation thanks to its high prediction accuracy and aging mechanism implications. However, realizing ...precise SOH metering for real-world electric vehicles (EVs) is still challenging, if not impossible, and comprehensive and large-scale laboratory tests necessitated are usually time-consuming and labor-intensive. This paper proposes an enabling SOH estimation scheme based on the ICA method for real-world EVs. This is realized by combining an equivalent IC-value calculation for battery packs with cell-level battery tests while taking cell inconsistency into consideration. The effectiveness of the proposed method is verified using the datasets collected from both well-controlled laboratory tests and daily operating EVs. The results show that battery cells within a batter pack generally experience similar degradation routes, which means insignificant cell inconsistency development with aging, and the proposed method can realize an accurate pack-level SOH estimation both for laboratory battery packs and real-world EVs. By applying the proposed method, the root mean square errors of battery SOH prediction for laboratory modules and packs and an electric taxi are 0.00955, 0.02457, and 0.0204, respectively.This study presents a verified framework of applying the ICA-based method to realize pack-level battery SOH estimation based on cell-level tests.
•A smoothing method for IC curves based on Kalman filter is proposed.•Battery standard and non-standard charging experiments are designed and conducted.•The influence of battery aging and charging ...condition on IC curves is revealed.•The adaptive ICA for capacity estimation takes the charging initial SOC into account.
Incremental capacity analysis (ICA) has been widely employed to investigate the degradation mechanism and perform the capacity estimation of lithium-ion batteries. However, the traditional capacity estimation based on ICA is limited by the computational efficiency and charging condition. In this paper, a rapid acquisition method of the incremental capacity (IC) curve is established, then an adaptive capacity estimation framework based on ICA considering the charging condition is proposed. Aiming at improving the computational efficiency, the Kalman filter is employed to acquire the smooth IC curves expeditiously, which requires small data length to handle the IC value. A considerable number of battery standard and non-standard charging experiments are designed and conducted. The influence of battery aging status, charging initial state of charge (SOC), and temperature on IC curves is revealed. Three features of the IC curve during standard charging are selected to investigate the relationship between battery capacity and the height of features. Furthermore, an adaptive correction method for capacity estimation considering the charging initial SOC is established, and the validation results show the effectiveness of the proposed correction method, which provides high accuracy and robustness.
Accurate SOH (State of Health) estimation is one of the key technologies to ensure the safe operation of lithium-ion batteries. When predicting SOH, efficient data feature extraction is the premise ...to ensure accurate prediction. In this work, a feature selection method is proposed to help neural networks train more effectively by removing useless features from the input data during the data preparation step. In addition, the skip connection is added to the convolutional neural network-long short-term memory (CNN-LSTM) model in this work to address the problem of neural network degradation caused by multi-layer LSTM. The presented approach is validated on the NASA and Oxford battery dataset. The results demonstrate that after using the feature selection approach to remove the less significant features, the SOH prediction accuracy is enhanced and the computational load on the neural network is decreased. Compared with other neural network models, the CNN-LSTM-Skip model has better robustness and higher accuracy under different conditions, and the RMSE is below 0.004 on the NASA dataset and the Oxford dataset.
•A CNN-LSTM-Skip model is presented to estimate the SOH of lithium-ion batteries.•A feature selecting approach is developed to remove useless features in the data.•The presented approach is validated on the NASA and Oxford battery datasets.•The RMSE of the CNN-LSTM-Skip model is below 0.004 on both datasets.
Prognostics and health management (PHM) are developed to accurately estimate the state of health (SOH) of lithium-ion batteries, which are crucial parts for planning the employment strategy in energy ...storage systems. Numerous studies about the data-driven batteries prognostics mostly assume complete and stable charging/discharging data. The on-board prognostics with random charging/discharging behaviors remains a challenging problem. This paper proposes a novel batteries prognostics method using random segments of charging curves, aiming at improving the flexibility and applicability in practical usage. Firstly, partial incremental capacity analysis is conducted within specific voltage range. And the extracted partial incremental capacity curves are used as features for SOH estimation and prognostics. Second, a long short-term memory network guided by Bayesian optimization is proposed to automatically tune the hyper-parameters and achieve accurate SOH estimation results. The effectiveness and robustness of the partial incremental capacity features acquired from different voltage ranges are investigated to provide guidelines for users. The superiority of the proposed method is validated on lithium-ion battery aging datasets from NASA and CALCE Prognostics Data Repository. The experimental results show that it can accurately predict aging patterns and estimate SOH by solely using small segments of charging curves, showing a promising prospect.
•Propose a novel battery prognostic method with LSTM and partial IC features.•Presented partial IC features avoid the identification of specified IC curve peaks.•Bayesian optimization is adapted into LSTM to automatically tune hyper-parameters.•The effectiveness is comprehensively investigated in two battery aging datasets.
The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries. A novel fusion-based SOH estimator is proposed in this study, which combines an ...open circuit voltage (OCV) model and the incremental capacity analysis. Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOIs) from the measured terminal voltage during constant-current charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness.
