•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.
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.
•A new framework based on ICA is used to monitor SOH on-board for battery packs.•The applicability of the framework is validated through simulation and experiment.•The method can monitor SOH for pack ...consisting of cells with various aging paths.•On-board incremental capacity analysis is realized by support vector regression.
Incremental capacity analysis (ICA) is a widely used technique for lithium-ion battery state-of-health (SOH) evaluation. The effectiveness and robustness of ICA for single cell diagnostics have been reported in many published work. In this study, we extend the ICA based SOH monitoring approach from single cells to battery modules, which consist of battery cells with various aging conditions. In order to achieve on-board implementation, an IC peak tracking approach based on the ICA principles is proposed. Analytical, numerical and experimental results are presented to demonstrate the utility of the IC peak tracking framework on multi-cell battery SOH monitoring and the effects of cell non-uniformity on the proposed method. Results show that the methods developed for single cell capacity estimation can also be used for a module or pack that has parallel-connected cells.
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.
•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.
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.
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.
•Analytical capacity model for mixed highway traffic with stochastic and heterogeneous headways.•Consideration of headway realizations in different CAV technology scenarios.•Consideration of the full ...spectra of CAV market penetration rates and platooning intensities.•A lane management model to determine the optimal number of CAV dedicated lanes with an analytical solution approach.•Draw managerial insights into impacts of various parameter settings on mixed traffic capacity.
The projected rapid growth of the market penetration of connected and autonomous vehicle technologies (CAV) highlights the need for preparing sufficient highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). This study proposes an analytical capacity model for highway mixed traffic based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. We identify sufficient and necessary conditions for the mixed traffic capacity to increase (or decrease) with CAV market penetration rate and platooning intensity. These theoretical results caution scholars not to take CAVs as a sure means of increasing highway capacity for granted but rather to quantitatively analyze the actual headway settings before drawing any qualitative conclusion. This analytical framework further enables us to build a compact lane management model to efficiently determine the optimal number of dedicated CAV lanes to maximize mixed traffic throughput of a multi-lane highway segment. This optimization model addresses varying demand levels, market penetration rates, platooning intensities and technology scenarios. The model structure is examined from a theoretical perspective and an analytical approach is identified to solve the optimal CAV lane number at certain common headway settings. Numerical analyses illustrate the application of this lane management model and draw insights into how the key parameters affect the optimal CAV lane solution and the corresponding optimal capacity. This model can serve as a useful and simple decision tool for near future CAV lane management.
The implementation of an accurate and low computational demanding state-of-health (SOH) estimation algorithm represents a key challenge for the battery management systems in electric vehicle (EV) ...applications. In this article, we investigate the suitability of the incremental capacity analysis (ICA) technique for estimating the capacity fade and subsequently the SOH of LMO/NMC-based EV lithium-ion batteries. Based on calendar aging results collected during 11 months of testing, we were able to relate the capacity fade of the studied batteries to the evolution of four metric points, which were obtained using the ICA. Furthermore, the accuracy of the proposed models for capacity fade and SOH estimation was successfully verified considering two different aging conditions.
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