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.
•Random forest regression is proposed for on-line battery capacity estimation.•The estimation is developed from partial charging voltage-capacity data.•Two features indicative of battery capacity ...fade are extracted from charging curves.•An incremental capacity analysis is used for assisting battery feature selection.
Machine-learning based methods have been widely used for battery health state monitoring. However, the existing studies require sophisticated data processing for feature extraction, thereby complicating the implementation in battery management systems. This paper proposes a machine-learning technique, random forest regression, for battery capacity estimation. The proposed technique is able to learn the dependency of the battery capacity on the features that are extracted from the charging voltage and capacity measurements. The random forest regression is solely based on signals, such as the measured current, voltage and time, that are available onboard during typical battery operation. The collected raw data can be directly fed into the trained model without any pre-processing, leading to a low computational cost. The incremental capacity analysis is employed for the feature selection. The developed method is applied and validated on lithium nickel manganese cobalt oxide batteries with different ageing patterns. Experimental results show that the proposed technique is able to evaluate the health states of different batteries under varied cycling conditions with a root-mean-square error of less than 1.3% and a low computational requirement. Therefore, the proposed method is promising for online battery capacity estimation.
The state of health for lithium battery is necessary to ensure the reliability and safety for battery energy storage system. Accurate prediction battery state of health plays an extremely important ...role in guaranteeing safety and minimizing maintenance costs. However, the complex physicochemical characteristics of battery degradation cannot be obtained directly. Here a novel Gaussian process regression model based on the partial incremental capacity curve is proposed. First, an advanced Gaussian filter method is applied to obtain the smoothing incremental capacity curves. The health indexes are then extracted from the partial incremental capacity curves as the input features of the proposed model. Additionally, the mean and the covariance function of the proposed method are applied to predict battery state of health and the model uncertainty, respectively. Four aging datasets from NASA data repository are employed for demonstrating the predictive capability and efficacy of the degradation model using the proposed method. Besides, different initial health conditions of the tested batteries are used to verify the robustness and reliability of the proposed method. Results show that the proposed method can provide accurate and robust state of health estimation.
•Gaussian filter method is proposed to smooth the incremental capacity curves.•A novel battery health prognostic method based on partial incremental capacity curve is proposed. .•The health indexes are extracted from partial incremental capacity curves as model input features.•Gaussian progress regression is applied to establish the battery degradation model based on battery health indexes.•Four batteries from NASA database are used to verify the robustness of the proposed method.
Prognostic and health management of lithium batteries is a multi-faceted approach that can provide crucial indexes for guaranteeing the reliability and safety of the energy storage system. Herein, a ...novel multi-time-scale framework is proposed that focuses on short-term battery state of health estimation and long-term remaining useful lifetime prediction. The proposed method extracts four significant features through in-depth analysis of partial incremental capacity and Gaussian process regression with nonlinear regression is applied to forecasting battery health conditions. First, the advanced signal filter methods are employed to smooth initial incremental capacity curves. After that, the significant feature variables are extracted from different degrees such as intercept, slope and peak by linear fitting the partial incremental capacity curves. Second, the significant feature variables feed to Gaussian process regression to establish a short-term battery degradation model using kernel-modified Gaussian process regression. Third, an autoregressive long-term battery prediction model is established by combining the offline short-term battery model with nonlinear regression. The predictive capability, robustness and effectiveness of proposed methods are verified using four datasets with different cycling test conditions and health levels. The results show that the proposed method can give accurate battery health conditions forecasting.
•Multi-timescale framework is established for forecasting battery SOH and RUL.•The health features are extracted from partial IC curves from different dimensions.•A nonlinear regression RUL model is developed by using the GPR-based model.•Four batteries are used to verify and evaluate the proposed method.
Degradation of Lithium-ion batteries is a complex process that is caused by a variety of mechanisms. For simplicity, ageing mechanisms are often grouped into three degradation modes (DMs): ...conductivity loss (CL), loss of active material (LAM) and loss of lithium inventory (LLI). State of Health (SoH) is typically the parameter used by the Battery Management System (BMS) to quantify battery degradation based on the decrease in capacity and the increase in resistance. However, the definition of SoH within a BMS does not currently include an indication of the underlying DMs causing the degradation. Previous studies have analysed the effects of the DMs using incremental capacity and differential voltage (IC-DV) and electrochemical impedance spectroscopy (EIS). The aim of this study is to compare IC-DV and EIS on the same data set to evaluate if both techniques provide similar insights into the causes of battery degradation. For an experimental case of parallelized cells aged differently, the effects due to LAM and LLI were found to be the most pertinent, outlining that both techniques are correlated. This approach can be further implemented within a BMS to quantify the causes of battery ageing which would support battery lifetime control strategies and future battery designs.
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•Degradation modes (DMs) evaluated within parallel connected cells.•A novel method to quantify the effect of DMs using EIS and IC-DV is presented.•LLI, LAM are the most pertinent DMs obtained with each technique.•The effect of the DMs obtained with EIS and IC-DV are correlated.•On-board implementation of EIS and IC-DV within a BMS is discussed.
This paper proposes a novel and computationally efficient estimation algorithm for lithium-ion battery state of health (SoH) under the hood of incremental capacity analysis. Concepts of regional ...capacity and regional voltage are introduced to develop an SoH model against experimental cycling data from four types of batteries. In the obtained models, SoH is a simple linear function of the regional capacity, and the R-square of linear fitting is up to 0.948 for all the considered batteries with properly selected regional voltage. The proposed method without using characteristic parameters directly from incremental capacity curves is insensitive to noise and filtering algorithms, and is effective for common current rates, where rates of up to 1C have been demonstrated. Then, a model-based SoH estimator is designed and shown to be capable of closely matching battery's aging data from NASA, with the error less than 2.5%. Furthermore, such a small scale of error is achieved in the absent of state of charge and impedance which are often used for SOH estimation in available methods.
•A new SoH estimation algorithm has been proposed for lithium-ion batteries.•Concepts of regional capacity and voltage are introduced based on ICA.•SoH models are developed as linear functions of the regional capacity.•Experimental results show that the estimation errors are less than 2.5%.
Precisely battery state of health estimation and remaining useful lifetime prediction are crucial factors in ensuring the reliability and safety for system operation. This paper thus focuses on the ...short-term battery state of health estimation and long-term battery remaining useful lifetime prediction. A novel hybrid method by fusion of partial incremental capacity and Gaussian process regression is proposed and dual Gaussian process regression models are employed to forecast battery health conditions. First, the initial incremental capacity curves are filtered by using the advanced signal process technology. Second, the important health feature variables are extracted from partial incremental capacity curves using correlation analysis method. Third, the Gaussian process regression is applied to model the short-term battery SOH estimation using the feature variables. Forth, an autoregressive long-term battery remaining useful lifetime model is established using the results of battery SOH values and previous output. The predictive capability and effectiveness of two models are demonstrated by four battery datasets under different cycling test conditions. Otherwise, the robustness of the two models is verified using four datasets with different health levels. The experimental results show that the proposed method can provide accurate battery state of health estimation and remaining useful lifetime.
•Dual GPR-based models are proposed to establish battery degradation models.•The health indexes are extracted from partial IC curves as model input features.•Correlational coefficient analysis method is applied to extract feature variables.•An autoregressive RUL model is developed using the capacity vs. cycle number.•Four batteries with different initial health levels are used to verify robustness.
State of health (SOH) estimation of lithium-ion batteries is a key but challengeable technique for the application of electric vehicles. Due to the ambiguous aging mechanisms and sensitivity to the ...applied conditions of lithium-ion batteries, the recognition of aging mechanisms and SOH monitoring of the battery might be difficult. A novel SOH estimation and aging mechanism identification method is presented in this paper. First, considering the dispersion effect, a fractional-order model is constructed, and the parameter identification approach is proposed, and a comparison between integer-order model and fractional-order model has been done from the prospect of predicting accuracy. Then, based on the identified open-circuit voltage, the battery aging mechanism can be analyzed by the means of an incremental capacity analysis method. Moreover, the normalized incremental capacity peak is used to estimate the remaining capacity. Finally, the robustness of the SOH estimation method is validated by batteries aged at different conditions based on the idea of cross validation, and the estimation error of the remaining capacity can be reduced within 3.1%.
•Used custom three-electrode pouch cells with built-in reference electrodes to determine the causes of the knee point.•Proved the degradation mode of the knee point — accelerated cathode resistance ...increase.•Identified the knee point mechanism to be accelerated degradation in cathode interfacial kinetics, potentially caused by the separator-side cathode surface layer formation.
The capacity of lithium-ion batteries decreases during usage (cycling) and storage (rest). After some initial charge-discharge cycling, the capacity fade rate has been observed to increase, and the capacity fade curve visibly bends, the onset of which is described as a knee point. The occurrence of the knee point above the end-of-life capacity threshold leads to a shorter life than expected based on the initial capacity fade rate. Although various degradation mechanisms and their effects on lithium-ion batteries are generally known, the degradation mechanisms for the knee point phenomenon have been in contention in the literature. In this paper, aging tests are conducted on custom three-electrode lithium-ion pouch cells to distinguish the contribution of all degradation modes to the cell's capacity fade and determine the one that causes the knee point, which is the acceleration of cathode resistance increase due to the increased degradation rate in cathode interfacial kinetics. Incremental capacity analysis and destructive analysis were further conducted to determine the degradation mechanisms that lead to the acceleration of cathode resistance increase.
An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries is proposed and validated in this work. It describes the open circuit voltage as a function of the ...state-of-charge by a polynomial of high degree, with a lumped thermal model to account for the effect of temperature. When applied for practical use, the model requires a prior learning from the initial charging or discharging data for the sake of parameter identification, using e.g. a nonlinear least squares method, but it is undemanding to implement. The study shows that the model is able to estimate the state-of-health of a LiFePO4 cell cycled under conditions where the temperature has fluctuated significantly with a relative error less than 0.45% at most. A short part of a constant current profile is enough for state-of-health estimation, and the effect of size and location of voltage window on the model's accuracy is also studied. In particular, the reason of accuracy change with different voltage windows is explained by incremental capacity analysis. Additionally, the versatility and flexibility of the model to different chemistries and cell designs are demonstrated.
•An open circuit voltage-based model is proposed to estimate the SOH of LIBs.•The accuracy of the model is validated by experimental results of an LFP cell.•The effect of size and location of voltage window on the model accuracy is studied.•ICA is used to explain the model accuracy change with different voltage window.•The versatility of the model to different chemistries is demonstrated.
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