Open-circuit-voitage (OCV) data is widely used for characterizing battery properties under different conditions. It contains important information that can help to identify battery state-of-charge ...(SOC) and state-of-health (SOH). While various OCV models have been developed for battery SOC estimation, few have been designed for SOH monitoring. In this paper, we propose a unified OCV model that can be applied for both SOC estimation and SOH monitoring. Improvements in SOC estimation using the new model compared to other existing models are demonstrated. Moreover, it is shown that the proposed OCV model can be used to perform battery SOH monitoring as it effectively captures aging information based on incremental capacity analysis (ICA). Parametric analysis and model complexity reduction are also addressed. Experimental data is used to illustrate the effectiveness of the model and its simplified version in the application context of SOC estimation and SOH monitoring.
Many service systems display nonstationary demand: the number of customers fluctuates over time according to a stochastic—though to some extent predictable—pattern. To safeguard the performance of ...such systems, adequate personnel capacity planning (i.e., determining appropriate staffing levels and/or shift schedules) is often crucial. This paper provides a state-of-the-art literature review on staffing and scheduling approaches that account for nonstationary demand. Among references published during 1991–2013, it is possible to categorize relevant contributions according to system assumptions, performance evaluation characteristics, optimization approaches and real-life application contexts. Based on their findings, the authors develop recommendations for further research.
•We provide a literature review on staffing and scheduling approaches for nonstationary demand.•We categorize articles according to system assumptions and performance metrics.•We categorize articles based on optimization approach and application context.•We develop recommendations to achieve a better integration of theory and practice.
Non-orthogonal multiple access (NOMA), millimeter wave (mmWave), and massive multiple-input-multiple-output (MIMO) have been emerging as key technologies for fifth generation mobile communications. ...However, less studies have been done on combining the three technologies into the converged systems. In addition, how many capacity improvements can be achieved via this combination remains unclear. In this paper, we provide an in-depth capacity analysis for the integrated NOMA-mmWave-massive-MIMO systems. First, a simplified mmWave channel model is introduced by extending the uniform random single-path model with angle of arrival. Afterward, we divide the capacity analysis into the low signal to noise ratio (SNR) and high-SNR regimes based on the dominant factors of signal to interference plus noise ratio. In the noise-dominated low-SNR regime, the capacity analysis is derived by the deterministic equivalent method with the Stieltjes-Shannon transform. In contrast, the statistic and eigenvalue distribution tools are invoked for the capacity analysis in the interference-dominated high-SNR regime. The exact capacity expression and the low-complexity asymptotic capacity expression are derived based on the probability distribution function of the channel eigenvalue. Finally, simulation results validate the theoretical analysis and demonstrate that significant capacity improvements can be achieved by the integrated NOMA-mmWave-massive-MIMO systems.
Battery health prognostics management is an important prerequisite for ensuring its safe use. As the battery is charging and discharging, its capacity deteriorates. Incremental capacity (IC) analysis ...is a common tool to analyse the degradation process based on the change rate of capacity relative to voltage. However, measuring battery voltage and current is difficult and there are inherent errors. This paper improves the IC curve from the perspective of time series, extracts a health indicator (HI), and uses the hybrid prediction model to predict the remaining useful life (RUL). Firstly, we convert the traditional IC-voltage (IC-V) curve to the IC-time (IC-T) curve. The time series corresponding to the peak of the improved curve is extracted. Secondly, the extracted degradation HI is predicted using grey forecasting model, and a probabilistic and iterative hybrid battery prediction method is established combined with Gaussian process regression. Finally, public datasets are used to validate the proposed method at different starting points and comparisons with other prediction methods are carried out. Results show that the HI based on the improved IC curve is highly correlated with the degradation process and the proposed method can provide an accurate result, which verifies its effectiveness and rationality.
•This paper extracts the time corresponding to the improved IC-T curve peak as a new HI.•An iterative structure and probabilistic prediction method for RUL is established.•Grey forecasting model and Gaussian process regression are combined for RUL prediction.•Validation of the proposed method at different starting points for RUL is performed.
As a green and renewable energy source, biomass energy has the potential to solve environmental pollution and resource shortage. The utilization of the straw as a feedstock for the production of ...clean energy gases and notably methane, via anaerobic processes, has garnered substantial interest within the scientific community. Nevertheless, the intricacies inherent in the biomass synthesis system and the practical constraints associated with experimental operations pose challenges in developing a precise predictive model for the yield estimation when working with limited sample data. Therefore, a novel production prediction method using the synthetic minority oversampling technique (SMOTE) algorithm incorporating an Attention-Enhanced convolutional long short-term memory (SMOTE-ACL) is proposed. The SMOTE algorithm is utilized to extend the original data to build the training and test sets. Then, an embedding layer enhances the local features to higher dimensions, by using a convolutional neural network (CNN) for the feature extraction. Subsequently, the long short-term memory (LSTM) augmented with an attention mechanism is utilized for the temporal prediction and derives the prediction result through the fully-connected layer. Finally, the SMOTE-ACL method is applied to predict the unit production of the straw bioconversion for response conditions optimization. The SMOTE-ACL method integrating local and global information improves the ability to model multidimensional time series data, and achieves the best prediction accuracy than the radial basis function (RBF) neural network, the multilayer perceptron (MLP), the CNN, the recurrent neural network (RNN) and the LSTM. Meanwhile, it is of guiding significance for real-time monitoring of experiments and optimizing the plant production.
Display omitted
•The original data of straw reforming is expanded using the SMOTE.•Novel production forecasting model based on the SMOTE-ACL is proposed.•The proposed method can achieve state-of-the-art performance.•Response conditions of straw reforming can be optimized based on the proposed method.
Using an equivalent circuit model to characterize the constant-current part of a charging/discharging profile, a model is developed to estimate the state-of-health of lithium ion batteries. The model ...is an incremental capacity analysis-based model, which applies a capacity model to define the dependence of the state of charge on the open circuit voltage as the battery ages. It can be learning-free, with the parameters subject to certain constraints, and is able to give efficient and reliable estimates of the state-of-health for various lithium ion batteries at any aging status. When applied to a fresh LiFePO4 cell, the state-of-health estimated by this model (learning-unrequired or learning-required) shows a close correspondence to the available measured data, with an absolute difference of 0.31% or 0.12% at most, even for significant temperature fluctuation. In addition, NASA battery datasets are employed to demonstrate the versatility and applicability of the model to different chemistries and cell designs.
•An ICA-based model is proposed to estimate SOH of LIBs.•The ICA-based model can be learning-required or learning-unrequired.•This model can give reliable estimates of SOH for various LIBs at any aging status.•The accuracy of the model is validated by experimental results of LFP and NCA batteries.
This work presents a comprehensive degradation study of two types of large lithium-ion pouch cells; 26 NMC532/Graphite (64 Ah) and 9 NMC433/Graphite (31 Ah) pouch cells. The cells were degraded under ...different cycling conditions and periodically characterized at room temperature. Specifically, the effect of different ambient temperatures and constraining the cells by clamping was studied. Incremental capacity analysis is an in situ, non-invasive characterization technique that allows the identification of battery degradation modes, and is a technique that does not require additional and advanced equipment. Therefore, in this study we also look into applying the analysis technique on an existing data set. This is done by combining incremental capacity analysis on a qualitative level with the tracking of features of interest in the incremental capacity curve as a function of State of Health and utilizing the simulation of different degradation modes for a more in-depth analysis. We combine simulation and experimental incremental capacity analysis with conclusions from capacity loss and resistance changes with a focus on understanding the benefit and limitations of the incremental capacity analysis for large cells. This is important, as incremental capacity analysis is a relatively fast analysis to qualify large commercial batteries for 2nd life applications. Specifically in this study, we found that degradation and capacity loss do not always correlate. For the 64 Ah Cells cycled at 15 °C and 25 °C, the rate of capacity loss appeared to be similar, although the degradation modes and mechanisms are found to be very different. The clamping was the most important factor for impeding degradation. The 31 Ah Cell cycled at low temperatures showed a very poor cycling performance, where the incremental capacity analysis revealed that Loss of Lithium Inventory from fast and irreversible plating was responsible.
Battery state of health (SOH) monitoring has become a crucial challenge in hybrid electric vehicles (HEVs) and all electric vehicles (EVs) research, as SOH significantly affects the overall vehicle ...performance and life cycle. In this paper, we focus on the identification of Li-ion battery capacity fading, as the loss of capacity and therefore the driving range is a primary concern for EV and plug-in HEV (PHEV). While most studies on battery capacity fading are based on laboratory measurement such as open circuit voltage (OCV) curve, few publications have focused on capacity loss monitoring during on-board operations. We propose a battery SOH monitoring scheme based on partially charging data. Through analysis of battery aging cycle data, a robust signature associated with battery aging is identified through incremental capacity analysis (ICA). Several algorithms to extract this signature are developed and evaluated for on-board SOH monitoring. The use of support vector regression (SVR) is shown to provide the most consistent identification results with moderate computational load. For battery cells tested, we show that the SVR model built upon the data from one single cell is able to predict the capacity fading of 7 other cells within 1% error bound.
► An on-board battery state-of-health (SOH) monitoring framework is proposed. ► Capacity loss and therefore SOH can be monitored by using partially charging data. ► Support vector regression algorithm is used for robust aging signature extraction. ► Established a quantitative correlation to predict capacity fade with high accuracy.
Conventional state of health (SOH) estimation often requires capacity measurement from battery's full charge or discharge profile between fully charged state and cut-off state. Incremental capacity ...analysis can improve estimation efficiency by extracting features to estimate SOH or recalibrate state of charge estimation without using full profile. While direct numerical derivatives often do not show smooth result due to measurement noise, this paper utilizes robust cubic smoothing spline method on producing incremental capacity curve, which is superior over typical filters that require tuning on window size usually by trial&error because smoothing parameters in the proposed method can be determined by cross validation. Comparison through simulated data shows that the proposed method maintains good fidelity on data and feature of interest with low RMSE values under derivative form. This paper also proposes a peak height ratio feature for SOH estimation. While a linear relationship is noted between SOH and peak height ratio, estimation of SOH from peak height ratio is demonstrated using linear regression. A more generalized version of SOH estimation method is also demonstrated using multiple linear regression with covariates of both peak height ratio and the height of peak associated with "last phase-transition of Li ions intercalation during charging."
The rate and shape of the charging current indubitably affect the charging time and the ageing rate of a battery. Depending on the application requirements, it is possible to use high-charging ...current in order to decrease the charging time. However, the influence of fast-charging current profiles should be investigated to identify their impact on battery functionality over time. In this article, static and dynamic fast-charging current profiles are applied to a high power 7 Ah LiFePO4-based cells (LFP), and the results of cycle-life and characterization tests are discussed. To select the proper fast-charging profile, the evaluation relies on some factors: discharge capacity retention, charging capacity, charging time, and cell temperature. After 1700 cycles, the results revealed that the dynamic fast-charging current profile has a prominent role in decreasing the degradation rate as well as the charging time of cells compared with the static fast-charging profile.
•Different static and dynamic fast charging methodologies have been tested and analyzed.•Battery impedance representation using the equivalent circuit.•The aged cells have been subjected to the incremental capacity analysis (ICA) to identify the degradation effect.•Investigate the impact of the fast charging methodology on the battery's lifetime.•An extended analysis to select the proper charging method that can be used to design an enhanced charging system.
PDF
