One of the major issues hampering the acceptance of electric vehicles (EVs) is the anxiety associated with long charging time. Hence, the ability to fast charging lithium-ion battery (LIB) systems is ...gaining notable interest. However, fast charging is not tolerated by all LIB chemistries because it affects battery functionality and accelerates its aging processes. Here, we investigate the long-term effects of multistage fast charging on a commercial high power LiFePO4-based cell and compare it to another cell tested under standard charging. Coupling incremental capacity (IC) and IC peak area analysis together with mechanistic model simulations (‘Alawa’ toolbox with harvested half-cell data), we quantify the degradation modes that cause aging of the tested cells. The results show that the proposed fast charging technique caused similar aging effects as standard charging. The degradation is caused by a linear loss of lithium inventory, coupled with a less degree of linear loss of active material on the negative electrode. This study validates fast charging as a feasible mean of operation for this particular LIB chemistry and cell architecture. It also illustrates the benefits of a mechanistic approach to understand cell degradation on commercial cells.
•Aging analysis of LiFePO4 cells cycled under standard and fast charge schemes.•A high-fidelity mechanistic model was used to quantify the degradation modes.•Similar cycle aging effects were found under both cycling schemes.•Degradation is caused by a linear loss of Li inventory and loss of active material.•Fast charging attained safe recharges without excess capacity fading from cycling.
Accurate capacity estimation is crucial and challenging for guaranteeing safety and durability of Li-ion batteries. This work proposes a data-fusion-model method to estimate battery capacity using ...the partial charging curve. First, during the charging process, two representative battery ageing features are extracted from the partial incremental capacity curve smoothed by Locally Weighted Scatterplot Smoothing (LOWESS). Second, the dual Gaussian process regressions (GPRs) are employed to establish a data-driven battery ageing state-space representation, which takes battery capacity as the state variable and takes two ageing features as the input variables. Third, combining with the GPR-based battery state-space representation, Particle Filter (PF) is introduced to suppress the measurement noises and a Gaussian Process Particle Filter (GPPF) is built to estimate battery capacity. Meanwhile, the output capacity is fed back to the GPPF to update the battery ageing state-space representation. Finally, ageing experiments are conducted to validate the effectiveness of the proposed method. Meanwhile, GPR is implemented with the cycle and two ageing features for capacity estimation as a contrast. The results show that the proposed GPPF method can provide more accurate and robustness battery estimation results than GPR.
Accurate prediction of remaining useful life (RUL) and management for sodium-ion batteries have great significance, since they are promising for implementation as large-scale energy storage plants in ...renewable energy systems. In this paper, 18650 sodium-ion batteries are investigated. The observed data from the cycle life test has been used to examine the oxidation process and aging mechanisms based on incremental capacity analysis (ICA). Moreover, the Gaussian process regression (GPR) is established for accurate RUL prediction. The negative electrode half-cell with hard carbon, positive electrode half-cell with Na3·2V1·8Zn0·2(PO4)3, coin cells with Na3·2V1·8Zn0·2(PO4)3 vs hard carbon, and 18650 cells with Na3·2V1·8Zn0·2(PO4)3 vs hard carbon are analysed based on ICA. The oxidation process of vanadium (V3+→V4+) corresponding to the incremental capacity peak is selected to extract six potential health indicators. To reduce redundant information among various features, the principal component analysis is utilized to obtain the syncretic health indicator. The GPR is established for reliable prediction with a 95% confidence interval. When compared to the traditional methods, the proposed method can achieve higher accuracy in RUL prediction with a root mean square error below 1.16%.
•The first attempt to predict the remaining useful life of 18,650 sodium-ion batteries.•Oxidation process and aging mechanism of sodium-ion batteries are analysed.•Six potential indicators are selected and the syncretic indicator is obtained.•Gaussian process regression is built for degradation model of sodium-ion batteries.
The state of health (SoH) of electric vehicle (EV) batteries is important for the EV owner and potential buyer of second hand EVs. The incremental capacity analysis (ICA) has by several researchers ...proven to be a promising SoH estimation method for lithium-ion batteries. However, in order to be practical useable, the method needs to be feasible on a pack or EV level and not only on an individual cell level. Therefore, the purpose of this article is to demonstrate the feasibility of the ICA method on real EVs. Nickel manganese cobalt (NMC) cells used in BMW i3 EVs and lithium manganese oxide (LMO) used in Nissan Leaf EVs have been tested both on the cell level and on car level. The results are consistent and the characteristic peaks and valleys of the ICA on car level match with the same on cell level. A root-mean-square error of 1.33% and 2.92% has been obtained for the SoH estimation of the NMC and LMO type, respectively. It is therefore concluded that the ICA method is also applicable to the car level for battery SoH estimation.
In order to meet the requirements of massively connected devices, different quality of services (QoS), various transmit rates, and ultra-reliable and low latency communications (URLLC) in ...vehicle-to-everything (V2X) communications, we introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized V2X system model. We, then, classify the V2X communications into two scenarios and give their exact capacity expressions. To solve the computation complicated problems of the involved exponential integral functions, we give the approximate closed-form expressions with arbitrary small errors. Numerical results indicate the validness of our derivations. Our analysis has that the accuracy of our approximate expressions is controlled by the division of <inline-formula> <tex-math notation="LaTeX">\frac {\pi }{2} </tex-math></inline-formula> in the urban and crowded scenarios, and the truncation point <inline-formula> <tex-math notation="LaTeX">{T} </tex-math></inline-formula> in the suburban and remote scenarios. Numerical results manifest that: 1) increasing the number of V2X device, NOMA power, and Rician factor value yields a better capacity performance; 2) effect of FD-NOMA is determined by the FD self-interference and the channel noise; and 3) FD-NOMA has a better latency performance compared with other schemes.
Accurately estimating the capacity of lithium-ion batteries in electric vehicles (EVs) is critical for making correct management decisions. However, the randomness of the charging voltage range of ...EVs can lead to missing observations or reduced accuracy of capacity estimation methods. This article proposes an adaptive battery capacity estimation method suitable for arbitrary charging voltage range based on incremental capacity (IC) analysis and data-driven techniques. All charging conditions of EVs are divided into three categories according to the charging voltage range. Three data-driven estimation submethods with sequential application priority are designed for the three charging conditions separately, including back-propagation neural network with IC peak coordinates as input, ensemble learning with local high IC curve as input, and linear regression with ampere-hour coordinate transformation. The method is based on a priori knowledge to select a suitable estimation submethod under different charging conditions, so as to improve the adaptability. Experimental data is collected from eight commercial lithium-ion battery modules for model establishment and verification. Over 250 000 experimental samples at different states of health and random charging ranges show that the method can accurately estimate battery capacity under arbitrary charging conditions, with a maximum error of 2%.
Efficient battery capacity estimation is of utmost importance for safe and reliable operations of electric vehicles (EVs). This article 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%, while maintaining low computational cost.
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.
This article proposes an adaptive state-of-health (SOH) estimation method for lithium-ion (Li-ion) batteries using machine learning. Practical problems with feature extraction, cell inconsistency, ...and online implementability are specifically solved using a proposed individualized estimation scheme blending offline model migration with online ensemble learning. First, based on the data of pseudo-open-circuit voltage measured over the battery lifespan, a systematic comparison of different incremental capacity features is conducted to identify a suitable SOH indicator. Next, a pool of candidate models, composed of slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are trained offline. For online operation, the prediction errors due to cell inconsistency in the target new cell are then mitigated by a proposed modified random forest regression (mRFR)-based ensemble learning process with high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably improved SOH estimation accuracy (15%) while only a small amount of early-age data and online measurements are needed for practical operation. Furthermore, the applicability of the proposed SBC-RBFNN-mRFR algorithms to real-world operation is validated using measured data from electric vehicles, and it is shown that a 38% improvement in estimation accuracy can be achieved.
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