Capacity Estimation Method Research Articles

Capacity estimation method of lithium-ion batteries based on deep convolution neural network

Capacity estimation method of lithium-ion batteries based on deep convolution neural network

A Flexible Battery Capacity Estimation Method Based on Partial Voltage Curves and Polynomial Fitting

A Flexible Battery Capacity Estimation Method Based on Partial Voltage Curves and Polynomial Fitting

Capacity estimation method of lithium-ion batteries based on deep convolution neural network

Capacity estimation method of lithium-ion batteries based on deep convolution neural network

A novel capacity and initial discharge electric quantity estimation method for LiFePO4 battery pack based on OCV curve partial reconstruction

LiFePO4 batteries are widely used in electric vehicles (EVs) and battery energy storage systems (BESSs). However, the consistencies in capacity, internal resistance, and open circuit voltage (OCV) limit the battery pack performance, which makes it difficult to evaluate the state information of the cells effectively without disassembling the pack. Therefore, this paper proposes a battery capacity and initial discharge electric quantity (DEQ) estimation method for series-connected battery packs based on partial reconstruction of the OCV curve. The aging characteristics of each cell are extracted based on incremental capacity analysis (ICA), which is taken as the transformation law for the OCV curve considering aging. And then the multipopulation genetic algorithm (MPGA) is utilized to optimize the transformation coefficients and obtain the reconstructed OCV curve of each cell in the pack. Finally, the actual capacity and initial DEQ of batteries at a low current rate are estimated according to the reconstructed complete OCV curve and optimized OCV curve transformation coefficients. The experimental results show that the estimation error of capacity is less than 5%, and the maximum estimation error of the initial DEQ is 5.56%.

Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

Battery Capacity Estimation Based on Incremental Capacity Analysis Considering Charging Current Rate

Incremental capacity analysis (ICA) is widely used in the battery decay mechanism analysis since the features of battery incremental capacity (IC) curves are closely related to battery aging and maximum available capacity. However, the traditional ICA method to estimate battery capacity mainly focuses on a single charging condition, and the influence of charging current on IC curves is ignored. In this paper, an adaptive capacity estimation method based on ICA considering the charging current is established. First, the charging experiments using different charging current rates under different battery aging statuses are designed and conducted. Then, the relationship between battery maximum available capacity, IC curve features, and charging current is investigated. Furthermore, the fitting method and data-driven method considering charging current are proposed and compared. Finally, the capacity estimation results prove the accuracy and adaptability of the proposed method.

Lithium-ion battery capacity estimation based on open circuit voltage identification using the iteratively reweighted least squares at different aging levels

In traditional incremental SOC (stage of charge)-capacity method, errors of the selected SOC points inevitably lead to inaccurate capacity estimation. In this paper, a capacity estimation method based on OCV (open circuit voltage) identification and IRLS (iteratively reweighted least squares) algorithm method is proposed. Firstly, the accuracy and complexity of three parameter identification schemes of first-order RC model at different aging levels are studied with the forgetting factor recursive least squares algorithm. Through the online identified OCV by the optimal scheme, the full interval SOC estimation can be obtained indirectly. Finally, the capacity is estimated using the full interval SOC estimation with IRLS algorithm, which avoids the randomness of the SOC point selection, and solves the problem of possible outliers in the SOC estimation or the unknown variance of the random error of the SOC gain. Through the designed aging experiment, the capacity estimation accuracy is verified under different life conditions. The results show that the estimation accuracy of this method is high and the error does not exceed 2.5%.

Investigating Behavior of Six Methods for Sediment Transport Capacity Estimation of Spatial-Temporal Soil Erosion

Estimation of sediment transport capacity (STC) plays a crucial role in simulating soil erosion using any physics-based models. In this research, we aim to investigate the pros and cons of six popular STC methods (namely, Shear velocity, Kilinc-Richardson (KR), Effective stream power, Slope and unit discharge, Englund-Hansen (EH), and Unit stream power) for soil erosion/deposition simulation at watershed scales. An in-depth analysis was performed using the selected STC methods integrated into the Grid Surface Subsurface Hydrologic Analysis model for investigating the changes in morphology at spatial-temporal scales at the Cheoncheon watershed, South Korea, over three storm events. Conclusions were drawn as follows. (1) Due to the ability of the KR and EH methods to include an additional parameter (i.e., erodibility coefficient), they outperformed others by producing more accurate simulation results of sediment concentration predictions. The KR method also proved to be superior to the EH method when it showed a more suitable for sediment concentration simulations with a wide range of sediment size and forcing magnitude. (2) We further selected 2 STC methods among the 6 methods to deeply explore the spatial distribution of erosion/deposition. The overall results were more agreeable. For instance, the phenomenon of erosion mainly occurred upstream of watersheds with steep slopes and unbalanced initial sediment concentrations, whereas deposition typically appeared at locations with flat terrain (or along the mainstream). The EH method demonstrated the influence of topography (e.g., gradient slope) on accretionary erosion/deposition results more significantly than the KR method. The obtained results contribute a new understanding of rainfall-sediment-runoff processes and provide fundamental plans for soil conservation in watersheds.

Examining deterministic and probabilistic capacity estimation methods under mixed traffic using empirical data

The present research primarily investigates the applicability of different roadway capacity estimation methods under mixed-traffic conditions, operating with weak-lane discipline. The study also explores the feasibility and advantages of stochastic capacity estimation methods for mixed traffic scenarios on urban roads in India. Three methods, namely Traffic-stream models (calibrated empirically), Product limit method (PLM), and Sustained Flow Index (SFI), are used to estimate the capacity for five different categories of road sections. The Van-Aerde model is found to be the best representative for estimating deterministic roadway capacity. The PLM method is observed to provide the freedom of selecting desirable operational quality. And the SFI method provides the maximum sustained flow for the roadway infrastructure. As a practical outcome, a novel Level of Service monitoring framework is developed by augmenting the traditional volume-to-capacity ratio with the breakdown probabilities.

Analysis of Urban Energy Resources to Achieve Net-Zero Energy Neighborhoods

This paper summarizes a methodology developed to optimize urban-scale energy mix. An optimal capacity estimation method based on energy credits is proposed, the objective of which is to plan renewable and alternative energy sources to yield zero (or positive) year-end energy credits. Several renewable and alternative energy resources are considered, including photovoltaic systems, solar thermal collectors, wind turbines, waste to energy (WtE) potential, as well as thermal seasonal storage. The methodology employs several energy simulation and optimization tools, including Energy Plus, TRNSYS and MATLAB. The optimization employs a non-linear process that uses objective function, boundaries and non-linear/linear constraints as input. The methodology is demonstrated on a hypothetical mixed-use neighborhood, designed to achieve high-energy performance objectives, with three scenarios of energy operations: 1) all electric, 2) all-electric except for DHW, and 3) DHW and space heating arenon-electric. The pilot location of this mixed-use neighborhood, including residential and commercial buildings, is Calgary (AB, Canada). For the all-electric scenario, PV systems implemented in all available south facing roof areas together with a limited number of wind turbines can achieve NZE status. For the other two scenarios, solar thermal collectors coupled to borehole thermal storage (STC and BTES) need to be considered. Although in all cases of the considered scenarios waste-based energy is not required, it can be used to shave the peak electric load, reducing the stress on the grid. This methodology can be employed for the design of an integrated urban energy systems, in different neighborhood designs, to achieve energy self-sufficient, or energy positive status.

Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model

Accurate residual capacity estimation of retired LiFePO4 batteries is critically important for second-use applications but is challenging with multiple aging pathways and nonlinear degradation mechanisms. In this study, a fast and accurate residual capacity estimation method based on the mechanism and data-driven model is developed with two main contributions. First, as the basis of the residual capacity estimation model, three new health indicators directly related to the capacity loss mechanism are derived from the prognostic and mechanism model using the Levenberg-Marquardt method and Spearman correlation. Second, residual capacity tests were conducted on 1000 retired batteries to establish a data-driven model for residual capacity estimation based on the proposed health indicators, guaranteeing better universality and estimation accuracy for different types of retired LiFePO4 batteries. To establish a data-driven model for the residual capacity estimation, an improved moth–flame optimization and support vector regression method is used; the adaptive weight and Levy flight are introduced in the moth–flame optimization algorithm to prevent the local optimal value. The residual capacity estimation results are compared with the results from three other typical methods and input health indicators. The results show that the root mean square error of the proposed method is within 2.18% using only the first 10% of the data, a smaller error than with the other methods. A fast and accurate residual capacity estimation method for retired batteries can reduce the cost and improve the development for second-use applications.

누적 전하량을 이용한 VRFB 시스템의 용량 추정에 관한 연구

VRFB is emerging as a new alternative to ESS batteries applied to renewable power generation systems to solve environmental and resource depletion problems. Currently, lithium batteries are mainly used in ESS, but there is a problem that a fire accident may occur and the lifespan is limited. VRFB has the advantage of being suitable for large-capacity systems because it has a longer lifespan than lithium batteries and has a capacity proportional to the amount of electrolyte. However, there is a disadvantage in that it is difficult to estimate the capacity due to the operating characteristics. Therefore, in this paper, we propose a capacity estimation method by applying the accumulated charge index of VRFB. In addition, the current integration method using the estimated capacity is presented, and the SOC estimation performance results using the long-term charge/discharge cycle experimental data are presented.

Screening of Retired Lithium-Ion Batteries Using Incremental Capacity Charging Curve-Based Residual Capacity Estimation Method for Facilitating Sustainable Circular Lithium-Ion Battery System

Abstract The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. In this study, we used the incremental capacity (IC) curve to estimate the residual capacity of waste power batteries. First, through experimental means, the parameters of the battery and the IC charging curve are measured. Second, to achieve rapid capacity estimation, a battery capacity estimation method based on the adaptive genetic algorithm-back propagation neural network (AGA-BPNN) is proposed and compared with other classic machine learning methods. The proposed algorithm reduced the error of capacity estimation to 3%. Finally, through the analysis of the IC curve, a method for identifying aging mechanism of large-scale decommissioned batteries is obtained. This research provides effective support for the capacity-based classification of large-scale decommissioned power batteries.

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

ESTIMATION METHOD OF LEFT TURN LANE CAPACITY UNDER THE INFLUENCE OF PEDESTRIANS AND CROSSWALK LAYOUT AT SIGNALIZED CROSSWALKS

The left turn lane capacity is highly influenced by the presence of pedestrians under shared signal phasing. Pedestrian crossing time can be highly influenced by pedestrian platoon maneuver under the influence of traffic signal settings, crosswalk length and bidirectional interactions of pedestrian, due to that left turning vehicles movement may be significantly affected. However, existing capacity estimation methods of left turn lanes did not consider those factors. In addition, the influence of crosswalk layout did not properly consider in the existing methods and researches. This study aims to propose a method to estimate the left turn lane capacity, considering the characteristics of crossing pedestrians and crosswalk layout variations. Waiting pedestrian presence-time is estimated by applying pedestrian presence probability model. In addition, the left turning vehicles discharge flow rate under different crosswalk layout and pedestrian demand condition are investigated and modeled. The left turn lane capacity estimation equation is proposed by characterizing the observed discharge flow rate of left turning vehicles under the influence of crossing pedestrians. It was found that the proposed capacity estimation method can represent the realistic influence of pedestrians and crosswalk layout on the left turn lane capacity at signalized crosswalks.

Density saliency for clustered building detection and population capacity estimation

Building detection is a critically important task in the field of remote sensing and it is conducive to urban construction planning, disaster survey, shantytown modification, and emergency landing, it etc. However, few studies have focused on the task of the clustered building detection which is inescapable and challenging for some relatively low space resolution images. The appearance structures of those buildings are not clear enough for the single-building detection. Whereas, it has been found that the distributions of clustered buildings are mostly dense and cellular, while the backgrounds are not. This clue will be beneficial to the clustered building detection. Motivated by the fact above and other similar density estimation applications, this work mainly focuses on the information mining mechanism of dense and cellular structure. Firstly, we propose a concept of Clustered Building Detection (CBD), which contributes to develop clustered building detection techniques of remote sensing images. Secondly, a saliency estimation algorithm is proposed to mine the prior information for the clustered buildings. Thirdly and most notably, combining with the CBD and the density saliency map, a Population Capacity Estimation (PCE) method is presented. The PCE can be easily used to estimate the population carrying capacity of certain areas and future applied for national land resource management. Moreover, a Clustered Building Detection Dataset (CBDD) from Gaofen-2 satellite is annotated and contributed for the public research. The experimental results by the representative detection algorithms manifest the effectiveness for the clustered building detection.

A method for capacity estimation of lithium-ion batteries based on adaptive time-shifting broad learning system

Accurate capacity estimation of lithium-ion batteries can improve the safety and reliability of equipment. Deep learning provides a new approach for capacity estimation. However, it is still difficult to adapt to the changes in different stages of capacity degradation due to many and complex parameters in deep structure network and the time-invariant model parameters. Considering that Broad Learning System is a neural network independent of deep structure, which has a simple single hidden layer structure and fewer parameters, this paper proposes an Adaptive Time-shifting Broad Learning System (ATBLS) capacity estimation model. For input layer, the input data of each time step is combined with the output of previous time step to get a new input, so as to provide local information to hidden unit of the network and provide guidance for local parameters. For hidden layer, the hidden unit of each time step is weighted-fused with the hidden unit of previous time step to update the parameters, so as to reflect the long-term dynamics of sequence. The experiment are conducted on three different data sets. And the effectiveness of ATBLS is verified by comparing with other methods.

Sequent extended Kalman filter capacity estimation method for lithium-ion batteries based on discrete battery aging model and support vector machine

Precise battery capacity estimation plays an important role in the future intelligent battery management system. In this paper, a fusion estimation method based on support vector machine and discrete battery aging model is put forward to enhance the online capacity estimation accuracy of lithium-ion batteries under variable temperature conditions. During the constant current charging process, the support vector machine is developed to estimate the battery capacity, which first trains a single 18650 battery offline and then tests the accuracy of the model using two other batteries of the same type intermittently. Subsequently, the discrete aging model of the battery is proposed to continuously estimate the capacity of the battery. However, unmodelled dynamics between battery aging model and real physical battery is easily occur in the process of modeling, which affects the accuracy and robustness of the model. Therefore, a sequent extended Kalman filter algorithm is deployed for solving the problem. The first Kalman filter takes the identified value of support vector machine as observation value to update the model parameters of discrete battery aging model. The second Kalman filter fuses the identified value of support vector machine and the discrete battery aging model after updating model parameters to improve the precision of online battery capacity estimation. The experimental results indicate that the proposed discrete battery aging model and support vector machine have good applicability, and the algorithm used can online modify the parameters of the model. When the model parameters are modified four times, the fusion estimation error is less than 2%.

Practical state estimation using Kalman filter methods for large-scale battery systems

The system states of battery energy storage systems (BESSs) such as state of charge (SOC) and state of health (SOH) are essential for the functions of the system, such as frequency support services and energy trading. However, the complexity of a large-scale battery system makes the estimations more difficult than at the cell-level. This is further compounded by real-world limitations on system monitoring data granularity, accuracy and quality. In this paper it is shown how cell-level state estimation techniques can be utilised on large-scale BESSs using experimental data from a 2MW, 1MWh BESS. The results show how a Dual Sigma point Kalman Filter (DSPKF) SOC estimation provides more accurate results compared to the commercial BESS battery management system SOC. It is shown how the DSPKF parameters can be tuned by a genetic algorithm to simplify selection and generalise the approach for different BESSs. Furthermore, it shows how this method of SOC estimation can be combined with a total least-squares (TLS) method for capacity estimation to less than 1% error. Online system state estimation is demonstrated using both designed tests and real-world operational profiles where the BESS has provided contracted frequency response services to the national electricity grid in the UK.

Fault Identification and Quantitative Diagnosis Method for Series-Connected Lithium-Ion Battery Packs Based on Capacity Estimation

Internal short circuit is considered as one of the general causes that may lead to battery thermal runaway. The capacity of cells ages with the effect of working conditions. Hence, both micro-short circuit (MSC) and low-capacity cells may exist in a battery pack. However, both two faults perform the same features in the discharging process: state of charge (SOC) deviation increases continuously. If we diagnosed the abnormal states only based on the discharging data, it would misdiagnose these two faults. A fault identification method based on capacity estimation is proposed to distinguish MSC and low-capacity cells in the article. Furthermore, the capacity of the low-capacity cell and the MSC cell could be quantitatively estimated. In the proposed method, a mean-difference model and extended Kalman filter algorithm are used to calculate the cell SOCs in the battery pack. An online capacity estimation method is adopted to estimate the cell capacities in the charging and discharging process. A reasonable threshold considering capacity change characteristics is established to initially identify the fault and for further quantitative diagnosis. The experimental results show that a coexisting MSC fault and low-capacity fault in the battery packs could be diagnosed effectively by using the proposed method.