State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

An Investigation of the Effects of Wave State and Sea Spray on an Idealized Typhoon Using an Air-Sea Coupled Modeling System

In this study, the impact of atmospherewave coupling on typhoon intensity was investigated using numerical simulations of an idealized typhoon in a coupled atmospherewaveocean modeling system. The coupling between atmosphere and sea surface waves considered the effects of wave state and sea sprays on airsea momentum flux, the atmospheric lowlevel dissipative heating, and the wavestateaffected sea spray heat flux. Several experiments were conducted to examine the impacts of wave state, sea sprays, and dissipative heating on an idealized typhoon system. Results show that considering the wave state and seasprayaffected seasurface roughness reduces typhoon intensity, while including dissipative heating intensifies the typhoon system. Taking into account sea spray heat flux also strengthens the typhoon system with increasing maximum wind speed and significant wave height. The overall impact of atmospherewave coupling makes a positive contribution to the intensification of the idealized typhoon system. The minimum central pressure simulated by the coupled atmospherewave experiment was 16.4 hPa deeper than that of the control run, and the maximum wind speed and significant wave height increased by 31% and 4%, respectively. Meanwhile, within the area beneath the typhoon center, the average total upward airsea heat flux increased by 22%, and the averaged latent heat flux increased more significantly by 31% compared to the uncoupled run.

Sea State Bias Estimation with Least Absolute Shrinkage and Selection Operator(LASSO)

With the sea-level rising,the measurement of sea surface height(SSH) is attracting more and more attention in the area of oceanography.Satellite radar altimeter is usually used to measure the SSH.However,deviation between the measured value and the actual one always exists.Among others,the sea state bias(SSB) is a main reason to cause the deviation.In general,one needs to estimate SSB first to correct the measured SSH.Currently,existing SSB estimation methods more or less have shortcomings,such as with many parameters,high prediction error and long training time.In this paper,we introduce an effective and efficient linear model called LASSO to the SSB estimation.The LASSO algorithm minimizes the residual sum of squares under the condition that the sum of the absolute values of each coefficient is less than a certain constant.In the implementation of LASSO,we use the significant wave height and wind speed to fit the LASSO model.Hence,the applied model has only 3 parameters,corresponding to the two inputs and a bias.Experimental results on the data of JASON-2,JASON-3,T/P and HY-2 radar altimetry show that LASSO performs better than geophysical data records(GDR) and ordinary least squares(OLS) estimator.Moreover,from the running time,we can see that LASSO is very efficient.

The Influence of Wave State and Sea Spray on Drag Coefficient from Low to High Wind Speeds

Ocean waves alter the roughness of sea surface,and sea spray droplets redistribute the momentum flux at the air-sea interface.Hence,both wave state and sea spray influence sea surface drag coefficient.Based on the new sea spray generation function which depends on sea surface wave,a wave-dependent sea spray stress is obtained.According to the relationship between sea spray stress and the total wind stress on the sea surface,a new formula of drag coefficient at high wind speed is acquired.With the analysis of the new drag coefficient,it is shown that the drag coefficient reduces at high wind speed,indicating that the sea spray droplets can limit the increase of drag coefficient.However,the value of high wind speed corresponding to the initial reduced drag coefficient is not fixed,and it depends on the wave state,which means the influence of wave cannot be ignored.Comparisons between the theoretical and measured sea surface drag coefficients in field and laboratory show that under different wave ages,the theoretical result of drag coefficient could include the measured data,and it means that the new drag coefficient can be used properly from low to high wind speeds under any wave state condition.

Nonparametric estimations of the sea state bias for a radar altimeter

To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.

Optimal Stiffness for Flexible Connectors on A Mobile Offshore Base at Rough Sea States

This paper investigates a simplified method to determine the optimal stiffness of flexible connectors on a mobile offshore base(MOB) during the preliminary design stage. A three-module numerical model of an MOB was used as a case study. Numerous constraint forces and relative displacements for the connectors at rough sea states with different wave angles were utilized to determine the optimized stiffness of the flexible connectors. The range of optimal stiffnesses for the connectors was obtained based on the combination and intersection of the optimized stiffness results, and the implementation steps were elaborated in detail. The percentage reductions of the optimized and optimal stiffness of the flexible connector were determined to quantitatively evaluate the decreases of the constraint force and relative displacement of the connectors compared with those calculated by using the original range of the connector stiffnesses. The results indicate the accuracy and feasibility of this method for determining the optimal stiffness of the flexible connectors and demonstrate the rationality and practicability of the optimal stiffness results. The research ideas, calculation process, and solutions for the optimal stiffness of the flexible connectors of an MOB in this paper can provide valuable technical support for the design of the connectors in similar semisubmersible floating structures.

Trace-Norm Regularized Multi-Task Learning for Sea State Bias Estimation

Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where one model is built on the data from only one radar altimeter.In this paper,taking account of the data from multiple radar altimeters available,we introduced a multi-task learning method,called trace-norm regularized multi-task learning(TNR-MTL),for SSB estimation.Corresponding to each individual task,TNR-MLT involves only three parameters.Hence,it is easy to implement.More importantly,the convergence of TNR-MLT is theoretically guaranteed.Compared with the commonly used STL models,TNR-MTL can effectively utilize the shared information between data from multiple altimeters.During the training of TNR-MTL,we used the JASON-2 and JASON-3 cycle data to solve two correlated SSB estimation tasks.Then the optimal model was selected to estimate SSB on the JASON-2 and the HY-270-71 cycle intersection data.For the JSAON-2 cycle intersection data,the corrected variance(M)has been reduced by 0.60 cm^2 compared to the geophysical data records(GDR);while for the HY-2 cycle intersection data,M has been reduced by 1.30 cm^2 compared to GDR.Therefore,TNR-MTL is proved to be effective for the SSB estimation tasks.

THE DEPENDENCE OF WIND STRESS ON SEA STATE

Based on up to date literature, this paper details the evolution of wave dependence of wind stress.Some typical models of the dependence of wind stress on waves are described in detail. Although there isno universally accepted theory and model, recent studies indicate that the wind strees strongly dependson the development state of sea waves, i. e., young seas are rougher than mature seas, in other words, thewind stress decreases with increasing wave age.

INTERDECADAL SEA LEVEL VARIATION AT THE JAPANESE COAST AND LARGE SCALE CLIMATE STATE

Thirty years of monthly mean anomalies of sea level(SL) at 15 Japanese coastal stations, sea sur-face temperature (SST) and sea level pressure (SLP) in or over the northern Pacific were analyzed bycanonical correlation analysis (CCA) to study the relationship between the interdecadal SL variationand large scale climate state. Given two time-varying fields this technique identifies the pair ofspacial patterns with optimally correlated time series.The results show that there are two important air-sea interactive processes in the extratropicalPacific region for the variation of the SL at the Japanese coast on interdecadal scale. One is theocean heating or cooling of the atmosphere over the Kuroshio extension region, which results in ahuge SLP anomalous vortex with planetary spacial scale big enough to change the global climate. An-other is the large Kuroshio meander phenomenon controlled by the large-scale wind-stress curls oneyear earlier in the adjacent region of the Hawaiian Islands. The first process

Wave Period Distributions in Non-Gaussian Mixed Sea States

The wave period probability densities in non-Gaussian mixed sea states are calculated by utilizing a transformed Gaussian process method. The transformation relating the non-Gaussian process and the original Gaussian process is obtained based on the equivalence of the level up-crossing rates of the two processes. A saddle point approximation procedure is applied for calculating the level up-crossing rates in this study. The accuracy and efficiency of the transformed Gaussian process method are validated by comparing the results predicted by using the method with those predicted by the Monte Carlo simulation method.

Statistical Analyses of Sea State Conditions in South China Sea

The statistical characterization of sea conditions in the South China Sea(SCS) was investigated by analyzing a 30-year(1976–2005) numerically simulated daily wave height and wind speed data. The monthly variation of these parameters shows that wave height and wind speed have minimum values of 0.54 m and 4.15 ms^(-1), respectively in May and peak values of 2.04 m and 8.12 ms^(-1), respectively in December. Statistical analysis of the daily wave height and wind speed and the subsequent characterization of the annual, seasonal and monthly mean sea state based on these parameters were also done. Results showed that, in general, the slight sea state prevails in the SCS and has nearly the highest occurrence in all seasons and months. The moderate sea condition prevails in the winter months of December and January while the smooth(wavelets) sea state prevails in May. Furthermore, spatial variation of sea states showed that calm and smooth sea conditions have high occurrences(25%–80%) in the southern SCS. The slight sea condition shows the largest occurrence(25%–55%) over most parts of the SCS. High occurrences(8%–17%) of the rough and very rough seas distribute over some regions in the central SCS. Sea states from high to phenomenal conditions show rare occurrence(<12%) in the northern SCS. The calm(glassy) sea condition shows no occurrence in the SCS.

An Improved Method of Retrieving Sea Surface Wind Speed Based on a Four-Layer Medium Model at High Sea States

Considering about the effect of whitecaps and foams on pulse-limited Radar Altimeters, an improved algorithm of retrieving sea surface wind speed is proposed in this paper. Firstly, a four-layer dielectric model is established in order to simulate an air-sea interface. Secondly, the microwave reflectivity of a sea surface covered by spray droplets and foams at 13.5 GHz is computed based on the established model. These computed results show that the effect of spray droplets and foams in high sea state conditions shall not be negligible on retrieving sea surface wind speed. Finally, compared with the analytical algorithms proposed by Zhao and some calculated results based on a three-layer dielectric model, an improved algorithm of retrieving sea surface wind speed is presented. At a high wind speed, the improved algorithm is in a better accord with some empirical algorithms such as Brown, Young ones and et al., and also in a good agreement with ZT and other algorithms at low wind speed. This new improved algorithm will be suitable not only for low wind speed retrieval, but also for high wind speed retrieval. Better accuracy and effectiveness of wind speed retrieval can also be obtained.

Estimating the evolution of sea state non-Gaussianity based on a phase-resolving model

The occurrence of rogue waves is closely related to the non-Gaussianity of sea states,and this non-Gaussianity can be estimated using corresponding two-dimensional wave spectra.This paper presents an approach to non-Gaussianity estimation based on a phase-resolving model called the high-order spectral method(HOSM).Based on numerous HOSM simulations,a set of precalculated non-Gaussianity indicators was established that could be applied to real sea states without any calibration of spectral shapes.With a newly developed extraction approach,the indicators for given two-dimensional wave spectra could then be conveniently extracted from the precalculated dataset.The feasibility of the newly developed approach in a real wave environment is verified.Using the estimation approach,phase-resolved non-Gaussianity can now be illustrated throughout the evolution of sea states of interest,not just at a few specific times;and the level of non-Gaussianity at any time in a duration can be identified according to the statistics(e.g.,quantities)of the phase-resolved indicators,that are obtained throughout the duration concerned.

Device-Free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.

Short-Term and Long-Term Price Forecasting Models for the Future Exchange of Mongolian Natural Sea Buckthorn Market

Sea buckthorn market floated uncertainly within a narrow range. The market situation provided upward pressure on prices, and producer and consumer interest were poor, coupled with weak prices in the regional markets. The objectives of the study are: 1) to estimate the relationship between wild Sea buckthorn (SB) price and Supply, Demand, while some other factors of crude oil price and exchange rate by using simultaneous Supply-Demand and Price system equation and Vector Error Correction Method (VECM);2) to forecast the short-term and long-term SB price;3) to compare and evaluate the price forecasting models. Firstly, the data was analyzed by Ferris and Engle-Granger’s procedure;secondly, both price forecasting methodologies were tested by Pindyck-Rubinfeld and Makridakis’s procedure. The result shows that the VECM model is more efficient using yearly data;a short-term price forecast decreases, and a long-term price forecast is predicted to increase the Mongolian Sea buckthorn market.

State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Optimizing the LSTM Deep Learning Model for Arctic Sea Ice Melting Prediction

The National Oceanic and Atmospheric Administration reports a 95% decline in the oldest Arctic ice over the last 33 years [1], while the National Aeronautics and Space Administration states that summer Arctic Sea Ice Extent (SIE) is shrinking by 12.2% per decade since 1979 due to warmer temperatures [2]. Given the rapidly changing Arctic conditions, accurate prediction models are crucial. Deep learning models developed for Arctic forecasts primarily focus on exploring convolutional neural networks (CNN) and convolutional Long Short-Term Memory (LSTM) networks, while the exploration of the power of LSTM networks is limited. In this research, we focus on enhancing the performance of an LSTM network for predicting monthly Arctic SIE. We leverage five climate and atmospheric variables, validated for their correlation with SIE in prior studies [3]. We utilize the Spearman’s rank correlation and ExtraTrees regressor to enhance our understanding of the importance of the five variables in predicting SIE. We further enhance our predictor variables with seasonal information, lagged time steps, and a linear regression simulated SIE that accounts for the influence of past SIE on current SIE. Statistical methods guide our selection of data scalers and best evaluation metrics for our model. By experimenting with hyperparameter optimization and advanced deep learning training techniques, such as batch sizes, number of neurons, early stopping, and model checkpoint, our model achieved a Mean Absolute Error (MAE) of 0.191 and R2 of 0.996, underscoring its ability to account for nearly all the variance in our data and holds great promise for the prediction of SIE.

Long-Term Characterization of Sea Conditions in the East China Sea Using Significant Wave Height and Wind Speed

In this study, the statistical characterization of sea conditions in the East China Sea(ECS) is investigated by analyzing a significant wave height and wind speed data at a 6-hour interval for 30 years(1980–2009), which was simulated and computed using the WAVEWATCH Ⅲ(WW3) model. The monthly variations of these parameters showed that the significant wave height and wind speed have minimum values of 0.73 m and 5.15 ms^(-1) and 1.73 m and 8.24 ms^(-1) in the month of May and December, respectively. The annual, seasonal, and monthly mean sea state characterizations showed that the slight sea generally prevailed in the ECS and had nearly the highest occurrence in all seasons and months. Additionally, the moderate sea prevailed in the winter months of December and January, while the smooth(wavelets) sea prevailed in May. Furthermore, the spatial variation of sea states showed that the calm and smooth sea had the largest occurrences in the northern ECS. The slight sea occurred mostly(above 30%) in parts of the ECS and the surrounding locations, while higher occurrences of the rough and very rough seas were distributed in waters between the southwest ECS and the northeast South China Sea(SCS). The occurrences of the phenomenal sea conditions are insignificant and are distributed in the northwest Pacific and its upper region, which includes the Southern Kyushu-Palau Ridge and Ryukyu Trench.

Exploitable wave energy assessment based on ERA-Interim reanalysis data—A case study in the East China Sea and the South China Sea

Wave energy resources assessment is a very important process before the exploitation and utilization of the wave energy. At present, the existing wave energy assessment is focused on theoretical wave energy conditions for interesting areas. While the evaluation for exploitable wave energy conditions is scarcely ever performed. Generally speaking, the wave energy are non-exploitable under a high sea state and a lower sea state which must be ignored when assessing wave energy. Aiming at this situation, a case study of the East China Sea and the South China Sea is performed. First, a division basis between the theoretical wave energy and the exploitable wave energy is studied. Next, based on recent 20 a ERA-Interim wave field data, some indexes including the spatial and temporal distribution of wave power density, a wave energy exploitable ratio, a wave energy level, a wave energy stability, a total wave energy density, the seasonal variation of the total wave energy and a high sea condition frequency are calculated. And then the theoretical wave energy and the exploitable wave energy are compared each other; the distributions of the exploitable wave energy are assessed and a regional division for exploitable wave energy resources is carried out; the influence of the high sea state is evaluated. The results show that considering collapsing force of the high sea state and the utilization efficiency for wave energy, it is determined that the energy by wave with a significant wave height being not less 1 m or not greater than 4 m is the exploitable wave energy. Compared with the theoretical wave energy, the average wave power density, energy level, total wave energy density and total wave energy of the exploitable wave energy decrease obviously and the stability enhances somewhat. Pronounced differences between the theoretical wave energy and the exploitable wave energy are present. In the East China Sea and the South China Sea, the areas of an abundant and stable exploitable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, east of Taiwan, China and north of Ryukyu Islands; annual average exploitable wave power density values in these areas are approximately 10-15 kW/m; the exploitable coefficient of variation （COV） and seasonal variation （SV） values in these areas are less than 1.2 and 1, respectively. Some coastal areas of the Beibu Gulf, the Changjiang Estuary, the Hangzhou Bay and the Zhujiang Estuary are the poor areas of the wave energy. The areas of the high wave energy exploitable ratio is primarily in nearshore waters. The influence of the high sea state for the wave energy in nearshore waters is less than that in offshore waters. In the areas of the abundant wave energy, the influence of the high sea state for the wave energy is prominent and the utilization of wave energy is relatively difficult. The developed evaluation method may give some references for an exploitable wave energy assessment and is valuable for practical applications.