State of Health Estimation for Lithium-Ion Batteries Using Enhanced Whale Optimization Algorithm for Feature Selection and Support Vector Regression Model

As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction.

Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression

The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA). Although meticulous experimental studies are needed to determine a rock’s CWA, predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR) models were created: a traditional SVR, two ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR). Results show that all models have acceptable performance in predicting the CWA with R2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods.

A State of Health (SOH) estimation of lithium-ion batteries is a core function of battery management systems, directly affecting the safe operation, lifetime prediction, and economic efficiency of batteries. However, existing methods still face challenges in balancing feature robustness and model generalization ability; for instance, some studies rely on features whose physical correlation with SOH lacks strict verification, or the models struggle to simultaneously capture the temporal dynamics of health factors and nonlinear mapping relationships. To address this, this paper proposes an SOH estimation method based on incremental capacity (IC) curves and a Temporal Convolutional Network—Relevance Vector Machine (TCN-RVM) model, with core innovations reflected in two aspects. Firstly, five health factors are extracted from IC curves, and the strong correlation between these features and SOH is verified using both Pearson and Spearman coefficients, ensuring the physical rationality and statistical significance of feature selection. Secondly, the TCN-RVM model is constructed to achieve complementary advantages. The dilated causal convolution of TCN is used to extract temporal local features of health factors, addressing the insufficient capture of long-range dependencies in traditional models; meanwhile, the Bayesian inference framework of RVM is integrated to enhance the nonlinear mapping capability and small-sample generalization, avoiding the overfitting tendency of complex models. Experimental validation is conducted using the lithium-ion battery dataset from the University of Maryland. The results show that the mean absolute error of the SOH estimation using the proposed method does not exceed 0.72%, which is significantly superior to comparative models such as CNN-GRU, KELM, and SVM, demonstrating higher accuracy and reliability compared with other models.

The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0–1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.

The online estimation of battery SOH (state of health) and prediction of RUL (remaining useful life) are a prerequisite of ensuring its safe and reliable operation, optimal balance and battery SOC estimation. Considering the actual situation, a relatively stable charging environment is more suitable for accurate estimation and prediction of SOH and RUL compared to uncertain discharge conditions. A health indicator based on charging characteristics is selected to predict battery degradation trends through Pearson and Spearman correlation analysis. The grid search optimisation (GS) algorithm and support vector regression (SVR) are integrated to extract the intrinsic relationship between the health indicator and capacity, combining GS algorithm to optimise SVR kernel parameters. With the Lithium-ion data set provided by NASA, it’s proved that the GS-SVR fusion model is more effective in estimating battery life decay than the SVR model, with a reduction of 52.64% in mean absolute error and 68.51% mean square error. Besides, the R 2 of the GS-SVR model is 97.3%, and the robustness of the model is stronger. The RUL prediction results demonstrate the effectiveness and advantage. Highlights The methods of obtaining indirect health indicators were discussed in detail. Data-driven algorithm based on small samples to realise online model estimation. The estimation results based on GS-SVR model and SVR model were compared. Application to data taken from the Lithium-ion battery cycle aging test of the NASA’s Prognostics Center of Excellence.

Aquaponics is a growing industry that combines intensive food production with waste-stream recycling and water conservation, offering alternative solutions to soil degradation and water scarcity. This technique can contribute to global food security but requires careful management. One of the key parameters in aquaponics is pH, which must be maintained to accommodate three different types of living organisms: fish, plants, and bacteria. In aquaponics systems, pH naturally decreases due to the nitrification process, making monitoring essential. To predict pH levels in a small-scale aquaponics system—consisting of three hydroponic techniques (DWC, media bed culture, and NFT) combined with a tilapia fish tank—three machine learning models were proposed in this study. The results showed that the random forest regressor model can predict pH fluctuations over 12 days with a root mean square error (RMSE) of 0.0260 and a mean squared error (MSE) of 0.0006. The random forest model outperformed the MLP regressor and SVR models in terms of accuracy, suitability, and prediction error. Predicting pH is crucial for the stability of an aquaponics system.

Battery state of health (SOH) estimation is a prerequisite for battery health management and is vital for second-life utilization. Existing techniques implemented in well-controlled experimental conditions fail to reflect complex working conditions during actual vehicular operation. In this article, a novel SOH estimation method for battery systems in real-world electric vehicles (EVs) is presented by combing results of regional capacity calculation and a light gradient boosting machine (LGBM) model. The LGBM model is used to capture the relationship between battery degeneration and influential factors based on datasets from real-world EVs. The regional capacity, which is calculated through incremental capacity analysis with a Gaussian smoothing filter, is utilized to reflect the battery degradation level while ensuring high flexibility and applicability. Accumulated mileage, average charging current, average charging temperature, and start and end of SOC values are chosen as influential factors for model establishment. The effectiveness, complexity, superiority, and robustness of the proposed method are verified using data from real-world EVs. Results indicate accurate SOH estimation can be achieved with an average absolute error of only 0.89 Ah, where the MAPE and RMSE of the test vehicles are 2.049% and 1.153%, respectively.

Evaluation of data driven models for river suspended sediment concentration modeling

With the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitoring of LIBs has emerged as critical to safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial application and computational cost still need to be improved further. In contrast, this article carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature, and other data that can be obtained online. Then, the optimized Gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, by combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum MAPE of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this article is helpful for realizing online SOH estimation in battery management systems.

Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

Nonlinear QSAR models with high-dimensional descriptor selection and SVR improve toxicity prediction and evaluation of phenols on Photobacterium phosphoreum

Evaluation of machine learning algorithms in the prediction of hydraulic conductivity and soil moisture at the Brazilian Savannah

It is necessary to develop general and efficient models for the representation of vapor-liquid equilibria (VLE) for the binary mixtures containing R1234ze(E) which will be new alternatives to conventional refrigerants. This work investigates the applicability of four machine learning models, including K-nearest neighbor (KNN), support vector regression (SVR), random forests (RF), and multi-layer perceptron (MLP), in representing VLE for 10 binary mixtures. The accuracy, stability, computational complexity and extrapolation ability of these machine learning models are analyzed and compared with two thermodynamic models including Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR). The results show the KNN, RF and MLP models are not suitable to accurately represent the experimental pressure and vapor-phase mole fraction. The SVR model is the most accurate machine learning model in representing the experimental pressure with a mean absolute relative error of 0.71%, basically as accurate as the PR and SVR models. Meanwhile, the SVR model describes the experimental vapor-phase mole fraction more accurately with a mean absolute error of 0.0049 compared to the PR and SVR models. In addition, the SVR model has a low running-time cost and a certain extent of extrapolation ability.

A stacking ensemble model for predicting soil organic carbon content based on visible and near-infrared spectroscopy