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Accurate estimation of the state of health (SOH) is crucial for the safe and stable operation of lithium-ion batteries. However, since the labeled values may contain non-Gaussian noise which may lead to a decrease in the effectiveness of traditional data-driven based estimation methods. Therefore, a novel robust stacking integrated learning model (ILM) is proposed to enable accurate SOH estimation under non-Gaussian noises (or outliers) conditions. The mixture correntropy loss (MCL) is used in original extreme learning machine (ELM) and the gated recurrent unit (GRU) frameworks to develop novel robust learning models, i.e MCL-ELM and MCL-GRU, and they are used as the sub-models of the proposed ILM, which takes into account the generalization performance of the model and the overall trend of SOH over the time series. In addition, the entropy weighting method that can well reflect the differences between the sub-models is utilized to reduce the complexity of the stacked ILM. Furthermore, the local tangent space alignment is used to capture the local relationships between different loops to enhance the effectiveness of the extracted health features. Several numerical experiments are performed under different cases to validate the estimation effect of the proposed model by using two publicly available datasets, and the experimental results demonstrate that the maximum RMSE, MAE, and MAPE values of the proposed model are 1.391%, 0.932%, and 1.480%, respectively, under non-Gaussian noises conditions. • Robust ELM and GRU models using MCL are developed. • Novel robust stacking ILM via the combination of the MCL-ELM and MCL-GRU is proposed for SOH estimation. • The EWM is used to obtain the output results of multiple base learners in the proposed ILM. [ABSTRACT FROM AUTHOR]

Kalman filters (KFs) are widely used for state-of-charge (SOC) estimation of Li-ion batteries due to their excellent dynamic tracking capability. Especially the cubature KF (CKF), with the computational efficiency and nonlinear processing ability, is an outstanding candidate for SOC estimation. However, the actual working conditions are complex and changeable, and the measurement data is usually accompanied by non-Gaussian noise (outliers). Therefore, the performance of the original CKF with minimum mean square error (MMSE) criterion may be degraded seriously in these cases. In order to enhance the robustness of CKF, the MMSE in the CKF framework is substituted by the generalized maximum correntropy criterion (GMCC), and thus a robust CKF with GMCC (GMCC-CKF) is developed by fixed point iteration approach in this work. Furthermore, a SOC estimation model via the GMCC-CKF is proposed to improve estimation accuracy under non-Gaussian noise environments. The simulation results show that, compared with the traditional KFs, the proposed GMCC-CKF can accurately estimate the SOC of lithium batteries under different temperatures and operating conditions considering non-Gaussian noise interference. The results of mean absolute error (MAE) and root mean square error (RMSE) are less than 1%, which verifies the excellent performance of GMCC-CKF. • GMCC is introduced into the CKF framework to develop a novel robust CKF. • The proposed GMCC-CKF method is applied for robust SOC estimation under non-Gaussian noise interferences. • Some simulations under different conditions are performed to evaluate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, various wind power forecasting methods have been developed to improve wind power utilization. Most of these techniques are designed based on the mean square error (MSE) loss, which are very suitable for the assumption that the error distribution obeys the Gaussian distribution. However, there are many outliers in real wind power data due to many uncertain factors such as weather, temperature, and other random factors. Meanwhile, the highly nonlinear process of converting wind energy into wind power may changes the statistical characteristics of errors. Therefore, the prediction model established based on the traditional MSE loss may lead to unsatisfactory results. As a result, a robust short-term wind power hybrid forecasting model based on Long Short-term Memory (LSTM) neural network with Correntropy combining an improved variational mode decomposition (IVMD) and Sample Entropy (SE) is proposed in this work. The IVMD in which the parameter K in the IVMD is determined by the Maximum Correntropy Criterion (MCC) is used to decompose the original wind power data and the decomposed subseries is reconstructed by SE to improve the prediction efficiency. Then the MCC is also utilized to replace the MSE in the classic LSTM network to develop a novel robust hybrid model to forecast the wind power. Finally, four experiments were conducted using real data from two wind farms in China at different sampling intervals to evaluate the effectiveness of the proposed method, and the results show that proposed method is more effective than other traditional methods. • An adaptive VMD decomposition algorithm with MCC is proposed. • A new data preprocessing model is proposed by combining with the sample entropy. • The LSTM network with MCC loss is proposed in the forecasting process. • A novel IVMD-SE-MCC-LSTM model is proposed for short-term wind power forecasting. • Experiments are performed to evaluate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) plays a crucial role in battery management system, which directly reflects the usage of the battery. Recently the extreme learning machine (ELM) model as a data-driven method has been utilized to estimate SOC due to its simple single hidden layer structure and fast learning performance. The battery management system, however, may usually work in complex working conditions, which means that the non-Gaussian complex noise (or outliers) interference problem may exist in some measurement data for model training. So the performance of the classical ELM with mean square error (MSE) criterion may be degraded under this case. This work considers Non-Gaussian noise interference issue, the MSE in ELM is substituted by mixture generalized maximum correntropy criterion (MGMCC), and a novel robust ELM model is developed to improve the SOC estimation capability which mainly relies on the stable and robust nonlinear similarity characteristics of the MGMCC. A data set from a Panasonic 18,650 battery cell is used to verify the robustness of the proposed model, the experiment results demonstrate that it can achieve better estimation performance in terms of different evaluation metrics compared with the traditional methods. • A mixture GMCC is defined which can be as a learning criterion for NN model. • A robust ELM with MGMCC is developed to improve robust performance of the traditional ELM. • The MMGCCELM based SOC estimation approach is proposed to improve estimation accuracy under non-Gaussian noise interferences. [ABSTRACT FROM AUTHOR]