State-of-charge (SoC) estimation is indispensable for battery management systems (BMSs). Accurate SoC estimation can improve the efficiency of battery utilization, especially for electric vehicles (EVs). Several kinds of battery SoC estimation approaches have been developed, but a simple and efficient method for battery SoC estimation that can adapt to a variety of lithium-ion batteries is worth exploring. To this end, a recurrent neural network (RNN) model based on a gated recurrent unit (GRU) is presented for battery SoC estimation. The GRU-RNN model can rapidly learn its own parameters by means of an ensemble optimization method based on the Nadam and AdaMax optimizers. The Nadam optimizer is used in the model pre-training phase to find the minimum optimized value as soon as possible, and then the AdaMax optimizer is used in the model fine-tuning phase to further determine the model parameters. To validate the effectiveness and robustness of the proposed method, the GRU-RNN model was trained and tested with three kinds of dynamic loading profiles and compared with existing SoC estimation methods. The experimental results show that the proposed method dramatically reduces the model training time and increases estimation accuracy. INDEX TERMS Lithium-ion batteries, state of charge, gated recurrent unit, ensemble optimizer. BIN XIAO received the M.S. degree in computer application technology from the Hunan University of Science and Technology. He is currently pursuing the Ph.D. degree in control theory and control engineering with the School of Automation Science and Engineering, South China University of Technology. His current research interests include power lithium battery state estimation and intelligent detection technology.

The state of health (SoH) is a key indicator of a battery management system (BMS). Accurate SoH estimation can be adopted to guide the timely recovery and ladder utilization for lithium-ion batteries (LiBs), which is particularly beneficial to environmental protection. Although many battery SoH estimation algorithms have been developed, there are few simple and easy-to-use methods for on-site rapidly measurement. Therefore, in this paper, a model for battery SoH estimate is realized by least-square support vector regression (LS-SVR) configured with radial basis function (RBF) kernel. Based on the hysteresis behavior of LiB, data samples can be quickly obtained by the hybrid pulse power characteristic (HPPC) test. The grey correlation analysis (GRA) was conducted to select features of data samples, and the K-fold cross-validation and grid search (GS) were performed to optimize the hyperparameters of the estimation model LS-SVR. Finally, to verify the proposed method, data samples collected from 18 650 LiB with different aging degrees were used for LS-SVR model training and testing, and the method was compared to existing SoH estimation methods. Experimental results demonstrate that the SoH estimation model only requires some short-term data of a battery to achieve highprecision SoH estimation, which shows that this method has broad application prospects.

The state of health is an indicator of battery performance evaluation and service lifetime prediction, which is essential to ensure the reliability and safety of electric vehicles. Although a large number of capacity studies have emerged, there are few simple and effective methods suitable for engineering practice. Hence, a least square support vector regression model with polynomial kernel function is presented for battery capacity estimation. By the battery charging curve, the feature samples of battery health state are extracted. The grey relational analysis is employed for the feature selection, and the K-fold cross-validation is adopted to obtain hyper-parameters of the support vector regression estimation model. To validate this method, the support vector regression estimation model was trained and tested on the battery data sets provided by NASA Prognostics Center of Excellence. The experimental results show that the proposed method only needs some battery feature data, and can achieve high-precision capacity estimation, which indicates that the proposed method shows great efficiency and robustness.

Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational management requires the development and refinement of a system for evaluating teaching quality. Traditional approaches to assessing teaching quality, on the other hand, are problematic due to their limitations. As a result, a scientific and reasonable model for evaluating the teaching quality of college undergraduate teachers must be developed. We present a unique model for evaluating the quality of classroom teaching in colleges and universities, which is based on improved genetic algorithms and neural networks. The basic idea is to use adaptive mutation genetic algorithms to refine the initial weights and thresholds of the BP neural network. The teaching quality evaluation findings were improved by improving the neural network’s prediction accuracy and convergence speed, resulting in a more practical scheme for evaluating college and university teaching quality. We have conducted simulation experiments and comparative analysis, and the mean square error of the results of the proposed model is very low, which proves the effectiveness and superiority of the algorithm.

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