The volatility of solar energy, geographic location, and weather factors continues to affect the stability of photovoltaic power generation, reliable and accurate photovoltaic power prediction methods not only effectively reduce the operating cost of the photovoltaic system but also provide reliable data support for the energy scheduling of the light storage microgrid, improve the stability of the photovoltaic system, and provide important help for the optimization operation of the photovoltaic system. Therefore, it is an important study to find reliable photovoltaic power prediction methods. In recent years, researchers have improved the accuracy of photovoltaic power generation forecasting by using deep learning models. Compared with the traditional neural network, the Transformer model can better learn the relationship between weather features and has good stability and applicability. Therefore, in this paper, the transformer model is used for predicting ultra-short-term photovoltaic power generation, and the photovoltaic power generation data and weather data in Hebei are selected. In the experiment, the prediction result of the transformer model was compared to the GRU and DNN models to show that the transformer model has better predictive ability and stability. Experimental results demonstrated that the proposed Transformer model outperforms the GRU model and DNN model by a difference of about 0.04 kW and 0.047 kW in the MSE value, and 22.0% and 29.1% of the MAPE error. In addition, the public DC competition dataset is selected for control experiments to demonstrate the general applicability of the transformer model for PV power prediction in different regions.

High maintenance costs and safety risks due to lithium-ion battery degeneration have significantly and seriously restricted the application potential of batteries. Thus, this paper proposes an efficient calculation approach for state of health (SOH) estimation in lithium-ion batteries that can be implemented in battery management system (BMS) hardware. First, from the variables of the charge profile, only the complete voltage data is taken as the input to represent the complete aging characteristics of the batteries while limiting the computational complexity. Then, this paper combines the light gradient boosting machine (LightGBM) and weighted quantile regression (WQR) methods to learn a nonlinear mapping between the measurable characteristics and the SOH. A confidence interval is applied to quantify the uncertainty of the SOH estimate, and the model is called LightGBM-WQR. Finally, two public datasets are employed to verify the proposed approach. The proposed LightGBM-WQR model achieves high accuracy in its SOH estimation, and the average absolute error (MAE) of all cells is limited to 1.57%. In addition, the average computation time of the model is less than 0.8 ms for ten runs. This work shows that the model is effective and rapid in its SOH estimation. The SOH estimation model has also been tested on the edge computing module as a possible innovation to replace the BMS bearer computing function, which provides tentative solutions for online practical applications such as energy storage systems and electric vehicles.

Accurate prediction of battery remaining useful life (RUL) under various operating conditions is essential for battery management systems to evaluate battery reliability, reduce the risk of battery usage and provide a rationale for battery maintenance. However, RUL prediction is a challenging problem since battery degradation is a nonlinear process and is influenced by external factors. In order to improve the prediction speed and accuracy, the research proposes a new Li-ion batteries RUL prediction method based on temporal pattern attention-based, which can take into account the influence of different variables for prediction. To model time-invariant patterns across multiple time steps, it combines a gated recurrent unit (GRU), a convolutional neural network, and an attention mechanism. Battery capacity, impedance and temperature are taken as input to train the model. Experiments are validated on public datasets and the results are compared with state of art methods. The experimental results show that the proposed method achieves the lowest MAE with 8.99, which proves the effectiveness of the method.

Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method’s high efficiency and speed.