State-of-Health (SOH) prediction of lithium-ion batteries is crucial in battery management systems. In order to guarantee the safe operation of lithium-ion batteries, a hybrid model based on convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) and attention mechanism (AM) is developed to predict the SOH of lithium-ion batteries. By analyzing the charging and discharging process of batteries, the indirect health indicator (HI), which is highly correlated with capacity, is extracted in this paper. HI is taken as the input of CNN, and the convolution and pooling operations of CNN layers are used to extract the features of battery time series data. On this basis, a BiLSTM depth model is built in this paper to collect the data coming from CNN forward and reverse dependencies and further emphasize the correlation between the serial data by AM to obtain an accurate SOH estimate. Experimental results based on NASA PCoE lithium-ion battery data demonstrate that the proposed hybrid model outperforms other single models, with the root mean square error (RMSE) of SOH prediction results all less than 0.01, and can accurately predict the SOH of lithium-ion batteries.

Accurate state of health (SOH) prediction of lithium-ion batteries is essential for battery health management. In this paper, a novel method of predicting the SOH of lithium-ion batteries based on the voltage and temperature in the discharging process is proposed to achieve the accurate prediction. Both the equal voltage discharge time and the temperature change during the discharge process are regarded as health indicators (HIs), and then, the Pearson and Spearman relational analysis methods are applied to evaluate the relevance between HIs and SOH. On this basis, we modify the relevance vector machine (RVM) to a multiple kernel relevance vector machine (MKRVM) by combining Gaussian with sigmoid function to improve the accuracy of SOH prediction. The particle swarm optimization (PSO) is used to find the optimal weight and kernel function parameters of MKRVM. The aging data from NASA Ames Prognostics Center of Excellence are used to verify the effectiveness and accuracy of the proposed method in numerical simulations, whose results show that the MKRVM method has higher SOH prediction accuracy of lithium-ion batteries than the relevant methods.

Remaining useful life (RUL) prediction is vital to provide accurate decision support for a safe power system. In order to solve capacity measurement difficulties and provide a precise and credible RUL prediction for lithium-ion batteries, two health indicators (HIs), the discharging voltage difference of an equal time interval (DVDETI) and the discharging temperature difference of an equal time interval (DTDETI), are extracted from the partial discharging voltage and temperature. Box-Cox transformation, which is data processing, is used to improve the relation grade of HIs. In addition, the Pearson correlation is employed to evaluate the relationship degree between HIs and capacity. On this basis, a local Gaussian function and a global sigmoid function are utilized to improve the multi-kernel relevance vector machine (MKRVM), whose weights are optimized by applying a whale optimization algorithm (WOA). The availability of the extracted HIs as well as the accuracy of the RUL prediction are verified with the battery data from NASA.

Prediction of photovoltaic (PV) power is extremely important in power grid which is organized in a hierarchical framework. In this paper, we propose a novel end-to-end deep network, named hierarchical graph network with adaptive reconciliation (HiGN-ARec), to predict the PV power for all levels. Specifically, HiGN-ARec consists of two parts: the base forecasts and the reconciliation. For the base forecasts, we adopt the state-of-the-art spatio-temporal (ST) modules to exploit the intra-level information and propose interaction modules to capture the valuable inter-level information. The cooperation of both modules in the base forecasts contributes to the significant enhancement of the accuracy of base forecasts. For the reconciliation, we propose a reconciliation matrix $P$ which is used to adjust the base forecasts and aggregation matrix $S$ to impose the consistency constraint on the network. The reconciliation matrix $P$ is learnable and determined with other parameters in the network via training. It incorporates knowledge about hierarchy structure and learned parameters that adjust base forecasts effectively. We use synthetic solar PV power plant data from the National Renewable Energy Laboratory (NREL) to verify the effectiveness of the proposed method. Experimental results demonstrate the superiority of our proposed approach over the competing methods.