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Lithium-ion batteries are widely used in electric vehicles, energy storage power stations, and many other applications. Accurate and reliable monitoring of battery health status and remaining capacity is the key to establish a lithium-ion cell management system. In this paper, based on a Bayesian optimization algorithm, a deep neural network is structured to evaluate the whole charging curve of the battery using partial charging curve data as input. A 0.74 Ah battery is used for experiments, and the effect of different input data lengths is also investigated to check the high flexibility of the approach. The consequences show that using only 20 points of partial charging data as input, the whole charging profile of a cell can be exactly predicted with a root-mean-square error (RMSE) of less than 19.16 mAh (2.59% of the nominal capacity of 0.74 Ah), and its mean absolute percentage error (MAPE) is less than 1.84%. In addition, critical information including battery state-of-charge (SOC) and state-of-health (SOH) can be extracted in this way to provide a basis for safe and long-lasting battery operation. [ABSTRACT FROM AUTHOR]

Battery state of health (SOH), which is a crucial parameter of the battery management system, reflects the rate of performance degradation and the aging level of lithium-ion batteries (LIBs) during operation. However, traditional machine learning models face challenges in accurately diagnosing battery SOH in complex application scenarios. Hence, we developed a deep learning framework for battery SOH estimation without prior knowledge of the degradation in battery capacity. Our framework incorporates a series of deep neural networks (DNNs) that utilize the direct current internal resistance (DCIR) feature to estimate the SOH. The correlation of the DCIR feature with the fade in capacity is quantified as strong under various conditions using Pearson correlation coefficients. We utilize the K-fold cross-validation method to select the hyperparameters in the DNN models and the optimal hyperparameter conditions compared with machine learning models with significant advantages and reliable prediction accuracies. The proposed algorithm is subjected to robustness validation, and the experimental results demonstrate that the model achieves reliable precision, with a mean absolute error (MAE) less than 0.768% and a root mean square error (RMSE) less than 1.185%, even when LIBs are subjected to varying application scenarios. Our study highlights the superiority and reliability of combining DNNs with DCIR features for battery SOH estimation. [ABSTRACT FROM AUTHOR]