As a typical machine learning algorithm, neural networks (NNs) has been designed and developed for battery management system (BMS) with artificial intelligence. State of charge (SOC) estimation of lithium-ion battery (LIB) is the basis of BMS so as to widely employ NNs, and recurrent neural network (RNN) is usually selected to describe the time-series characteristics of LIB. However, RNN is a data-driven statistic black box, which cannot reveal electrochemical principle and learn inner Knowledge of LIB. This paper introduces fractionalorder gradients for RNN to improve its backpropagation process, so that network updates weights instructed by the fractionalorder characteristics of LIB. Our work provides two backpropagation patterns with fractional-order gradient descent and momentum for RNN, respectively, both resulting in a physicsinformed RNN for SOC estimation of LIB. The proposed physicsinformed RNN can conduct training in which the gradients and the loss of network is informed by the physical fractional-order laws of LIB. Experimental results under operation conditions of federal urban driving schedule (FUDS) are presented with satisfying SOC estimation accuracy. Furtherly, physics-informed RNN proposed in this paper is not limited to SOC estimation, but also other state estimation or even fault prognosis for LIB.

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO₄ batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery.

Typical usage scenarios for energy storage and electric vehicles (EVs) require lithium-ion batteries (LIBs) to operate under extreme conditions, including varying temperatures, high charge/discharge rates, and various depths of charge and discharge, while also fulfilling vehicle-to-grid (V2G) interaction requirements. This study empirically investigates the impact of ambient temperature, charge/discharge rate, and charge/discharge cut-off voltage on the capacity degradation rate and internal resistance growth of 18,650 commercial LIBs. The charge/discharge rate was found to have the most significant influence on these parameters, particularly the charging rate. These insights contribute to a better understanding of the risks associated with low-temperature aging and can aid in the prevention or mitigation of safety incidents.

The state estimation of lithium-ion battery is the basis of an intelligent battery management system; therefore, both model-based and data-driven methods have been designed and developed for state estimation. Rather than using complex partial differential equations and the complicated parameter tuning of a model-based method, a machine learning algorithm provides a new paradigm and has been increasingly applied to cloud big-data platforms. Although promising, it is now recognized that big data for machine learning may not be consistent in terms of data quality with reliable labels. Moreover, many algorithms are still applied as a black box that may not learn battery inner information well. To enhance the algorithm generalization in realistic situations, this paper presents a fractional-order physics-informed recurrent neural network (PIRNN) for state estimation. The fractional-order characteristics from battery mechanism are embedded into the proposed algorithm by introducing fractional-order gradients in backpropagation process and fractional-order constraints into the convergence loss function. With encoded battery knowledge, the proposed fractional-order PIRNN would accelerate the convergence speed in training process and achieve improved prediction accuracies. Experiments of four cells under federal urban driving schedule operation conditions and different temperatures are conducted to illustrate the estimation effects of the proposed fractional-order PIRNN. Compared to the integer-order gradient descent method, the fractional-order gradient descent method proposed in this work can optimize network convergence and obtains regression coefficient larger than 0.995. Moreover, the experimental results indicate that the proposed algorithm can achieve 2.5% estimation accuracy with the encoding fractional-order knowledge of lithium-ion batteries.

The rapid development of energy storage power stations plays a significant role in the widespread adoption of the energy internet. Anomaly detection in these stations, as a critical component of daily operation and maintenance, holds great importance for ensuring the normal operation of energy storage systems. Currently, station monitoring primarily relies on preset fixed threshold-based alerts combined with manual supervision. However, this approach is unable to detect abnormal states below the threshold and poses a risk of missing certain anomalies. This study employs an unsupervised deep learning model based on variational autoencoders (VAEs) to perform anomaly detection on real operational data. By training the model on normal operational data, the model learns the distribution of data in the latent space under normal conditions. Experimental results demonstrate that the VAE-based model is capable of effectively detecting abnormal data segments and outliers in electricity power real-world data. Compared to classical machine learning algorithms such as Isolation Forest and Support Vector Machine, the detection performance of the VAE-based model demonstrates superiority, indicating its practical value and research significance.