State of health prediction for lithium-ion batteries using multiple-view feature fusion and support vector regression ensemble

State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning

Lithium-ion batteries are a key technology for addressing energy shortages and environmental pollution. Assessing their health is crucial for extending battery life. When estimating health status, it is often necessary to select a representative characteristic quantity known as a health indicator. Most current research focuses on health indicators associated with decreased capacity and increased internal resistance. However, due to the complex degradation mechanisms of lithium-ion batteries, the relationship between these mechanisms and health indicators has not been fully explored. This paper reviews a large number of literature sources. We discuss the application scenarios of different health factors, providing a reference for selecting appropriate health factors for state estimation. Additionally, the paper offers a brief overview of the models and machine learning algorithms used for health state estimation. We also delve into the application of health indicators in the health status assessment of battery management systems and emphasize the importance of integrating health factors with big data platforms for battery status analysis. Furthermore, the paper outlines the prospects for future development in this field.

A simplified electrochemical model for lithium-ion batteries based on ensemble learning

The mass transfer in lithium-ion batteries is a low-frequency dynamic that affects their voltage and performance. To find an effective way to describe the mass transfer in lithium-ion batteries, a simplified electrochemical lithium-ion battery model based on ensemble learning is proposed. The proposed model simplifies lithium-ion transfer in electrode particles with ensemble learning which ensembles discrete-time realization algorithm (DRA), fractional-order Padé approximation model (FOM), and three parameters (TPM) parabolic. The lithium-ion transfer in the electrolyte is simplified by the first-order inertial element (FIE). The results show that the proposed model achieves not only accurate lithium-ion concentration prediction in solid and electrolyte phase but also precise voltage prediction with low computational complexity.

Machinery Prognostics and High-Dimensional Data Feature Extraction Based on a Transformer Self-Attention Transfer Network

Machinery degradation assessment can offer meaningful prognosis and health management information. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain discriminator to extract invariant features from different machine operating conditions. Case studies were carried out using the FEMTO-ST bearing dataset, and the Monte Carlo method was employed for RUL prediction during the degradation process. When compared to other established techniques such as the RNN-based RUL prediction method, convolutional LSTM network, Bi-directional LSTM network with attention mechanism, and the traditional RUL prediction method based on vibration frequency anomaly detection and survival time ratio, our proposed TSTN method demonstrates superior RUL prediction accuracy with a notable SCORE of 0.4017. These results underscore the significant advantages and potential of the TSTN approach over other state-of-the-art techniques.

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries.

A Machine Learning-Based Robust State of Health (SOH) Prediction Model for Electric Vehicle Batteries

The car industry is entering a new age due to electric energy as a fuel in the contemporary era. Electric batteries are being more widely used in the automobile sector these days. As a result, the inner workings of these battery systems must be fully comprehended. There is currently no accurate model for predicting an electric car battery’s state of health (SOH). This study aims to use machine learning to develop a reliable SOH prediction model for batteries. A correct optimal method was also constructed to drive the modeling process in the right direction. Extensive simulations were performed to verify the accuracy of the suggested methodology. A state of health method for data processing was developed. The method involves a complex data-driven model combining Big Data, Artificial Intelligence (A.I.), and the Internet of Things (IoT) technologies. To establish the most effective technique for certifying the actual condition of real-life battery health, researchers compared the accuracy and performance of several states of health models. For improved understanding and prediction of the condition of health behavior, data-driven modeling has certain significant advantages over older methodologies. The methods used in this study can be seen as a revolutionary low-cost, high-accuracy, and dependable approach to understanding and analyzing the state of health of batteries. At first, an intelligent model was created using a data-driven modeling strategy. Secondly, the concurrent battery data are qualified using the data-driven model. The machine learning (ML) method creates a very accurate and dependable model for forecasting battery health in real-world scenarios. Third, the previously established ML model was used to develop a knowledge-based online service for battery health. This web service can be used to test battery health, monitor battery behavior, and perform a variety of other tasks. A variety of similar solutions for diverse systems can be derived using the same technique. The default efficiency of the ML algorithmic module, R-Squared (R2), and Mean Square Error (MSE) were also utilized as performance measures. The R2 as a standard is used to examine the effectiveness of a fit. The result is a value between 0 and 1, with 1 indicating a better model fit. MSE stands for mean squared error. A lower MSE number implies superior model performance, since it reflects how close the parameter estimates are to the actual values. The training set of the battery model had a score of 0.9999, whereas the testing set had a score of 0.9995. The R2 score was one, with an M.S.E. of 0.03. As a result of these three indicators, the data-driven ML model used in this study proved to be accurate.

New Multifeature Information Health Index (MIHI) Based on a Quasi-Orthogonal Sparse Algorithm for Bearing Degradation Monitoring

Data-driven intelligent prognostic health management (PHM) systems have been widely investigated in the area of defective bearing signals. These systems can provide precise information on condition monitoring and diagnosis. However, existing PHM systems cannot identify the accurate degradation trend and the current fault types simultaneously. Given that different fault types have various effects on the mechanical system, the corresponding maintenance strategies also vary. Then, choosing the appropriate maintenance strategy according to the future fault type can reduce the maintenance cost of the equipment operation. Therefore, a multifeature information health index (MIHI) must be developed to trace various bearing degradation trends with various types of faults simultaneously. This paper reports a new quasi-orthogonal sparse project algorithm that can mutually convert the degraded processing feature vector sets (such as spectrum) for each type of fault to orthogonal approximate spatial straight lines. The algorithm builds a MIHI through the spectrum of current state measured points. The MIHI is then transformed by a quasi-orthogonal sparse project algorithm to trace the various bearing degradation trends and recognize the fault type simultaneously. The case study of bearing degradation data demonstrates that this approach is effective in assessing the various degradation trends of different fault types.

An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries

An accurate estimation of the state of charge for lithium battery depends on an accurate identification of the battery model parameters. In order to identify the polarization resistance and polarization capacitance in a Thevenin equivalent circuit model of lithium battery, the discharge and shelved states of a Thevenin circuit model were analyzed in this paper, together with the basic reasons for the difference in the resistance capacitance time constant and the accurate characterization of the resistance capacitance time constant in detail. The exact mathematical expression of the working characteristics of the circuit in two states were deduced thereafter. Moreover, based on the data of various working conditions, the parameters of the Thevenin circuit model through hybrid pulse power characterization experiment was identified, the simulation model was built, and a performance analysis was carried out. The experiments showed that the accuracy of the Thevenin circuit model can become 99.14% higher under dynamic test conditions and the new identification method that is based on the resistance capacitance time constant. This verifies that this method is highly accurate in the parameter identification of a lithium battery model.