Reliability Of Battery System Research Articles

Fault Diagnosis and Detection for Battery System in Real-World Electric Vehicles Based on Long-Term Feature Outlier Analysis

Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems. Developed methods for battery early fault diagnosis concentrate on short-term data to analyze the deviation of external features without considering the long-term latent period of faults. This work proposes a novel data-driven method to detect long-term latent fault and abnormality for electric vehicles based on real-world operation data. Specifically, the battery fault features are extracted from the incremental capacity curves, which are smoothed by advanced filter algorithms. Secondly, principal component analysis algorithm is utilized to reduce dimensionality and the cumulative percent variance is to determine the number of significant features. Based on the features, a cluster algorithm is employed to capture the battery potential failure information. Moreover, the cumulative root-mean-square-deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data. In addition, a battery system failure index is proposed to evaluate battery fault conditions. The results indicate that the proposed long-term feature analysis method can effectively detect and diagnose faults.

Online Estimating State of Health of Lithium-Ion Batteries Using Hierarchical Extreme Learning Machine

Battery state-of-health (SoH) monitoring is of great importance to ensure the safety and reliability of battery systems. This study proposed an innovative SoH estimation method using hierarchical extreme learning machine (HELM) to improve the estimation robustness and accuracy without the complex parameter model was directly applied to establish the HELM-oriented online SoH estimation framework. First, the increase in mean ohmic resistance was constructed as a novel health indicator (HI) to characterize battery aging. Then, the HI was adopted for offline training to build an HELM model, which captures the underlying correlation between the extracted HI and capacity degradation. Finally, the datasets of four batteries at three different temperatures with dynamic loading profiles were used for validation. The results show that the SoH estimation errors are no more than 1.5%, while the training and estimation datasets are from the same temperature; when the SoH estimation is conducted at different temperatures, the maximum error is only 3.36%. The results indicate that the proposed method had good generalization and reliability for SoH estimation, which is applicable for dynamic scenarios with different temperatures.

Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm

State of health estimation (SOH) and remaining useful lifetime (RUL) prediction are significant health indicators for improving the safety and reliability of battery systems. Herein, a data-fusion method is developed to establish a non-parametric degradation model and a particle filter algorithm for forecasting battery health conditions. Firstly, a dynamic battery aging state-space system is developed, in which Gaussian process regression is applied to establish state equation using historical capacity series and current capacity as input and output variables, respectively. Meanwhile, multi-output Gaussian process regression maps the relationship between capacity degradation and battery health indicators to construct an observation equation. Second, two filter methods are unitized to obtain the smooth differential thermal voltammetry curves and the significant health indicators are extracted from partial differential thermal voltammetry curves. Third, the short-term SOH estimation and long-term RUL prediction are carried out using a particle filter algorithm. Moreover, two types of five batteries with various designed cases are conducted to verify and analyze the proposed method. The results show that the estimation errors of short-term SOH are within 4% and prediction errors of long-term RUL are around 7% (relative error/EOL, 12/159), which indicate the proposed method has an excellent capability for accurate and robust forecasting battery health conditions. • A non-parametric state-space representation for battery degradation is developed. • Battery health conditions are estimated and predicted in multi-timescale perspective. • The health indicators are extracted from partial DTV curves in different dimensions. • Two types of five batteries with various designed cases are discussed and analyzed.

A novel reliable and economic topology for battery energy storage system

In order to improve the operational reliability and economy of the battery energy storage system (BESS), the topology and fault response strategies of the battery system (BS) and the power conversion system (PCS) have been emphatically studied. First, a new type of BS topology is proposed, which can greatly improve the reliability and economy of the system when single or multiple battery cells fail. Compared with the conventional topology, the maximum number of faulty battery cells that the new topology can bear and the economic gains will increase. In addition, by comparing with the centralized PCS topology, the advantages of the distributed PCS topology are analyzed from the four aspects of consistency, efficiency, BS reliability and PCS reliability. The consistency, efficiency and reliability of the latter are greatly improved. Therefore, the research results obtained in this paper will be helpful to improve the intelligent operation and maintenance of BESS.

Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models

Prognostics of batteries involve state estimation and remaining useful life (RUL) prediction. Various data-driven approaches are being studied to achieve accurate RUL predictions and SOH estimations to ensure safety and reliability of battery systems. The Gaussian Process Regression (GPR) is a statistical approach that accommodates the nonlinear nature and small sample size data to effectively predict the RUL of lithium-ion batteries. Artificial Neural Networks (ANN) have the ability to approximate nonlinear data and since battery degradation is a nonlinear process, neural networks-based models can provide accurate RUL predictions for lithium-ion batteries. In this paper, both the GPR model and ANN model approaches are implemented on the NASA PCoE battery datasets and the predictions are compared. The model training and validation is performed by splitting the data into various proportions (30:70%, 50:50%, and 70:30% for training: validation). Furthermore, the model's prediction accuracy is compared with respect to single vs multi-variable data and single battery data vs combined multiple battery data. The approach improves the prediction accuracy with the models exhibiting low RMSE values of 0.0181 and 0.0367 for GPR and ANN models, respectively, for B0018 and RMSE values of 0.1516 and 0.0810 for GPR and ANN models, respectively, for B0048.

Online estimation of battery model parameters and state of health in electric and hybrid aircraft application

The accuracy of state of health (SOH) estimation function in battery management systems is an essential factor for ensuring the safety and reliability of battery systems in electric aircraft. Most common SOH estimation approaches are model-based and work with constant model parameters. However, the model parameters vary by the change in operating temperature and state of charge (SOC). The variation of model parameters has adverse impact on the accuracy of battery state's estimation if they are not updated. In this paper, an accurate online parameter estimation method is proposed for lithium-ion batteries (LIBs) to increase the accuracy of the battery model. A more accurate model for the battery leads to a more accurate SOC and SOH estimation. An adaptive sliding observer is developed to estimate the SOC and capacity based on the proposed parameter estimator. An adaptive SOH estimation scheme is proposed to mitigate the temperature variation effect on the accuracy of the SOH estimation. The experimental results verify the effectiveness of the proposed parameter estimator along with the adaptive sliding observer on achieving accurate estimation of SOC and capacity. The proposed adaptive SOH shows good agreement with experiments by less than 1.3% estimation error at different operating temperature conditions.

Remaining useful life prediction for lithium-ion battery by combining an improved particle filter with sliding-window gray model

Dependable and accurate battery remaining useful life (RUL) prediction is essential for ensuring the safety and reliability of battery systems. To improve the dynamic traceability of the battery degradation process for RUL prediction under different loading profiles, this paper presents an improved RUL prediction method, which is established from the combination of the linear optimization resampling particle filter (LORPF) with the sliding-window gray model (SGM). Major innovations are presented as follows: (1) To increase the accuracy of RUL prediction, a linear optimization combination is proposed to overcome the particle diversity deficiency in the resampling process of the standard PF, i.e. the LORPF; (2) To improve the traceability of the LORPF in predicting degradation trajectory, the SGM is employed to update the state variables of the state–space model in the LORPF. Additionally, an SGM-LORPF framework is constructed for RUL prediction. The performance of the SGM-LORPF is synthetically verified by data from two types of batteries under different loading profiles. Prediction test results indicate that the SGM-LORPF can achieve accurate RUL prediction under both constant current discharge conditions (relative error within 7.20%) and dynamic current discharge conditions (relative error within 2.75%). Moreover, using only a small amount of historical data, the proposed SGM-LORPF framework can acquire accurate results. The experimental outcome indicates that the SGM-LORPF has considerable efficiency and a wide range of practicality.

The Open-Circuit Voltage Characteristic and State of Charge Estimation for Lithium-Ion Batteries Based on an Improved Estimation Algorithm

Abstract The State of Charge (SoC)-Open Circuit Voltage (OCV) curve and the quality of estimation algorithm are two important factors that infect the accuracy of SoC estimation for lithium-ion batteries in electric vehicles. The purpose of this study is to improve the accuracy of SoC estimation for the lithium-ion battery. The battery management system is established to monitor the state of lithium-ion batteries to ensure the safety and reliability of the battery system. Firstly, the specific experiments were designed to analyze the relationship between the SoC-OCV curve and experimental conditions (e.g., ambient temperature and current rate) and battery states (e.g., State of Health and positive materials). A series of conclusions was found and used to correct the process of SoC estimation. Secondly, by analyzing the reasons that the SoC estimation error increased in the low-capacity period and the late-stage of estimation using the extended Kalman filter (EKF), an improved estimation algorithm was proposed. In the improved estimation algorithm, the ampere-hour counting was used in the low-capacity period, and the EKF was used in the rest. The accuracy of the improved estimation algorithm was verified by two experiments. Verification results show that the improved estimation algorithm makes up for the drawback of the EKF, the estimation error in constant current discharge experiment is less than 2 %, and the estimated error under dynamic conditions is less than 3 %. Therefore, the improved estimation algorithm has a higher accuracy than the EKF for the SoC estimation and can meet the operation requirements of a lithium-ion battery. This study contributes to the improvement of the safety and reliability of battery systems in electric vehicles by improving the accuracy of SoC estimation for lithium-ion batteries.

Reliability Evaluation of Large Scale Battery Energy Storage Systems

This paper analyzes the reliability of large scale battery storage systems consisting of multiple battery modules. The whole system reliability assessment is based on the reliability evaluation of system components including individual battery modules and power electronic converters. In order to evaluate the reliability of a battery module, a reliability model based on the state of health of individual battery cells is introduced. The state of health of a battery cell is calculated based on the capacity fade of the cell using a weighted Ampere-hour throughput method. A universal generating function-based method is then introduced to evaluate the reliability of the battery module. The reliability model of dc/ac power electronic converters is also presented in this paper. The reliability analysis is conducted for battery storage systems with different system configurations and management strategies, and the influence of system configuration on the reliability of battery system is studied. Comparative studies are conducted for a classic battery energy storage system (BESS) and a reconfigurable BESS (RBESS) to demonstrate the advantages of having a reconfigurable system topology. The comparison results show that the proposed RBESS has higher system reliability and more power outputs than the classic BESS.