Battery Fault Diagnosis Research Articles

Improvement of local outlier factor algorithms for lithium-ion battery fault diagnosis

Fault diagnosis is one of the most important active strategies to protect lithium-ion batteries (LIBs) from safety accidents. The tasks of fault diagnosis usually can be divided into three levels, i.e., (1) fault detection, (2) fault isolation, and (3) fault estimation. The local outlier factor (LOF) method has been proved effective in conducting fault detection (level 1 of fault diagnosis) for LIB energy storage systems (ESSs). However, the original LOF algorithm (OLOF) may fail to make correct fault estimation (level 3 of fault diagnosis). Therefore, in this study, two novel LOF algorithms, i.e., the improved LOF algorithm (ILOF) and the simplified LOF algorithm (SLOF), are put forward to enhance the capability of fault estimation. The performance of the three LOF algorithms: OLOF, ILOF and SLOF, are firstly compared via three mathematical cases. Their capabilities in fault diagnosis are further investigated based on the simulated data from an air-cooled LIB ESS and the experimental data from a water-cooled LIB ESS. Results prove the effectiveness of all the three algorithms in fault detection; nevertheless, when the OLOF algorithm fails to make correct fault estimation, both of the two proposed novel algorithms, i.e., the ILOF algorithm and the SLOF algorithm, present the capability in making correct fault estimation. It is found as well that the ILOF algorithm is better than the SLOF algorithm in robustness though the latter is more efficient than the former in memory usage and computational time.

Fault diagnosis method for lithium-ion batteries based on the combination of voltage prediction and Z-score

ABSTRACT Safety accidents in new energy electric vehicles caused by lithium-ion battery failures occur frequently, and the timely and accurate diagnosis of failures in battery packs is crucial. Voltage, as one of the primary characterization parameters of lithium-ion battery malfunctions, is widely utilized in fault diagnosis. This article proposes a lithium-ion battery fault diagnosis method Fault diagnosis method based on the combination of voltage prediction and Z-score. Firstly, the stable trend component is extracted from the battery voltage data using variational mode decomposition, which avoids the influence of noisy signals and random perturbations to the greatest extent. Subsequently, a TCN-BiLSTM-attention model is designed to estimate the average voltage of the battery under normal conditions. Finally, the residuals between the estimated and individual cell voltages are calculated, and the Z-score is utilized to locate and judge whether the battery is caused by the occurrence of a fault. Through verification with real vehicle data and experimental data, the proposed method effectively identifies abnormal battery cells. Compared to the correlation coefficient method, this approach exhibits superior applicability.

Transformer-based lithium battery fault diagnosis research for electric vehicles

With the fast advances of new energy vehicles, the EV battery technology needs to be further improved to follow the step. How to effectively diagnose the electric vehicle’s lithium battery fault becomes a hotspot in the academic circle. This study has proposed new method that uses the state of charge of the battery and self-coder depth to detect faults in the lithium battery group of electric vehicles. First, the study investigates the single lithium battery faults. Then, it builds a lithium battery group fault diagnosis model by integrating the battery charge state and denoising converter network. Finally, it uses a dataset and retired battery group to validate the model’s performance. The results show that the proposed model achieves an accuracy of 93.18% and a recall rate of 93.73% in identifying the faults in the lithium batteries of the electric vehicles and its F1 value is as high as 0.95. Moreover, the modeling method has the lowest prediction error, indicating its high accuracy and robustness in diagnosing the faults of battery packs. This study aims to provide an effective solution for the fault diagnosis of lithium battery packs in electric vehicles.

Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features

Accurately identifying a specific faulty monomer in a battery pack in the early stages of battery failure is essential to preventing safety accidents and minimizing property damage. While there are existing lithium-ion power battery fault diagnosis methods used in laboratory settings, their effectiveness in real-world vehicle conditions is limited. To address this, fault diagnosis methods for real-vehicle conditions should incorporate fault characteristic parameters based on external battery fault characterization, enabling the accurate identification of different fault types. However, these methods are constrained when confronted with complex fault types. To overcome these limitations, this paper proposes a battery fault diagnosis method that combines multidimensional fault features. By merging different fault feature parameters and mapping them to a high-dimensional space, the method utilizes a local outlier factor (LOF) algorithm to detect anomalous values, enabling fault diagnosis in complex working conditions. This method improves the detection time by an average of 22 min compared to the extended RMSE method and maintains strong robustness while correctly detecting faults compared to other conventional methods.

Multi-fault detection and diagnosis method for battery packs based on statistical analysis

Rapid and accurate battery fault diagnosis and distinction is of great importance in electrical vehicles and electrochemical energy storage system. However, misdiagnosis and missed diagnosis happened occasionally. In this paper, a statistical analysis-based multi-fault diagnosis method is proposed to detect and localize short circuit faults, electrical connection faults and voltage sensor faults in LFP battery packs. This method uses non-redundant interleaved voltage measurement topology to detect battery voltages, where every voltage sensor measures the sum of two neighboring batteries and one connection resistor between them. The statistical analysis method sets detection thresholds based on the battery operating data, and captures fault characteristics by analyzing abnormal changes in battery voltage unrelated to current. Theoretical analysis and tests verified that this method can diagnose these three kinds of faults. Sensor faults of excessive error and data sticking can also be distinguished.

Optimized LSTM based on an improved sparrow search algorithm for power battery fault diagnosis in new energy vehicles

Optimized LSTM based on an improved sparrow search algorithm for power battery fault diagnosis in new energy vehicles

Battery fault diagnosis and thermal runaway warning based on the Feature-Exponential-Function and Dynamic Time Warping method

Battery fault diagnosis and thermal runaway warning based on the Feature-Exponential-Function and Dynamic Time Warping method

Micro-fault identification for lithium-ion batteries in electric vehicles based on similarity measurement of the charging sequence

Battery fault diagnosis is critical to safeguard the safety of electric vehicles. Since lithium-ion battery cells are usually inconsistent and difficult to distinguish from faults, existing micro-fault identification methods are difficult to detect faults as early as possible based on real-world vehicle data. In contrast to the operation process, the voltage and temperature changes are more regular since the current relatively remains constant and the charging strategy is fixed, which is conducive to accurate micro-fault identification. According to the charging strategy of electric vehicles, charging data extraction and selection methods are introduced. Considering the practicality and the difficulty of threshold value determination, the micro-fault identification approach based on DBSCAN is proposed. Normalized IAVs and LIP distances are utilized as features, which come from charging sequences. The results of the thermal runaway vehicle show that the method can effectively find fault cells in voltage signals 18 days before thermal runaway occurs.

Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine

Battery fault diagnosis technology is crucial for the reliable functioning of battery systems. This research introduces an online least squares support vector machine method tailored for battery fault diagnosis. After examining battery fault types and gathering relevant data, this method creates a diagnostic model, effectively addressing small and sporadic fault data that is inadequately handled by conventional support vector machines. Recognizing that certain battery malfunctions evolve over time and are multifaceted, confidence intervals have been integrated into the diagnostic models, enhancing accuracy. Upon testing this model using empirical data, it demonstrated rapid diagnostic capabilities and outperformed other algorithms in identifying progressive faults, ensuring precise fault identification, minimizing false alarms, and bolstering battery system safety.

Voltage fault diagnosis and misdiagnosis analysis of battery systems using the modified Shannon entropy in real-world electric vehicles

Power battery systems fault diagnosis of electric vehicles (EVs) is a key technical approach to ensure the safety and reliable operation of the vehicle, of which avoiding misdiagnosis not only can reduce the driver's safety concerns, but also is a reflection of the reliability of the diagnostic method. This paper first proposes a modified Shannon entropy-based battery fault diagnosis method for identifying cells with abnormal voltage fluctuations in battery systems, and the method is implemented online by calculating the Shannon entropy of the voltage sequence in a moving time window. Then, the defined sensitivity factor (SF) can provide an efficient and accurate assessment of the extent of abnormal voltage fluctuations. Further, to improve the fault diagnosis accuracy of the method, we validated the diagnostic model by using battery operation data from a battery monitoring cloud platform with a sampling frequency of 0.1 Hz and eventually found a large number of false alarm cells in the diagnostic results. Based on this, we have analysed the fault diagnosis mechanisms of the model and thus obtained the causes of misdiagnosis. Finally, a solution to reduce misdiagnosis is proposed and its effectiveness is verified. To further test its comprehensive performance, we compared the accuracy of the method before and after optimization based on normal vehicle data, and the results showed a 81.9 % reduction in the average relative misdiagnosis rate of the model, thus greatly improving the reliability of this fault diagnosis strategy, creating conditions for the online application of the method and providing ideas for the analysis and improvement of the accuracy rate of other fault diagnosis methods.

A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window

AbstractThe vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults.

Fault diagnosis of lithium-ion batteries based on voltage dip behavior

ABSTRACT In recent years, the safety accidents of new energy electric vehicles have been increasing due to the failure of lithium-ion batteries. The lithium-ion battery fault diagnosis technology is critical to ensure the safe operation of electric vehicles. In this paper, we proposes a lithium-ion battery fault diagnosis method based on voltage dip behavior. The method first uses the Sparrow Search Algorithm(SSA) to optimize the Variational Modal Decomposition(VMD), then reconstructs multiple dynamic components and extracts the multi-feature parameters of the reconstructed components, and finally uses SSA to optimize Density Based Spatial Clustering of Applications with Noise(DBSCAN) for fault diagnosis. Through verification with real vehicle data and experimental data at different temperatures, this method can be applied to the operating environment of real vehicles at different temperatures, and can quickly and accurately identify abnormal cells.

Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks

Battery-powered electric vehicles (EVs) are poised to accelerate decarbonization in nearly every aspect of transportation. However, safety issues of commercial lithium-ion batteries related to the faults and failures in real-world applications are still serious concern. Even a small increase in risk during the battery's operational lifetime may evolve into a safety hazard-fire and explosion, named as thermal runaway, after long-term incubation. Modelling and predicting the evolution of nonlinear multiscale electrochemical systems is challenging due to uncertainties in materials and manufacturing processes, dynamic environmental and operating conditions, as well as a lack of high-quality datasets. This challenge is further complicated when solving real-life physical problems with missing and noisy data and uncertain boundary conditions. In this study, we address these challenges by developing a specialized Transformer network architecture called BERTtery (Bidirectional Encoder Representations from Transformers for batteries) based on field data of EVs. By using charging voltage and temperature curves from early cycles before exhibiting symptoms of battery, the two-tower Transformer with temporal-wise encoder and channel-wise encoder is demonstrated as a powerful tool to capture early-warning signals across multiple spatio-temporal scales under a wide range of operating conditions. The method reliably predicts the evolution of faults in battery systems using only data provided by the onboard sensor measurements of battery performance.

A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles

The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.

A double-layer fault diagnosis strategy for electric vehicle batteries based on Gaussian mixture model

Battery fault diagnosis is essential to ensure the safe operation of electric vehicles (EVs). In this paper, due to the complexity of EVs’ battery thermal runaway tracing investigation and the limited capacity of on-board computing system, a double-layer fault diagnosis strategy for abnormal cells is proposed. The method bases on probability distribution, which can accurately trace a faulty cell and avoid misinterpreting a normal cell. In this method, unified statistical features are extracted from the big data during vehicle charging, and the corresponding statistical values are analyzed based on Gaussian mixture model and abnormal alarm is made based on the risk accumulation in double-layer diagnostics. The electric vehicles with thermal runaway accident are taken as examples to verify the method, and based on the data of normal-running vehicles, the false alarm tests are carried out. The verification results show that the proposed method can not only successfully identify the outlier cells, but also not generate false alarm, which is conducive to the practical application of fault diagnosis in the on-board battery management system.

Challenges and outlook for lithium-ion battery fault diagnosis methods from the laboratory to real world applications

Lithium-ion batteries are the ideal energy storage device for numerous portable and energy storage applications. Efficient fault diagnosis methods become urgent to address safety risks. The fault modes, fault data, fault diagnosis methods in different scenarios, i.e., laboratory, electric vehicle, energy storage system, and simulation, are reviewed and compared comprehensively. The data characteristics, performance and limitations of fault diagnosis methods are discussed further. The results show that the fault diagnosis methods of laboratory scenario are more advanced than real-world applications because of the clean and perfect dataset, advanced equipment, and ideal operating conditions. At last, the outlook and challenges for applying fault diagnosis methods from laboratory to real-world applications are investigated from three aspects: unified framework of fault diagnosis methods, cloud big data fusion, and application of laboratory measurement technologies. To realize more accurate fault diagnosis in real-world applications within the advanced research of laboratory, more significant work is still needed.

A Fault Diagnosis Method for Power Battery Based on Multiple Model Fusion

The widespread adoption and utilization of electric vehicles has been constrained by power battery performance. We proposed a fault diagnosis method for power batteries based on multiple-model fusion. The method effectively fused the advantages of various classification models and avoided the bias of a single model towards certain fault types. Firstly, we collected and sorted parameter information of the power battery during operation. Three common neural networks: back propagation (BP) neural network, convolution neural network (CNN), and long short-term memory (LSTM) neural network, were applied to battery fault diagnosis to output the fault types. Secondly, the fusion algorithm proposed in this paper determined the accurate fault type. Based on the improved voting method, the proposed fusion algorithm, named the multi-level decision algorithm, calculated the voting factors of the diagnostic results of each classification model. According to the set decision thresholds, multi-level decision voting was conducted to avoid neglecting effective classification information from minority models, which can occur with traditional voting methods. Finally, the accuracy and effectiveness of the proposed method were verified by comparing the accuracy of each classification model with the multiple model fusion algorithm.

A new method to perform lithium-ion battery pack fault diagnostics – Part 3: Adaptation for fast charging

Electric Vertical Take-Off and Landing (eVTOL) aircraft are expected to become ubiquitous in the future Urban Air Mobility (UAM) landscape. Several eVTOL aircraft propelled using Lithium-ion batteries are under development. However, despite the early spotlight, the manufacturers need to ensure safe long-term operation of the vehicles including strict checks on battery-related hazards. On the other hand, fast charging of eVTOL batteries is crucial to enable multiple flights per day and justify the economics of UAM. This work is aimed at contextualising battery safety for eVTOL through the modification of a battery fault diagnosis algorithm for fast charging. The algorithm was developed in Parts 1 and 2 of the paper to use the charging cycle data for detecting disconnection faults but tested only for low charging currents. This paper adapts the algorithm for fast charging through a novel technique termed as Partial Incremental Capacity (PIC). The PIC method was developed using experiments at single cell and supercell level before integrating it into the algorithm. Finally, the fault detection ability of the adapted algorithm was validated using a real-life eVTOL battery module. Thus, the updated version of the algorithm facilitates fault diagnosis while charging fast, making it ideal for implementation in eVTOL.

Capacity evaluation and degradation analysis of lithium-ion battery packs for on-road electric vehicles

Accurately calculating the capacity of battery packs is of great significance to battery fault diagnosis, health evaluation, residual value assessment, and predictive maintenance in electric vehicles (EVs). However, many existing methods are not suitable for real vehicle conditions. In this paper, a capacity calculating method specialized for EVs is proposed. This method uses an open circuit voltage (OCV) correction strategy to guarantee the credibility of the battery state of charge (SOC). Its accuracy is validated by full charging carried out on 5 testing vehicles and the mean absolute error is 2.6 Ah. Data from 707 on-road electric vehicles are collected and the capacities of their battery packs are calculated through the proposed method. Taking the mileage and service life as variables, two degradation models of battery capacity are established with mean absolute errors equal to 3.138 Ah and 3.137 Ah. According to the degradation model, the battery's average service life is 71.8 months, and the average service mileage is 121,700 km. Moreover, correlations between capacity degradation and user behaviors are analyzed, and the results indicate that charging the battery with the start point at a SOC between 30 % and 40 % can effectively relieve the degradation.

Fault diagnosis of real-scenario battery systems based on modified entropy algorithms in electric vehicles

Entropy algorithm is an efficient fault diagnosis technology gradually gaining popularity, which is highly promising in terms of battery safety protection in electric vehicles. This paper compares two entropy algorithms commonly used for battery fault diagnosis and their ability to diagnose abnormal fluctuations implied in the pre-fault phase. By exploring the influence of key calculation factors in the entropy algorithm on the fault diagnosis effect, this study pioneered the discovery of a significant normal distribution pattern and the logarithmic relationship between the diagnostic effect and the size of the Shannon entropy's calculation window and multi-scale sample entropy's scale factor, respectively. The relationship between computation time and computation factor was found to provide a theoretical basis for improving diagnosis efficiency through the rational selection of computation factors. Furthermore, based on actual vehicle fault data verification, a multi-level diagnosis strategy with strong robustness and generality is proposed using statistical methods. More importantly, this study continues to explore the importance of deeper mining of different entropy features, and the proposed control strategies are of high practical significance for battery fault diagnosis/prognosis in real-world vehicle applications.