One important task for a BMS is to estimate the state of charge (SoC) and state of health (SoH) of a battery. The correlation between battery open circuit voltage (OCV) and SoC is an important
Multiple lithium-ion battery cells and multi-contact connection methods increase the chances of connection failures in power battery packs, posing a significant threat to the operational safety of electric vehicles.
Calculating the Shannon entropy clearly identifies the cause of the power fade fault. Appropriate measures are taken to solve the fault, and the latent safety issue is eliminated. The...
Furthermore, the short circuit current was calculated based on the differences in IC curves between the battery module with micro-short circuit faults and the normal battery module, which allowed them to obtain the micro-short circuit fault resistance. Gao et al. [14] developed a Cell Difference Model (CDM) to estimate the SOC difference between each
In this study, a voltage correlation coefficient-based method and a dual extended Kalman filter (DEKF) are proposed to detect the COC fault. Then, performances of these two methods are
test the battery packs for defects and performance. This testing can be a bottleneck in the manufacturing process, so test solutions that reduce time or increase test density are highly
For instance, at 736 s, the connection between batteries is intentionally disconnected to simulate an open circuit fault, with the fault duration set to 30 s, causing the current to return to zero. At
Abstract: Battery fault diagnosis has great significance for guaranteeing the safety and reliability of lithium-ion battery (LIB) systems. Out of many possible failure modes of the series–parallel connected LIB pack, cell open circuit (COC) fault is a significant part of the causes that lead to
This article considers the design of Gaussian process (GP)-based health monitoring from battery field data, which are time series data consisting of noisy temperature, current, and voltage measurements corresponding to the system, module, and cell levels. 7 In real-world applications, the operational conditions are usually uncontrolled, i.e., the device is in
test the battery packs for defects and performance. This testing can be a bottleneck in the manufacturing process, so test solutions that reduce time or increase test density are highly desirable. One of the most useful measurements for a battery cell or pack is the open circuit voltage (OCV), but
Using a high-resolution DC voltmeter makes it possible to detect defective cells sooner, shortening testing times. Accuracy calculations are key accurately understanding instrument
In this work, a battery pack insulation fault diagnosis scheme is proposed based on adaptive filtering. Specifically, a battery pack insulation detection topology based on signal injection is designed. The model of positive and negative electrode insulation fault is established by equivalent the electrode insulation fault of the battery pack
Using a high-resolution DC voltmeter makes it possible to detect defective cells sooner, shortening testing times. Accuracy calculations are key accurately understanding instrument performance. Most instruments define accuracy in terms of reading error and digit error. Batteries'' OCV varies with temperature.
First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary-least-squares-based voltage potential feature extraction method is proposed, which can effectively capture the small fault features of battery cells and achieve early warning.
Calculating the Shannon entropy clearly identifies the cause of the power fade fault. Appropriate measures are taken to solve the fault, and the latent safety issue is eliminated. The...
Abstract: Battery fault diagnosis has great significance for guaranteeing the safety and reliability of lithium-ion battery (LIB) systems. Out of many possible failure modes of the series–parallel connected LIB pack, cell open circuit (COC) fault is a significant part of the causes that lead to the strong inconsistency in the pack and the
This paper presents a systematic fault diagnostic scheme based on hybrid system for the typical faults of lithium-ion battery packs, including sensor faults and relay faults. The...
In this study, a voltage correlation coefficient-based method and a dual extended Kalman filter (DEKF) are proposed to detect the COC fault. Then, performances of these two methods are compared by taking several COC fault simulation experiment. Experiments with a 4S-3P battery pack under different operating conditions are used to verify two
battery faults early and accurately. Due to the complex nonlinear fea-tures and inconsistency of lithium batteries, traditional fault diagno-sis methods usually fail to detect battery minor faults in the early stages. Therefore, this letter proposes a real-time unsupervised learn-ing diagnosis approach for early battery faults based on improved
First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary
This paper presents a systematic fault diagnostic scheme based on hybrid system for the typical faults of lithium-ion battery packs, including sensor faults and relay
Multiple lithium-ion battery cells and multi-contact connection methods increase the chances of connection failures in power battery packs, posing a significant threat
battery faults early and accurately. Due to the complex nonlinear fea-tures and inconsistency of lithium batteries, traditional fault diagno-sis methods usually fail to detect battery minor faults
Abstract: Fault diagnosis has great significances for reducing the failures and improving the reliability of Li-ion battery systems. However, there are few researches on cell open circuit (COC) fault diagnostic for the series-parallel connected battery pack before. In this study, a voltage correlation coefficient-based method and a dual extended Kalman filter (DEKF) are proposed
In short, the conventional fault diagnosis methods for lithium-ion battery packs, to the authors'' knowledge, are inefficient for detecting the faults and abnormalities and locating faulty cells of battery packs. To address this issue, a systemic faults diagnosis method and a voltage abnormality detection approach are mainly investigated and developed for the battery
Xiong et al. established the Thevenin model of a battery pack and a single battery to diagnose the short circuit fault outside the battery pack and locate the fault of a single battery [16]. Liu et al. adopted three submodels, an electrical model, SOC model and thermal model, and considered the electric heating change of the battery under the internal short-circuit state to
For instance, at 736 s, the connection between batteries is intentionally disconnected to simulate an open circuit fault, with the fault duration set to 30 s, causing the current to return to zero. At 2947 s, a circuit breaker is connected in parallel with the battery to simulate a short circuit failure, resulting in a voltage drop and a peak in current. At 3684 s, white noise is injected into
Real-time and accurate estimating state-of-charge (SOC) of a lithium-ion battery is a critical but technically challenging task for battery management systems. Coulomb counting algorithm is an effective real-time SOC estimation algorithm but suffers from three typical faults: initial SOC fault, battery capacity fault, and biased load current measurement fault, making its
Based on the voltage data, this paper develops a fault warning algorithm for electric vehicle lithium-ion battery packs based on K-means and the Fréchet algorithm. And the actual collected EV driving data are used to verify. First, due to the noise of the EV data collected in actual operation, it will affect the accuracy of the diagnosis algorithm.
To this end, the study proposes an intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making.
And adaptive thresholds are set for the detection and localization of faulty cells. To the best of our knowledge, the discrete Fréchet algorithm is presented for the first time in the field of faulty detection of battery packs. The remainder of this paper is organized as follows.
The battery fault diagnosis method based on principal component analysis determines whether a fault occurs through the residual between the predicted and actual values. The original data matrix Xn m , as shown in (1). where n and m denote the number of samples and batteries, respec-tively.
A battery internal fault diagnosis method was developed using the relationship of residuals, which can reliably detect various faults inside lithium-ion batteries. 23 However, the method requires a large amount of historical fault data for rule building and fewer fault data in actual operation.
Integrated learning is applied to battery fault diagnosis where the weight matrix determines the accuracy and robustness of the integration results. The weighting matrix reflects the ability of the evidence source to provide the correct assessment or solution for solving a given problem.
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