Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems
The conventional fault-diagnosis methods are difficult to detect the battery faults in the early stages without obvious battery abnormality because lithium-ion batteries are complex nonlinear time-varying systems with abs. cell
Establishing an effective model for parallel-connected battery packs remains unsolved due to the coupling effect between battery cells in a pack. To tackle this issue, one can leverage the
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 developed framework is then employed to analyze the health of lithium ion batteries by monitoring the performance and detecting faults within the system''s behavior. Based on the outcomes, the DDP exhibits promising results in detection of anomaly and prognostication of batteries'' failure.
Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in severe cases. Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses.
Establishing an effective model for parallel-connected battery packs remains unsolved due to the coupling effect between battery cells in a pack. To tackle this issue, one can leverage the property of the same terminal voltage for diagnosis. Moreover, acquiring branch current information is challenging or nearly impossible in parallel-connected
Xu et al. (2024b) proposed a multi-objective nonlinear fault detection observer for lithium-ion batteries, developing a high Diagnosing various failures of lithium-ion batteries using
Experiments show that the proposed connection failure detection for a Lithium-ion battery pack when no external vibrations exist can identify the location of the connection failure well in real time. This paper presents a connection failure detection for a Lithium-ion battery pack when no external vibrations exist. First, the gradient correction method is employed to
In the present study, a systematic model based fault detection scheme is proposed using a bank of Unscented Kalman filter (UKF) on lithium ion battery pack model for multiple fault detection such
Xu et al. (2024b) proposed a multi-objective nonlinear fault detection observer for lithium-ion batteries, developing a high Diagnosing various failures of lithium-ion batteries using artificial neural network enhanced by likelihood mapping . J. Energy Storage, 40 (2021), Article 102768, 10.1016/j.est.2021.102768. View PDF View article View in Scopus Google Scholar. Li et al.,
With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an on-line detection of battery faults is much wanting. Internal short circuit is a very critical issue
Lithium-ion battery packs are widely deployed as power sources in transportation electrification solns. To ensure safe and reliable operation of battery packs, it is of crit. importance to monitor operation status
The conventional fault-diagnosis methods are difficult to detect the battery faults in the early stages without obvious battery abnormality because lithium-ion batteries are complex nonlinear time-varying systems with abs. cell inconsistency. Therefore, this paper proposes a real-time multi-fault diagnosis method for the early battery failure
In a lithium ion battery pack, where there are several cells, fire generation from a single cell greatly increases the likelihood of cell-to-cell propagation, where one failing cell can propagate its failure to other cells in the pack, creating a very large and destructive event. This stage of a lithium ion battery failure is detectable by a heat detector.
The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and potentially serious consequences, such as thermal
First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural...
This study investigates a novel fault diagnosis and abnormality detection method for battery packs of electric scooters based on statistical distribution of operation data that are stored in the cloud monitoring platform. According to the battery current and scooter speed, the operation states of electric scooters are clarified, and the
This study investigates a novel fault diagnosis and abnormality detection method for battery packs of electric scooters based on statistical distribution of operation data that are stored in the cloud monitoring platform. According to the battery current and scooter speed,
Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social...
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
In some earlier works, Alavi et al. proposed a two-step state-estimation based algorithm for Li plating detection, which can lead to degradation and failure of the battery. The algorithm uses an electrochemical model, with
As such, lithium-ion battery packs in real-world operation scenarios are typically equipped with a battery management system (BMS) for condition monitoring, thermal management, equalization management, and fault diagnosis to ensure their safe and efficient operation [4], [5], [6]. The success of any BMS depends upon the accurate acquisition of data
Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in severe cases. Therefore, timely and accurate
Abusive lithium-ion battery operations can induce micro-short circuits, which can develop into severe short circuits and eventually thermal runaway events, a significant safety concern in lithium-ion battery packs. This paper aims to detect and quantify micro-short circuits before they become a safety issue. We develop offline batch least square-based and real-time gradient
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
First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural...
In some earlier works, Alavi et al. proposed a two-step state-estimation based algorithm for Li plating detection, which can lead to degradation and failure of the battery. The algorithm uses an electrochemical model, with the first step being estimating the insertion and extraction rates of Li ions using particle filtering, and the second step
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.
As the global lithium-ion batteries (LIBs) market continues to expand, the necessity for dependable and secure LIBs has reached an all-time high. However, the use of batteries is associated with a number of significant risks, including the potential for thermal runaway and explosions. The meticulous inspection of LIBs is not only essential for
First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data.
As discussed above, the faults diagnosis and abnormality of battery pack can be detected in real time. In addition, timely detection and positioning of faults and defects of cells can improve the health and safety of the whole battery pack.
In Ref. , a lithium-ion battery fault diagnosis system suitable for high-power scenarios is designed, and it can evaluate the degradation of lithium-ion batteries and conduct diagnosis with the full knowledge of internal fault mechanism. Ref.
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.
Fault diagnosis research in other fields has shown that the most effective approach is often a combination of more than one method . Lu et al. briefly presented fault diagnosis as one of the key issues for Li-ion battery management in electric vehicles.
However, the portability of the method is poor. The authors in ref (26) use the Kernel Principal Component Analysis (KPCA) approach to train a nonlinear data model for internal short-circuit detection of lithium-ion batteries. However, the method requires a large amount of historical data for offline training.
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