Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, current sensor fault, and temperature sensor fault.
This paper proposes a feature engineering-driven multi-scale voltage anomaly detection method for lithium-ion batteries in EVs to address these issues. First, the voltage signals collected by the onboard battery management system (BMS) are divided into charging and discharging segments based on current and SOC.
In the data -driven approaches, the signal processing methods are mainly used for battery fault diagnosis, rather than ma chine learning -based methods. Sensor faults and actuator faults usually affect the external signals of the battery, such as voltag e, curre nt, and tempe rature.
Many existing studies have shown that when there are various abnormal faults in the battery, the voltage of the battery exhibits more pronounced fluctuations compared to other data during abnormal conditions. Therefore, voltage anomaly is an extremely important fault indicator in battery anomaly detection.
Battery fault diagnosis and thermal runaway warnings hold significant implications for the safety of electric vehicles. However, developing a reliable battery fault detection method that encompasses voltage anomaly patterns remains challenging due to the concealment and uncertainty of anomalies under complex profiles.
However, developing a reliable battery fault detection method that encompasses voltage anomaly patterns remains challenging due to the concealment and uncertainty of anomalies under complex profiles. A framework for detecting battery multi-scale voltage anomalies using feature engineering is proposed.
Using the difference between the true SOC and the estimated SOC as the residual, the fault detection of the voltage sensor and the current sensor of the lithium-ion battery pack is cleverly realized. Only fault detection and fault isolations are discussed; the fault size and shape cannot be obtained.
Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, current sensor fault, and temperature sensor fault.
Liu et al. (2024) proposed a multi-fault diagnosis method for LFP battery packs that employs a non-redundant interlacing voltage measurement topology to detect battery voltage and capture …
Aiming at the phenomenon of individual battery abnormalities during the actual operation of electric vehicles, this paper proposes a lithium-ion battery anomaly detection method based on the STL and improved Manhattan …
Li-ion battery output voltage residual – Noisy and denoised signals. Step 2.1.3. Fault detection features: In Figure 13, it easy to see the impact of the injected fault in the …
The experimental results show that the hybrid model proposed in this study outperforms the state-of-the-art techniques such as informer and transformer in voltage fault prediction by achieving MAE, MSE, and MAPE metrics of 0.009272%, 0.000222%, and 0.246%, respectively, and maintains high efficiency in terms of the number of parameters and runtime.
Before leaving the factory, lithium-ion battery (LIB) cells are screened to exclude voltage-abnormal cells, which can increase the fault rate, troubleshooting difficulty, and degrade pack performance. However, the time …
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 ...
The battery overvoltage or undervoltage fault can be diagnosed using the threshold-based method. The voltage information collected by the voltage sensor is compared …
Firoozi et al. [16] proposed a framework for real-time detection of battery voltage and thermal faults, ... Based on the feature extraction methods mentioned in Section 3, real-time feature extraction and fault diagnosis are performed under the constant movement of the time window. We firstly use SVD for feature extraction. The size of the time window is set to 20, i.e., …
A data-driven local outlier factor-based method is proposed in Ref. [25] to achieve the voltage fault detection with high-order statistical information of sensor measurement. Besides, an interleaved voltage measurement topology is introduced in Ref. [26] to firstly discriminate the sensor fault from cell faults from the perspective of topology. In summary, …
3 · A multifunctional battery anomaly diagnosis method deployed on a cloud platform is proposed, meeting the needs of anomaly detection, localization, and classification. First, the …
The experimental results show that the hybrid model proposed in this study outperforms the state-of-the-art techniques such as informer and transformer in voltage fault …
Chen et al. proposed an outlier-based battery voltage fault detection method. This method systematically combines the model-based system identification algorithm with the anomaly detection algorithm. First, the model parameters are identified to characterize the dynamic characteristics of the battery, and the fault detection problem is transformed into the …
Li-ion battery output voltage residual – Noisy and denoised signals. Step 2.1.3. Fault detection features: In Figure 13, it easy to see the impact of the injected fault in the windows (500,1500) seconds, where the SOC change by maximum 10%.
Liu et al. (2024) proposed a multi-fault diagnosis method for LFP battery packs that employs a non-redundant interlacing voltage measurement topology to detect battery voltage and capture fault characteristics through threshold detection.
The battery overvoltage or undervoltage fault can be diagnosed using the threshold-based method. The voltage information collected by the voltage sensor is compared with the preset threshold. When the battery voltage exceeds the threshold, the fault occurrence state and fault occurrence time are defined [13]. Pre-processing the collected data ...
In this paper, a novel fault diagnosis method for lithium-ion batteries of electric vehicles based on real-time voltage is proposed. Firstly, the voltage distribution of battery cells …
This paper proposes a feature engineering-driven multi-scale voltage anomaly detection method for lithium-ion batteries in EVs to address these issues. First, the voltage signals collected by the onboard battery management system (BMS) are divided into charging and discharging …
Battery fault diagnosis and thermal runaway warnings hold significant implications for the safety of electric vehicles. However, developing a reliable battery fault detection method that encompasses voltage anomaly patterns remains challenging due to the concealment and uncertainty of anomalies under complex profiles. A framework for detecting battery multi-scale voltage …
Firstly, the SD and the IPCC are extracted as the two-dimensional fault features characterizing the voltage fluctuation, in which the IPCC method makes up for the defect of the traditional PCC method that is unable to detect the progressive voltage fluctuation fault. Then, based on the analysis of real-world vehicle data, we found that the two ...
The latter solely relies on the measurable characteristic parameters of the battery, such as voltage, current, internal resistance, SOC, temperature, etc., without requiring a physical model of the battery (Komsiyska et al., 2021).Entropy is a tool that describes the degree of randomness or disorder in the time series data of a system, and it is widely used as a fault …
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 ...
Aiming at the phenomenon of individual battery abnormalities during the actual operation of electric vehicles, this paper proposes a lithium-ion battery anomaly detection method based on the STL and improved Manhattan distance algorithms. First, the original voltage data of battery cells is decomposed using the STL algorithm, which allows the ...
3 · A multifunctional battery anomaly diagnosis method deployed on a cloud platform is proposed, meeting the needs of anomaly detection, localization, and classification. First, the proposed method extracts four anomaly features from discharge voltage to indicate battery anomalies. A risk screening process is applied to classify vehicles into high ...
This paper proposes a feature engineering-driven multi-scale voltage anomaly detection method for lithium-ion batteries in EVs to address these issues. First, the voltage signals collected by the onboard battery management system (BMS) are divided into charging and discharging segments based on current and SOC. Second, 225 DIs are constructed ...
Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, …
Detecting the voltage fault accurately is critical for enhancing the safety of battery pack. Therefore, this paper presents a voltage fault detection method for lithium-ion battery pack using local outlier factor (LOF). The proposed method systematically incorporates a model-based system identification algorithm into an outlier detection ...
In this paper, a novel fault diagnosis method for lithium-ion batteries of electric vehicles based on real-time voltage is proposed. Firstly, the voltage distribution of battery cells is confirmed in electric vehicles, and the reasons are analyzed. Furthermore, kurtosis is utilized to discover cell faults for the first time. After the kurtosis ...
Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and...
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