Micro short detection framework in lithium-ion battery pack is presented. Offline least square-based and real-time gradient-based SoH estimators are proposed. SoH estimators accurately estimate cell capacity, resistances, and current mismatch.
The lithium-ion battery monitoring system proposed in this study consists of subordinate modules, main control modules, and host computers.
This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT-ZigBee technology.
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.
Once the connection is successful, the operational data of the lithium-ion battery can be displayed not only on the local host computer, but also on the local monitoring center. Figure 11. Server program. Figure 12. Client program. 3.2.5. Warning Function
Micro short detection framework in lithium-ion battery pack is presented. Offline least square-based and real-time gradient-based SoH estimators are proposed. SoH estimators accurately estimate cell capacity, resistances, and current mismatch. Micro short circuits are identified by cell-to-cell comparison of current mismatch.
By analyzing the abnormalities hidden beneath the external measurement and calcg. the fault frequency of each cell in pack, the proposed algorithm can identify the faulty type and locate the faulty cell in a timely manner. Exptl. results validate that the proposed method can accurately diagnose faults and monitor the status of battery packs.
Micro short detection framework in lithium-ion battery pack is presented. Offline least square-based and real-time gradient-based SoH estimators are proposed. SoH estimators accurately estimate cell capacity, resistances, and current mismatch.
Lithium-ion battery, a high energy density storage device has extensive applications in electrical and electronic gadgets, computers, hybrid electric vehicles, and electric vehicles. This paper ...
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 and diagnose the running faults in a timely manner. This study investigates a novel fault diagnosis and abnormality detection method ...
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 …
Use battery safety sensors (BASs) to quickly detect thermal runaway conditions in li-ion battery packs to prevent damage in EVs and battery storage systems.
Timely and accurate fault diagnosis for a lithium-ion battery pack is critical to ensure its safety. However, the early fault of a battery pack is difficult to detect because of its unobvious fault effect and nonlinear time-varying characteristics. In this paper, a fault diagnosis method based on relative entropy and state of charge (SOC) estimation is proposed to detect …
DOI: 10.1016/j.jclepro.2020.120277 Corpus ID: 213338368; Internal short circuit detection for lithium-ion battery pack with parallel-series hybrid connections @article{Yue2020InternalSC, title={Internal short circuit detection for lithium-ion battery pack with parallel-series hybrid connections}, author={Pan Yue and Xuning Feng and Zhang Mingxuan and Xuebing Han and …
This article, part of a three-part series, presents a novel algorithm exploiting the data collected while charging a battery pack for diagnosing faults. The algorithm works off-board, thus not adding any computational burden or weight to the aircraft while minimising aircraft turn-around time, which is crucial for commercial flight operation.
The thermal runaway of an electric vehicle (EV) battery can cause severe loss of property and human life. A cell short circuit can lead to thermal runaway in a minutes. Therefore, battery short circuit detection systems are important for prevention and limitation of EV fire incidents. This paper proposes a short circuit detection and isolation method for lithium-ion battery packs …
The multi-fault diagnosis of a lithium-ion battery pack was accomplished based on relative entropy and SOC estimation, including battery short-circuit fault, voltage sensor fault and temperature sensor fault.
A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles. IEEE Trans. Power Electron. 2019, 34, 9709–9718. [Google Scholar] Zheng, C.; Chen, Z.; Huang, D. Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filter.
This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT …
Fault diagnosis, hence, is an important function in the battery management system (BMS) and is responsible for detecting faults early and providing control actions to minimize fault effects, to ensure the safe and reliable operation of the battery system. This paper provides a comprehensive review of various fault diagnostic algorithms ...
Smiths Detection now offers reliable and accurate lithium battery detection as an option on the HI-SCAN 100100V-2is and 100100T-2is scanners, with other conventional X-ray …
Smiths Detection now offers reliable and accurate lithium battery detection as an option on the HI-SCAN 100100V-2is and 100100T-2is scanners, with other conventional X-ray systems to follow. Existing installations can also be upgraded on site.
The multi-fault diagnosis of a lithium-ion battery pack was accomplished based on relative entropy and SOC estimation, including battery short-circuit fault, voltage sensor …
A lithium iron phosphate battery with a rated capacity of 1.1 Ah is used as the simulation object, and battery fault data are collected under different driving cycles. To enhance the realism of …
Fault diagnosis, hence, is an important function in the battery management system (BMS) and is responsible for detecting faults early and providing control actions to …
Download page for Limon™ battery management system (BMS) diagnostic and configuration tool.
10s–16s Lithium-ion (Li-ion), LiFePO4 battery pack design. It monitors each cell voltage, pack current, cell and MOSFET temperature with high accuracy and protects the Li-ion, LiFePO4 battery pack against cell overvoltage, cell undervoltage, overtemperature, charge and discharge over current and discharge short-circuit situations. It adopts high-side N-channel MOSFET …
This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT-ZigBee technology. The system operates in a master-slave mode, with the subordinate module collecting and fusing multi-source sensor data, while the master control module uploads the ...
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 …
Ansys Fluent is used to generate experimental datasets and simulate the thermal imaging of lithium-ion batteries under three different conditions: a single-cell battery, a 1P3S battery pack, and a flattened 1P3S battery pack model. Our method has shown that the model has a diagnostic recall and accuracy of 0.95 for thermal faults in lithium-ion batteries …
In practical application on EV, the battery management system (BMS) is responsible for detecting the performance degradation of power battery and conducting the estimation algorithms. However, the online SOH estimation algorithms applied on early EV models have the limited precision, and it is difficult for the additional data acquisition device to …
This article, part of a three-part series, presents a novel algorithm exploiting the data collected while charging a battery pack for diagnosing faults. The algorithm works off …
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 ...
A lithium iron phosphate battery with a rated capacity of 1.1 Ah is used as the simulation object, and battery fault data are collected under different driving cycles. To enhance the realism of the simulation, the experimental design is based on previous studies ( Feng et al., 2018, Xiong et al., 2019, Zhang et al., 2019 ), incorporating fault fusion based on the fault characteristics.
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