DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Li, Da; Zhang, Zhaosheng; Liu, Peng; Wang, Zhenpo

doi:10.3390/en12152977

Cited by 31 publications

(9 citation statements)

References 33 publications

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“…e findings reveal that the greater the abuse temperature, the higher the battery's inflection point and maximum temperature and the quicker the time it takes for the battery to reach thermal runaway. Furthermore, the results reveal that the battery's temperature change characteristics are identical under various working settings [26][27][28].…”

Section: Discussionmentioning

confidence: 92%

Research on the Early Warning Mechanism for Thermal Runaway of Lithium-Ion Power Batteries in Electric Vehicles

Wang

Jun-rui

Zhang

2022

Security and Communication Networks

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Lithium-ion power batteries are critical to the macrostrategy of new energy vehicles, and safety concerns such as thermal runaway remain a major bottleneck in the productization and industrialization of lithium batteries. Based on COMSOL Multiphasic, an electrochemical-thermal coupling model for lithium-ion power batteries is created, as well as a thermal model for thermal runaway. A comparative study on lithium-ion power batteries was conducted using this model under on-board conditions, high rate cyclic charging and discharging conditions, and thermal abuse conditions. The temperature rise rate, average temperature, current, and voltage of the lithium-ion power battery and their correlation with thermal runaway are investigated. The results show that the temperature rise rate can significantly characterize the occurrence of thermal runaway, and when the temperature rise rate exceeds 1°C/s, the lithium-ion power battery has a high probability of thermal runaway. In addition, when the average temperature exceeds 80°C, there is a potential risk of thermal runaway in Li-ion power batteries. The results of the study show that high temperatures take longer to trigger thermal runaway, and the battery is slower to warm up. The time it takes for thermal runaway to occur, the maximum temperature reached, and the maximum voltage are all strongly connected to the overcharge flow under overload abuse conditions. The shorter the time it takes for thermal runaway to occur and the greater the maximum temperature and voltage attained, the larger the overcharge current.

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Section: Discussionmentioning

confidence: 92%

Research on the Early Warning Mechanism for Thermal Runaway of Lithium-Ion Power Batteries in Electric Vehicles

Wang

Jun-rui

Zhang

2022

Security and Communication Networks

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“…Through evaluating a large quantity of real-world data, the stability, feasibility, necessity, robustness, and reliability of the technique were acknowledged and discussed. Moreover, comparison with another diagnosis approach was accomplished, and the outcomes demonstrate the advantage of the suggested technique [11].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of Internal Flowing Twisted Conduits for Cooling of Lithium-Ion Batteries through Thermal Characterization

2020

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Lithium-ion batteries are extensively used for electric vehicles, owing to their great power and energy density. A battery thermal management system is essential for lithium-ion batteries. With the extensive utilization of liquid-cooling approaches for lithium-ion batteries’ thermal management, temperature homogeneity is considerably influenced by coolant distribution. A lower temperature of the cooling fluid brings about a lower temperature of the cell, but the relation and the amount are important to be analyzed. The cooling efficiency is considerably influenced by the flowing conduit arrangement in the cooling plate. Different parameters are affected by the cooling performance of the battery pack. Consequently, the effect of entrance temperature of coolant fluid, current rate, environment temperature, entrance velocity of the coolant fluid, and plate material on the performance and efficiency of a battery thermal management system were investigated. In this investigation, the program ANSYS/FLUENT was employed as the numerical solver to solve the problem. The simulation was accomplished after the end of the discharge. It was seen that the temperature distributions were the most sensitive to the entrance velocity of coolant fluid. It was concluded that the entrance velocity of coolant fluid has the greatest impact on the cooling efficiency and performance of the cold plate.

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“…At present, according to the different research methods of battery cell inconsistency fault diagnosis, it can be roughly divided into statistical analysis methods, machine learning methods based on outlier detection, neural network algorithm, signal processing method based on information entropy analysis, and so forth. [20][21][22][23][24][25][26][27][28] Gasper et al 23 used machine learning-assisted model recognition methods to predict battery life, with uncertainty quantified by bootstrap resampling, and the uncertainty of life prediction is greatly reduced. Xia et al 7 established a model for short-term capacity estimation and long-term remaining useful life prediction of lithium-ion batteries based on data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

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

Cheng,

Li,

2023

Energy Science & Engineering

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The 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.

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DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Cited by 31 publications

References 33 publications

Research on the Early Warning Mechanism for Thermal Runaway of Lithium-Ion Power Batteries in Electric Vehicles

Research on the Early Warning Mechanism for Thermal Runaway of Lithium-Ion Power Batteries in Electric Vehicles

Design and Simulation of Internal Flowing Twisted Conduits for Cooling of Lithium-Ion Batteries through Thermal Characterization

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

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