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Due to the importance of examining the temperature of battery packs (BYP), a study on the temperature of a lithium-ion BYP with 9 cylindrical cells is performed in this article. The BYP is filled with phase change material (PCM) to better temperature uniformity on the battery cells. The batteries and the PCM pack are placed in an air channel with two inlets. At the inlets, 4 arc blades are supposed to guide the airflow to enhance the uniformity of cooling in the BYP. By changing the horizontal and vertical distance (VD) of the batteries inside the pack, this study is carried out in 2500 s for the charging and discharging times of the batteries (1.5C rate). The thermal analysis is done on the batteries using COMSOL software. The results show that an increment in the horizontal distance (HD) of batteries has less effect on battery temperature (T-BT) and PCM phase change compared to their VD. By enhancing the horizontal and VDs of the batteries, the value of the PCM phase change during the maximum T-BT is higher. The heat transfer coefficient (HTCT) between the PCM and the air is also decreased by increasing the distance between the batteries. The T-BTs is not significantly impacted by changing the distance between them. • Study thermal management of a lithium-ion BYP with 9 cylindrical cells is performed. • BYP is filled with PCM to better temperature uniformity on the battery cells. • T-BTs is not significantly impacted by changing the distance between them. • HTCT between the PCM and air is also decreased by increasing the distance. [ABSTRACT FROM AUTHOR]

Abstract: Systems integration issues, such as electrical and thermal design and management of full battery packs – often containing hundreds of cells – have been rarely explored in the academic literature. In this paper we discuss the design and construction of a 9kWh battery pack for a motorsports application. The pack contained 504 lithium cells arranged into 2 sidepods, each containing 3 modules, with each module in a 12P7S configuration. This paper focuses particularly on testing the full battery pack and diagnosing subsequent problems related to cells being connected in parallel. We demonstrate how a full vehicle test can be used to identify malfunctioning strings of cells for further investigation. After individual cell testing it was concluded that a single high inter-cell contact resistance was causing currents to flow unevenly within the pack, leading to cells being unequally worked. This is supported by a Matlab/Simulink model of one battery module, including contact resistances. Over time the unequal current flowing through cells can lead to significant differences in cells’ state of charge and open circuit voltages, large currents flowing between cells even when the load is disconnected, cells discharging and aging more quickly than others, and jeopardise capacity and lifetime of the pack. [Copyright &y& Elsevier]

We discuss a rapid testing of capacity in Li-ion battery packs with impedance-measuring technology to evaluate their capability in mobile phones. Our measurements of the capacity and impedance at 1 kHz for various kinds of battery packs conclusively proved that there is a strong correlation between capacity and impedance. It can be applied even when the pack consists of not only a Li-ion battery but also a Positive Temperature Coefficient (PTC) device and a protection circuit in-series, and this correlation is largely unaffected by the degree of charge. The results we obtained from measuring impedance revealed the possibility of quickly assessing degradation in Li-ion battery packs. [Copyright &y& Elsevier]

Lithium-ion battery packs are widely deployed as power sources in transportation electrification solutions. To ensure safe and reliable operation of battery packs, it is of critical 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 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 diagnosis coefficient is determined based on the Gaussian distribution to highlight the parameter variation in each state. On this basis, the K-means clustering algorithm, the Z-score method and 3σ screening approach are exploited to detect and locate the abnormal cells. By analyzing the abnormalities hidden beneath the external measurement and calculating 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. Experimental results validate that the proposed method can accurately diagnose faults and monitor the status of battery packs. This theoretical study with practical implications shows the promising research direction of combining data mining technologies with machine learning methods for fault diagnosis and safety management of complex dynamical systems. • The operation state of electric scooter is classified by current and speed. • A faults diagnosis and state monitoring scheme is proposed for lithium-ion battery. • The outlier screening algorithm is employed to detect and locate abnormal cell. • Fault frequency is imported to quantitatively evaluate healthy status of battery. [ABSTRACT FROM AUTHOR]