arXiv subjects

Zhiwen Wan

Publications and source records attributed to Zhiwen Wan.

3 recordsLinked to original sources

arXiv2026

Health feature extraction from battery energy storage system field fault data

Health monitoring methods are critical for lithium-ion battery modules connected to the grid to prevent faults that can lead to catastrophic events. However, assessing the health of cells in modules from their operational data presents challenges including variable operating conditions, which directly confound health features, and sparse sensing in the modules, particularly within cells in parallel, which prevents observing critical states of individual cells. Here, we present a framework for extracting and calibrating health features for battery modules from their operational data to identify discriminative features for separating faulty parallel-connected cell groups within the modules. We applied this framework to operational data from 25 commercial grid-connected lithium-ion Battery Energy Storage System (BESS) modules. Each module consisted of 14 series-connected parallel groups, one of which was confirmed as faulty via post-mortem investigation; in total, the dataset included 25 faulty and 325 non-faulty cell groups. A statistical evaluation of these calibrated features demonstrated that group-level capacity, capacity degradation rate, and dV/dQ peak heights separate faulty parallel-connected cell groups within the modules with statistical significance (p<0.05). Conversely, group internal resistance did not (p>0.05), indicating that increased resistance was not a primary characteristic of the faults in this dataset. These findings challenge the exclusive reliance on resistance features for fault detection. The observed feature signatures suggest potential failure mechanisms, furthering the understanding of fault behavior in lithium-ion battery modules during field operation. More importantly, this work demonstrates a framework for robustly monitoring the health of cells in lithium-ion battery modules under real-world operations.

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arXiv2026

Modeling and Estimation of Solid Electrolyte Interphase during Formation in Battery Manufacturing

The solid electrolyte interphase (SEI) - a critical passivation layer that governs the longevity, safety, and efficiency of lithium-ion batteries - is created during the last step in cell manufacturing called cell formation. Conventional cell formation protocols are largely empirical, resulting in long processing times and limited control over the SEI growth rate that influences SEI quality and lifetime performance. This paper develops a control-oriented, semi-empirical model to estimate SEI thickness growth from terminal voltage and cell expansion measurements acquired in-operando during manufacturing using low-cost micrometer-precision integrated-sensing fixture. Model parameters are calibrated against cell formation data, and an unscented Kalman filter is employed to estimate the SEI film growth. The results lay the foundation for future closed-loop control of SEI growth, enabling high-quality and more efficient formation processes.

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arXiv2019

UAV-aided urban target tracking system based on edge computing

Target tracking is an important issue of social security. In order to track a target, traditionally a large amount of surveillance video data need to be uploaded into the cloud for processing and analysis, which put stremendous bandwidth pressure on communication links in access networks and core networks. At the same time, the long delay in wide area network is very likely to cause a tracking system to lose its target. Often, unmanned aerial vehicle (UAV) has been adopted for target tracking due to its flexibility, but its limited flight time due to battery constraint and the blocking by various obstacles in the field pose two major challenges to its target tracking task, which also very likely results in the loss of target. A novel target tracking model that coordinates the tracking by UAV and ground nodes in an edge computing environment is proposed in this study. The model can effectively reduce the communication cost and the long delay of the traditional surveillance camera system that relies on cloud computing, and it can improve the probability of finding a target again after an UAV loses the tracing of that target. It has been demonstrated that the proposed system achieved a significantly better performance in terms of low latency, high reliability, and optimal quality of experience (QoE).

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