Realistic Diagnostics to Evaluate Imbalance and Heterogeneity of Lithium-Ion Battery Modules
Authors: Tanim Tanvir R , Dufek Eric J. , Walker Lee K. , Ho Chinh 
Published: May 2020

Lithium-ion battery (LIB) packs for the electric drive vehicles (EDVs) consist of hundreds to thousands of unit cells. These multitudes of cells are arranged in various series-parallel configurations to build the complete pack. Ensuring the safety of these complicated packs in a myriad of operating conditions (including abuse conditions such as extreme fast charging, collisions, etc.) is a crucial requirement for the overall safety of the EDVs and is an open and evolving field of research. Pack safety undoubtedly relies on the inherent safety and abuse tolerance of the unit cell. However, the combination of many cells in packs introduces new and sometimes unknown pack dynamics, which could compromise the overall pack safety. Thus, advanced diagnostics and intelligent algorithms involving information from multiple levels, e.g., unit cells to modules to packs, are required to ensure the overall safety of the battery pack.
Many researchers have used the electrochemical impedance spectroscopy (EIS) to estimate the state of charge (SOC) and state of health (SOH) of batteries, notably in single cell settings [1-4]. The form factor and measurement time of conventional full spectra EIS, however, makes it unsuitable as an onboard diagnostic tool. The impedance measurement box (IMB) can generate the impedance spectra within 0.1-1638.4Hz in ten seconds [5-6]. This opens the possibility of using full frequency impedance spectra as an online diagnostic tool for battery SOC, SOH and state of stability estimation. Incremental capacity (IC) or dQ.dV-1 is another diagnostic method and has been primarily used for single cell’s aging and SOH estimation [7-8]. Full pack’s IC analysis is complex and challenging due to a multitude of intrinsic (material, manufacturing process, etc.) and extrinsic (environment, non-uniform current, etc.) uncertainties [9, 10] and has not been looked at extensively.
This paper includes experimental case studies to investigate whether rapid impedance and dQ.dV-1 diagnostic methods could identify and quantify different abnormal conditions in modules. Realistic in-vehicle abnormal conditions such as localized self-discharge and non-uniform aging were evaluated using automotive grade 16 Ah gr/NMC in series (up to 10S1P) and parallel (up to 1S4P) module settings at different SOCs. Understanding imbalance associated with localized self-discharge is crucial for long-term safety, especially for an aggressively used vehicle and following crash conditions [8]. On the other hand, cell-to-cell variability is a key concern for aged batteries as it may precede potential fault associated shorts from fast charging, or localized elevated temperature.
The analyses structure includes characterizing the measurement uncertainty (noise behavior) for each of the case study, followed by identifying distinct signals related to abnormal conditions above the noise floor. Baseline conditions with no abnormality were used for all the cases evaluated for detection signal identification. The impedance-based diagnostic method found to be sensitive to the string size and frequency range. For instance, the detection signal for the local imbalance is found to be more sensitive to the lower frequency, thus more likely to be detected. On the other hand, a higher frequency is suitable for cell-to-cell heterogeneity detection. Both the dQ.dV-1 and impedance-based diagnostic methods (with existing accuracy and precision) were sensitive to identify and detect series string's abnormality but struggled to detect parallel string issues.

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