Direction-Dependence in Li-Ion Battery Diagnostic Methods
Authors: Singh Anubhav , Barai Anup 
Published: December 2024

Lithium-Ion Batteries (LIBs) have been at the epicentre of the global push to electrify transport. Having already become mainstream in the automotive industry, vehicles powered by LIBs are gaining traction in other transport sectors such as aerospace and freight trucks. These sectors have more stringent requirements for performance as well as safety. Therefore, academia as well as industry are aggressively exploring battery diagnostic methods that can be implemented on-board [1]. A common feature shared among several well-developed tools for battery diagnostics is their reliance on an electrochemical technique called Incremental Capacity Analysis (ICA) [2,3]. ICA is ideal for in-field implementation because it: (a) requires only voltage and current data, which are available irrespective of application and (b) provides additional insights into electrode-level phenomena that are direct indicators of battery health. Therefore, ICA is also used by Battery Analytics companies for health and fault monitoring [4]. The long-term evolution in the understanding of ICA has captured the effects of factors such as C-rates, temperature, charge-discharge hysteresis etc. in literature [5]. However, one fundamental assumption omnipresent across all previous studies is the use of the entire battery voltage range: majority of ICA studies consist of the battery being charged/discharged over the entire 0-100% % State-of-Charge (SoC) interval (100 to 0 for discharge). While this assumption is valid for laboratory-based testing and validation, batteries in transport applications seldom undergo a complete charge/discharge. For instance, road EV manufacturers limit the available SoC windows to prolong the battery life. On the other hand, the electric aviation industry reserves 30% of the battery capacity for emergency scenarios. Therefore, there is a lack of studies as well as data considering the implementation of ICA with partial cycling data, which is a true representative of real-life use cases.

The presented work addresses the identified gap through an experimental investigation of ICA results obtained from partial charging data. Commercial Li-ion cells were cycled using multiple C-rates and Depths of Discharge (DOD). ICA was conducted on data obtained from full as well as partial charging regimes. As shown in Figure 1, the obtained ICA results highlighted a direction-dependence: the ICA curves from partial charge cycles commencing from the same SOC indicate a disparity based on the direction of the previous cycling history (charge or discharge). This direction-dependence has not been considered in battery research literature and could invalidate the previously established protocols of degradation diagnostics performed using ICA. Moreover, the results highlight a major deficiency in large battery dataset creation efforts: being generated to support the ultimate implementation of Artificial Intelligence (AI) [6] in predicting battery behaviour, the datasets are incomplete due to a lack of partial cycling data and would not be relevant for real-life application. Further work is ongoing to investigate the phenomenon further and evaluate the implications in current practices of battery diagnostics and large dataset generation.

Figure 1