Optimization of Hybrid Pulse Power Characterization (HPPC) Protocol for Lithium-Ion Battery Diagnostics
Authors: Rhyu Jinwook , Zhuang Debbie , Bazant Martin Z. , Braatz Richard D. 
Published: December 2024

The importance of accurate diagnostics in lithium-ion battery applications has received increasing emphasis, especially after the formation step to assess the quality of the solid-electrolyte interphase layer and before second-life usage to investigate whether the recycled battery meets safety and performance requirements [1, 2]. Among diagnostic methods, hybrid pulse power characterization (HPPC) tests are promising as they extract relatively large information from high-rate perturbations from a near-equilibrium state by undergoing a sufficiently long rest step before applying the pulses [3]. However, traditional HPPC protocols (that is, using fixed magnitudes of charge/discharge pulses at uniformly distributed states of charges) provide superficial information about the battery health by only capturing the convoluted symptoms instead of investigating the physical cause of the degradation [3]. To overcome this limitation, a novel diagnostic method of using voltage pulses for HPPC protocols (i.e., smart HPPC) was proposed to successfully separate the impact of the individual degradation mechanisms on current responses [3].
While smart HPPC has shown decent performance in identifying individual degradation mechanisms such as film resistance, surface blockage, and electrolyte loss, this work aims to make the protocol even smarter by (1) designing reaction model/electrode material-specific HPPC protocols, (2) performing a quantitative identifiability analysis by considering the uncertainty of estimated degradation parameters, (3) validating the robustness of designed HPPC protocols by performing simulations in various hypothetical situations, and (4) incorporating information from the previous diagnostic results to improve the next round of diagnostics. In this work, a novel framework for designing a set of optimal HPPC protocols is described which is composed of three steps: (1) formulating the parametric uncertainty using a model-based design of experiment (DOE) method and the diagnostic time using scaling analysis, (2) constructing a Pareto front by solving a two-objective optimization, and (3) evaluating the robustness of the designed HPPC protocol by performing Markov Chain Monte Carlo (MCMC) simulations with various degradation states. This work demonstrates the application to a Nickel-Cobalt-Aluminum (NCA)-graphite electrode full-cell with the coupled ion-electron transfer (CIET) reaction model. Our framework could reduce the total diagnostic time by up to 45% of the traditional HPPC protocol while retaining accuracy for identifying all degradation mechanisms.
The information dependency on the state of charge (SOC) is investigated in our work through simulation results from overpotential-balanced full cells, showing that the pulses performed with the lower filling fraction of the overpotential dominant electrode are more informative than the pulses with the higher filling fraction. This result allows us to design an innovative shallow-depth HPPC protocol that only explores the SOC regions with high information, which dramatically reduces the total diagnostic time.
References
[1] A. Weng, P. Mohtat, P. M. Attia, V. Sulzer, S. Lee, G. Less, A. Stefanopoulou. "Predicting the impact of formation protocols on battery lifetime immediately after manufacturing." Joule 5.11 (2021): 2971-2992.
[2] R. Xiong, Y. Pan, W. Shen, H. Li, F. Sun. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives." Renewable and Sustainable Energy Reviews 131 (2020): 110048.
[3] D. Zhuang, M. L. Li, V. N. Lam, R. D. Braatz, W. C. Chueh, M. Z. Bazant, “Physics-informed design of hybrid pulse power characterization (HPPC) tests for rechargeable batteries.” ChemRxiv (2023).

Figure 1