New Model Enhances Lithium-Ion Battery Management Using Real-Time Impedance Analysis

Recently, a team of researchers from the South China University of Technology explored a novel approach to lithium-ion battery modeling. This study, which was published in the Journal of Power Sources, focuses on overcoming longstanding challenges in parameter identification for physicochemical battery models by advancing battery management systems (1).

Why is lithium-ion battery analysis important?

Lithium-ion battery analysis is a growing field in spectroscopy. Because lithium-ion batteries play a major role in many application areas, spectroscopy has been used to test battery performance, safety, and lifespan (2). Spectroscopic techniques routinely used for lithium-ion battery analysis include inductively coupled plasma–optical emission spectroscopy (ICP-OES), Raman spectroscopy, near-infrared (NIR) spectroscopy, ICP-mass spectrometry (ICP-MS), X-ray fluorescence (XRF), and micro-discharge optical emission spectroscopy (MDOES), to name a few (2).

Electric car lithium battery pack and power connections. | Image Credit: © xiaoliangge - stock.adobe.com

Lithium-ion batteries are commonly found in many consumer electronics used daily, including mobile phones and electric vehicles (2). These batteries are rechargeable energy storage devices created through an extensive manufacturing process (2). Key components of these devices include the cathode, anode, electrolyte, separator, current collector, and battery cell casing (2).

What did the researchers test in their study?

In their study, the researchers examined a new way to improve physicochemical modeling of lithium-ion batteries. Physicochemical models of lithium-ion batteries are considered highly promising because they can estimate critical internal electrochemical states that are not directly measurable (1). However, the problem is that, on a practical level, physicochemical models have been limited in their usage because of the complexity involved in parameter identification (1). Traditional methods rely heavily on terminal voltage measurements, which provide only limited information.

To address this issue, the researchers implemented a new spectroscopic technique called dynamic electrochemical impedance spectroscopy (DEIS), which can be measured in real time during charging (1). The idea behind using DEIS is that the technique offers a far richer characterization of battery behavior.

As part of the experimental procedure, the researchers developed a lumped-parameter version of joint time-frequency physicochemical models, significantly reducing the number of parameters that must be identified while maintaining robust predictive capability (1). Doing so allowed them to build a model that could accelerate the simulation of DEIS responses alongside voltage behavior.

What were the results of the study?

In their analysis, the team conducted a comprehensive sensitivity study of 24 model parameters under varying charging currents and impedance frequencies. The results revealed several important trends. For one, the researchers determined that low-current charging amplified the sensitivity of most parameters to impedance responses (1). The researchers also determined that, on the other end, high-current charging enhanced sensitivity to voltage responses (1). By ranking parameters according to their sensitivity across both response types, the researchers grouped the 24 parameters into eight categories, each with recommended strategies for parameter identification.

What are the key takeaways from these findings?

The main takeaway from this study is that, by using DEIS, the research team demonstrated that their method provides a pathway for more reliable parameter identification, ensuring that models can be fine-tuned with greater precision. According to the authors, this dual use of voltage and DEIS data paves the way for more advanced battery management systems capable of improving safety monitoring, prolonging lifespan, and optimizing performance across diverse applications (1).

Two vital contributions should be highlighted. First, the creation of a reduced-parameter joint time-frequency model that expands measurable outputs while simplifying parameter identification, and second, the establishment of a framework for selecting optimal response types to guide identification efforts (1). Together, these advancements bring the battery research community one step closer to achieving highly accurate, real-time monitoring and management of lithium-ion systems.

References

1. Chen, H.; Li, Z. Joint Time-frequency Physicochemical Modeling and Parameter Sensitivity Analysis on Dynamic Electrochemical Impedance Spectroscopy of Lithium-ion Batteries. J. Pow. Sour. 2025, 626, 235762. DOI: 10.1016/j.jpowsour.2024.235762
2. Workman, Jr., J. A Comprehensive Review of Spectroscopic Techniques for Lithium-Ion Battery Analysis. Spectrosc. Suppl. 39, s11, 6–16. DOI: 10.56530/spectroscopy.ii3689u3