The application of electrochemical impedance spectroscopy (EIS) in battery science has evolved significantly since its inception, driven by the need for deeper insights into electrochemical processes within energy storage systems. Early developments in EIS can be traced back to the mid-20th century, when alternating current (AC) techniques were first employed to study electrode kinetics and interfacial phenomena. These foundational studies laid the groundwork for modern EIS methodologies, which now incorporate advanced signal processing, multi-frequency analysis, and dynamic testing protocols.

In the 1950s and 1960s, researchers began using simple sinusoidal perturbations to measure the impedance of electrochemical cells. The Randles equivalent circuit, proposed in 1947, became a cornerstone for interpreting impedance data, modeling charge transfer resistance, double-layer capacitance, and diffusion processes. Early battery studies focused on lead-acid systems, where EIS helped identify corrosion mechanisms and sulfation effects. The technique’s non-destructive nature made it attractive for probing battery health without disassembly.

The 1970s saw the adoption of frequency response analyzers (FRAs), which automated impedance measurements and improved accuracy. A key milestone was the work of Sluyters and collaborators, who refined the interpretation of Nyquist plots for battery electrodes. Their studies demonstrated how semicircles in the high-frequency region corresponded to charge transfer resistance, while low-frequency tails indicated diffusion limitations. This period also marked the first use of EIS in lithium-based systems, particularly in primary lithium cells, where interfacial stability was a critical concern.

Advancements in digital signal processing during the 1980s and 1990s enabled faster and more precise EIS measurements. The introduction of fast Fourier transform (FFT)-based analyzers allowed simultaneous multi-frequency excitation, reducing measurement time from hours to minutes. Researchers began applying EIS to lithium-ion batteries (LIBs) as they emerged in the 1990s. Studies by Aurbach and coworkers provided detailed insights into solid-electrolyte interphase (SEI) formation, linking impedance changes to electrolyte decomposition and passivation layer growth. These findings were pivotal for optimizing LIB performance and cycle life.

The early 2000s witnessed the integration of EIS with complementary techniques, such as differential capacity analysis, to deconvolute overlapping electrochemical processes. Researchers developed distributed equivalent circuit models to account for inhomogeneities in battery electrodes, moving beyond simplistic Randles-type models. A notable breakthrough was the identification of aging mechanisms through impedance spectroscopy, where increases in charge transfer resistance and SEI resistance were correlated with capacity fade. This era also saw the first attempts at in-situ EIS measurements, enabling real-time monitoring of battery degradation.

Modern EIS techniques have shifted toward dynamic and multi-sine methods, which offer superior speed and resolution. Multi-sine excitation, where multiple frequencies are applied simultaneously, has become a standard for high-throughput characterization. This approach minimizes measurement artifacts caused by battery relaxation effects, particularly in high-energy-density systems like lithium-sulfur and solid-state batteries. Dynamic EIS, which involves impedance measurements under transient conditions (e.g., during charge/discharge cycles), has provided new insights into kinetic limitations and transport phenomena.

Recent innovations include the use of distribution of relaxation times (DRT) analysis to resolve overlapping time constants in impedance spectra. DRT has proven especially valuable for identifying degradation modes in commercial LIBs, such as lithium plating and particle cracking. Another advancement is the coupling of EIS with machine learning algorithms, where impedance data is used to train models for state-of-health (SOH) prediction. These models can detect subtle changes in impedance signatures that precede catastrophic failure.

The development of high-frequency EIS (up to several MHz) has opened new avenues for studying interfacial phenomena at nanoscale dimensions. For example, researchers have employed high-frequency EIS to probe the ionic conductivity of thin-film solid electrolytes, revealing bottlenecks in all-solid-state battery designs. Similarly, ultra-low-frequency EIS (below 1 mHz) has been used to investigate slow diffusion processes in high-capacity electrodes, such as silicon anodes.

Technological breakthroughs in instrumentation have also played a critical role. Modern potentiostats with ultra-low impedance and high-current capabilities allow EIS measurements under realistic operating conditions, including high C-rates and extreme temperatures. The advent of wireless EIS systems has enabled impedance monitoring in large-scale battery packs, facilitating early fault detection in electric vehicle applications.

Despite these advancements, challenges remain in standardizing EIS protocols across different battery chemistries and formats. The interpretation of impedance data is often complicated by nonlinearities, especially in systems with phase transitions or multi-electron reactions. Recent efforts have focused on developing physics-based impedance models that incorporate thermodynamic and kinetic parameters derived from first principles.

The trajectory of EIS in battery science reflects a continuous push toward higher resolution, faster measurements, and deeper mechanistic understanding. From its roots in classical electrochemistry to its current role as a cornerstone of battery diagnostics, EIS has proven indispensable for unraveling the complexities of energy storage materials and devices. Future directions may include the integration of EIS with operando microscopy and spectroscopy techniques, offering a multi-modal view of battery behavior at multiple length and time scales. As battery technologies evolve toward higher energy densities and faster charging capabilities, EIS will remain a critical tool for ensuring performance, safety, and longevity.