Electrochemical impedance spectroscopy, or EIS, has been used for decades to study electrochemical systems. Since the mid-20th century, it has become an important method in fields such as corrosion research, physical electrochemistry, biosensors, coatings, fuel cells and battery development.
In battery research, EIS provides a non-destructive way to examine how a cell, module or pack responds to a small electrical perturbation. The result is a frequency-dependent “snapshot” of the battery’s electrochemical state. Because the method is based on the battery’s electrical response rather than on a specific chemistry, it can be applied across many different battery types.
EIS has therefore become a standard tool for battery characterisation. It can provide insight into internal resistance, charge-transfer behaviour, electrolyte and interface properties, ageing, degradation, state of charge and, when interpreted carefully, state of health.
Two common control modes are used in EIS: potentiostatic and galvanostatic. In potentiostatic EIS, the instrument applies a voltage signal and measures the resulting current response. In galvanostatic EIS, the instrument applies a current signal and measures the resulting voltage response. For batteries, galvanostatic EIS is particularly relevant because batteries are commonly charged and discharged under current-controlled conditions.
The foundations of EIS go back more than a century. Around 1899–1901, Warburg applied impedance concepts to electrochemical diffusion processes. In the 1940s, Randles’ work helped establish the equivalent-circuit foundations still widely used in electrochemical impedance analysis. Archie Hickling introduced the modern three-electrode potentiostat in 1942, and practical EIS instrumentation developed substantially during the 1960s and 1970s. Galvanostatic EIS became increasingly practical as modern potentiostat/galvanostat and frequency-response instrumentation matured.
Lars Eriksson’s invention of Batixt R3 EIS builds on galvanostatic EIS and extends it into a dynamic battery measurement regime. It addresses key limitations of conventional battery EIS — especially measurement speed, repeatability and the ability to measure during charge or discharge — and contributes to the broader development of EIS from a laboratory characterisation method toward a practical tool for battery research, production and diagnostics.
Galvanostatic electrochemical impedance spectroscopy, or galvanostatic EIS, is a method for studying how a battery responds to a controlled current signal.
In a typical galvanostatic EIS measurement, a small alternating current signal is applied to the battery, often as a sinusoidal AC signal. The resulting voltage response is recorded at the same frequency. By comparing the applied current and the measured voltage response, the impedance of the battery can be calculated.
Impedance contains two kinds of information: magnitude and phase. The magnitude describes how strongly the battery resists the applied signal, while the phase describes the time shift between the current excitation and the voltage response. For this reason, impedance is usually represented as a complex number, with both a real and an imaginary component.
Most conventional EIS measurements use small perturbation signals. The purpose is to disturb the battery as little as possible, so that its electrochemical behaviour can be observed under approximately linear and stable conditions. This is important because EIS interpretation normally assumes that the system does not change significantly during the measurement.
In traditional swept-frequency EIS, the measurement is performed one frequency at a time. The instrument applies a signal at one frequency, records the response, and then repeats the process at the next frequency. A complete EIS measurement therefore consists of many individual frequency points collected over a defined frequency range. Multisine EIS can shorten the measurement by applying several frequencies at once, although in many conventional implementations the number of simultaneous frequencies is still limited.
The selected frequency range depends on the battery, the test objective and the process being studied. Battery EIS measurements may span from very low frequencies, in the millihertz range, up to kilohertz or higher. Lower frequencies are often important for slow transport and diffusion-related processes, while higher frequencies are used to study faster electrical and electrochemical responses. In many practical battery applications, the most relevant information is found below the highest frequencies used in general-purpose EIS testing.
Measurement time is strongly influenced by the lowest frequency included in the test. Low-frequency measurements take longer because the signal period is longer. In addition, conventional EIS often requires the battery to be in a stable or near-equilibrium condition before each measurement. For this reason, rest periods between measurements can range from minutes to hours, depending on the test protocol and the desired accuracy.
EIS results are commonly visualised in a Nyquist plot. In a Nyquist plot, each frequency is represented by one point in the complex plane, with the real part of the impedance on one axis and the imaginary part on the other. When the points are connected, they form a curve that reflects how the battery responds across the measured frequency range.
EIS is one of the most information-rich methods available for studying battery behaviour.
Today, EIS is most commonly used in research laboratories, where it supports the development and evaluation of battery materials, cell designs and electrochemical systems. However, the potential value of EIS extends far beyond material characterisation. Because a battery is fundamentally an electrochemical system, understanding its impedance response can provide insight into many of the processes that determine performance, ageing, safety and lifetime.
Different parts of an EIS spectrum can be associated with different physical and electrochemical processes. High-frequency features are often linked to ohmic resistance, current collectors, contacts and electrolyte-related resistance. Mid-frequency features may reflect processes such as charge transfer, double-layer behaviour and interfacial phenomena. Lower-frequency features often contain information related to mass transport, diffusion and slower electrochemical processes.
These interpretations are not automatic. A battery is a complex system, and the same part of an impedance spectrum can sometimes be influenced by several overlapping processes. Temperature, state of charge, cell design, ageing state and test conditions all affect the measured response. For this reason, EIS is most powerful when measurements are made systematically and interpreted with an understanding of the underlying battery physics.
Used carefully, EIS can support many areas of battery development and diagnostics. It can help researchers compare materials, evaluate electrode design, study ageing mechanisms, investigate state of charge and state of health, and understand how a battery changes over time. In industrial settings, EIS has potential value for cell production, quality control, module and pack assessment, fault detection and lifetime prediction.
EIS is also relevant for the future of battery circularity. Large-scale reuse, repurposing and recycling of batteries require better diagnostic methods than simple voltage checks or capacity tests alone. To decide whether a battery can be reused, how it should be classified, or whether it is safe for continued operation, deeper insight into its internal electrochemical condition is needed. EIS is one of the most promising routes toward that kind of diagnostic understanding.
A typical EIS analysis involves three steps. First, the impedance spectrum is measured across a selected frequency range. Second, the measured data is compared with a model that can reproduce the shape of the impedance curve. Third, the model parameters are interpreted in relation to the physical and electrochemical properties of the battery.
The most common quantitative approach is equivalent circuit modelling. In this method, the battery is represented by an electrical circuit containing elements such as resistors, capacitors and diffusion-related components. These elements are not intended to be a literal map of the battery’s internal structure. Instead, they provide a simplified representation of processes that influence the impedance response.
Equivalent circuit modelling therefore requires judgement. A model must be chosen that is simple enough to be useful, but detailed enough to reflect the relevant physics of the system. Different circuit structures can sometimes fit the same data, which means that a good mathematical fit is not enough on its own. The model also needs to make physical sense.
Other analysis methods are also used, including distribution of relaxation times (DRT), physics-based modelling and data-driven approaches. Each method has strengths and limitations. Physics-based models can provide deeper mechanistic insight, while data-driven and AI-based models may become powerful when large volumes of high-quality, well-labelled EIS data are available.
One of the major challenges in battery EIS is that conventional measurements are often slow, complex and difficult to scale. This limits the amount of data that can be collected and makes it harder to build robust models across state of charge, temperature, ageing state and operating conditions. Better measurement methods, better metadata and better interpretation frameworks are all needed if EIS is to move from a specialist laboratory technique toward broader use in battery research, production and diagnostics.
