Impedance Spectroscopy Battery SOH — PatSnap Eureka
Impedance Spectroscopy for Non-Invasive Battery State-of-Health Estimation
Over 60 patents from Samsung SDI, LG Energy Solution, Huawei, Toyota, and leading universities reveal how electrochemical impedance spectroscopy is becoming the dominant non-invasive SOH methodology for EVs, grid storage, and consumer electronics — now accelerated by on-device EIS and machine learning.
How Impedance Spectroscopy Encodes Battery Aging
Electrochemical impedance spectroscopy operates by applying a small-amplitude sinusoidal excitation signal across a battery at a range of frequencies and recording the complex voltage-current response. The resulting impedance spectrum encodes physically interpretable electrochemical information — ohmic resistance, charge-transfer resistance, double-layer capacitance, and Warburg diffusion impedance — each appearing at characteristic frequency ranges. As described by Central South University's 2025 patent, the impedance dataset consists of three arrays: frequency, real impedance part, and imaginary impedance part.
The equivalent circuit model (ECM) fitting approach is one of the most widely deployed strategies for translating raw EIS data into SOH estimates. China Inspection and Quarantine Science Research Institute (2020) constructs an ECM, identifies the ohmic internal resistance R from the impedance spectrum for batteries at 100% SOC, and derives the SOH using the linear formula SOH = ax + b, where x = R − R_new. This non-destructive approach was explicitly validated as a lossless method capable of estimating health state without interrupting battery operation.
Advanced Measurement Technology Inc. (2022) extends this further: their system generates EIS spectra for battery modules, fits them to an ECM to establish equivalent circuit fit parameters, then combines those parameters with measured open-circuit voltage and temperature to derive weighting factors — ultimately producing a weighted SOH measurement that compensates for thermodynamic operating conditions. According to leading battery management teams, this multi-parameter fusion approach systematically outperforms single-modality assessments.
Shandong University's dual patents (2024–2025) extend beyond standard ECM fitting by predicting the distribution of relaxation times (DRT) from impedance data collected at multiple specific frequency bands. The DRT provides a more granular deconvolution of overlapping electrochemical processes and is claimed to enable low-cost, real-time, high-accuracy SOH monitoring with significant implications for electric vehicle battery management systems.
Filing Landscape and Technical Approach Distribution
Derived from analysis of 60+ patents filed between 2007 and 2026 across five jurisdictions, mapped by PatSnap Eureka.
Patent Filing Distribution by Jurisdiction (2007–2026)
China leads with the highest volume of EIS battery SOH filings, followed by Korea — reflecting the concentration of major cell manufacturers and university research programmes in East Asia.
Technical Approach Distribution Across Patent Corpus
Three dominant approaches emerge: direct EIS with ECM fitting, operational signal impedance extraction, and machine learning inference — with ML integration growing fastest in recent filings.
Moving Beyond Laboratory Instruments
A substantial portion of the corpus solves the deployment problem: how to perform EIS on batteries in the field without expensive, slow laboratory instruments. According to patent analytics on this corpus, this is now technically achievable using existing BMS hardware.
BMS-Native Impedance Acquisition During Normal Operation
Huawei's approach has the battery management system apply multiple excitation signal groups of different amplitudes during normal battery operation, selecting the most accurate resulting impedance spectrum as the working-condition impedance, then sending it to a server for correction. The method avoids interrupting normal device operation — a critical requirement for field-deployed energy storage systems.
No operational interruptionDigital Spectral Transform Without Battery Rest
Texas Instruments demonstrates a purely digital approach: digital samples of voltage and current during a measurement period are transformed via spectral operations; a second voltage spectral component accounts for battery state transitions before and after the measurement window. The patent explicitly notes that single-frequency impedance measurements can track core temperature (with temperature differentials up to 20°C between core and casing), SOC, and SOH as a function of depth-of-discharge.
20°C core-casing ΔT detectableTransient Vehicle Events as Opportunistic EIS Excitation
Rather than using a dedicated signal generator, Isuzu's device calculates impedance spectra from the frequency components naturally present in the battery current and voltage waveforms at the moment an operating state change produces a step-like current transition — for instance, motor start-up or regenerative braking. This approach reuses transient events inherent to vehicle operation as opportunistic EIS excitation sources.
Zero dedicated hardwareEarliest On-Vehicle EIS: Variable Charge/Discharge Periods
Toyota's early patents established the principle of using variable charge/discharge control periods from an on-board generator to excite impedance measurement, deriving internal impedance at each operational frequency and comparing it against a degradation detection map. These are among the earliest filed patents in the dataset demonstrating that on-vehicle EIS is operationally viable — filed in 2007 and 2011 respectively.
Earliest on-vehicle EIS (2007)Decoupling Aging from Temperature and SOC in Impedance Data
The most persistent engineering challenge in EIS-based SOH: measured impedance is simultaneously influenced by temperature, state-of-charge, and aging. These patents specifically address this degeneracy problem.
| Assignee & Year | Strategy | Key Technical Claim | Application |
|---|---|---|---|
| Alps Green Devices 2016 · JP & CN | Frequency Selection | Measures impedance at temperature-invariant frequencies (4 kHz and/or 500 kHz) for Li-ion batteries; averages multiple such frequencies to reduce measurement error | SOH/SOC estimation without concurrent temperature sensor |
| Southwest Jiaotong University 2025 · CN | Joint Estimation | Uses imaginary impedance at a first target frequency to estimate battery temperature; uses estimated temperature to select a second target frequency most informative for SOH estimation | Joint temperature and SOH estimation from impedance data alone |
| Sookmyung Women's University 2025–2026 · KR | Singularity Tracking | Processor iteratively adjusts measurement frequency based on predefined accuracy target, homing in on impedance singularity points defined by specific magnitude relationships between imaginary impedance components at adjacent frequencies | Reduced measurement time while preserving informativeness |
| Shenzhen Power Supply Bureau 2025 · CN | Multi-Harmonic Injection | Simultaneously excites multiple frequency components in a single current signal to acquire impedance spectrum data rapidly; determines SOH through ECM parameter fitting, internal resistance growth models, and capacity degradation model fusion | Rapid multi-frequency EIS for grid-side energy storage |
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How Neural Networks Are Transforming EIS-Based SOH
A rapidly growing segment of the patent corpus integrates machine learning models directly with EIS data, treating impedance spectra as feature inputs rather than fitting them to explicit physical circuit models.
CNN End-to-End Impedance-to-State Mapping
Noum Engineering (2021) describes a system where a series of complex impedance measurements ordered by measurement frequency are fed directly into a convolutional neural network (CNN), which outputs battery state. The method operates without intermediate physical model fitting — the CNN learns the impedance-to-state mapping end-to-end from training data, substantially reducing the need for domain expertise in ECM construction at the cost of training data requirements. See more via PatSnap analytics.
Deep Learning on Nyquist Plot Morphology
Beijing University of Aeronautics and Astronautics (2022) combines an optimized fast EIS acquisition procedure with deep learning image recognition and prediction algorithms trained on EIS curves, enabling non-destructive SOH identification. The neural network identifies SOH state from the visual morphology of the Nyquist plot, accommodating both offline standard EIS testing and online rapid EIS testing modes.
Hybrid Neural Network + Particle Filter Architecture
Shanghai Jiao Tong University (2022) develops a hybrid architecture: neural networks are trained to fit EIS parameters as functions of temperature, SOC, and aging state; an online particle filter estimates operational SOC and aging state; periodic in-situ EIS measurements recalibrate the neural network parameters and flag anomalous cells. This architecture provides both real-time tracking and scheduled recalibration, as detailed in PatSnap's R&D intelligence resources.
PCA + CNN + BiLSTM + Attention Composite Pipeline
Nantong Lechuang New Energy (2023) chains PCA dimensionality reduction → CNN spatial feature extraction → BiLSTM temporal sequence modeling → attention mechanism for feature weighting → SOH prediction. The composite architecture is designed to capture both the spatial structure of the Nyquist plot and the temporal evolution of impedance with cycling, addressing two complementary aspects of EIS-based aging characterization.
EIS-Based SOH Across Every Battery Use Case
The patent corpus spans electric vehicles, stationary energy storage, consumer electronics, spacecraft, aerospace, and used battery second-life evaluation — each with distinct engineering requirements.
BMS-Integrated EIS During Normal Charging
LG Energy Solution (2025) collects EIS data from a battery pack, identifies feature points in the spectrum, calculates a resistance parameter from those feature point impedances, and maps it to SOH using a pre-characterised function that accounts for temperature dependence. Samsung Electronics (2024) adds low-frequency current or voltage perturbations to constant-current or constant-voltage charging cycles, computing internal impedance and open-circuit voltage to determine SOH during the normal charging process — requiring no dedicated test window.
No dedicated test window requiredDynamic Impedance Under Real Grid Operating Conditions
State Grid Shanghai Electric Power Company (2021) uses dynamic impedance — the impedance extracted from real operational charge/discharge conditions rather than controlled sinusoidal excitation — as the health indicator, enabling online SOH assessment under complex grid-side operating conditions without requiring battery rest periods. For nuclear power backup systems, Hainan Nuclear Power (2024) uses periodic in-situ impedance spectroscopy to continuously correct SOH without interrupting the float-charge operational state — enabling continuous health monitoring in safety-critical infrastructure where disruptive capacity testing is unacceptable.
No battery rest requiredNon-Destructive Pack Grading Without Disassembly
Soongsil University (2021) applies a perturbation signal to a decommissioned battery module and measures the response to generate a full impedance spectrum — providing a non-destructive characterisation pathway for second-life battery grading without disassembly. LG Energy Solution (2024) directly addresses inferring individual cell impedance spectra from pack- or module-level EIS measurements combined with pulse discharge data, enabling SOC and SOH determination without disassembling battery packs to the cell level — a major cost and logistics advantage for both in-service monitoring and second-life assessment.
No disassembly neededSolid-State Battery EIS via Nyquist Arc Peak Frequency
Toyota Motor Corporation (2024) targets solid-state batteries specifically, deriving a peak frequency from the circular arc in the Nyquist plot and correlating its shift before and after degradation tests to a state model — an important extension of EIS methodology to next-generation solid-electrolyte battery chemistries. For portable batteries, Mintec Co., Ltd. (2025) uses a magnetic field sensor to measure battery cell current, derives the actual cell voltage accounting for protection circuit efficiency, and computes the actual impedance from the magnitude and phase difference — enabling non-contact, non-invasive impedance measurement for consumer electronics applications.
Solid-state + portable coverageWho Is Leading EIS Battery SOH Innovation?
Samsung SDI holds the largest cluster of patents in the dataset, primarily centred on adaptive filter-based G/H parameter extraction from operational voltage and current data — a proprietary non-EIS approach to SOH estimation that delivers real-time performance without spectroscopy hardware. Samsung SDI also has an ANN-based SOH estimation patent (2025) featuring attention map reliability scoring.
LG Energy Solution / LG Chem files across multiple EIS and impedance modalities: module-level EIS decomposition to cell level, resistance profile eigenvalue analysis, and SOH prediction via EIS feature points. Their pack-to-cell impedance decomposition method — inferring individual cell EIS from pack-level EIS combined with pulse data — is commercially significant for both in-service monitoring and second-life battery programmes.
SK On integrates EIS Nyquist analysis directly into battery management system alarm architectures, using EIS real intercept values and Nyquist plot inflection points to detect battery abnormalities and SOC deviations, coupling SOH estimation with fault detection in a unified BMS architecture.
Three clear trends emerge from the corpus. First, the convergence of EIS with machine learning across all major assignees, replacing or augmenting explicit ECM fitting with data-driven inference. Second, the push toward on-device, real-time EIS that does not require battery rest or specialised external hardware — a prerequisite for deployment in EVs, drones, spacecraft, and grid storage. Third, multi-modal fusion combining EIS data with voltage, current, temperature, OCV, ultrasound, and historical data for more robust SOH estimation. For deeper competitive intelligence, explore the PatSnap customer success stories from battery R&D teams.
Impedance Spectroscopy Battery SOH — key questions answered
Electrochemical impedance spectroscopy operates by applying a small-amplitude sinusoidal excitation signal across a battery at a range of frequencies and recording the complex voltage-current response. The resulting impedance spectrum encodes physically interpretable electrochemical information — ohmic resistance, charge-transfer resistance, double-layer capacitance, and Warburg diffusion impedance — each appearing at characteristic frequency ranges. These parameters directly reflect battery aging mechanisms.
Yes. Patents from Isuzu Motors (2021), Texas Instruments (2022), and Huawei Digital Energy (2024) demonstrate that EIS can be performed using existing BMS hardware and operational current/voltage waveforms, removing the barrier of costly external instrumentation. Huawei's approach applies multiple excitation signal groups during normal battery operation, selecting the most accurate resulting impedance spectrum without interrupting device operation.
Measured impedance values are strongly influenced by temperature and SOC in addition to aging state. Alps Green Devices demonstrated a frequency-selection strategy where the internal impedance of a lithium-ion battery is measured at a frequency where the impedance is temperature-invariant — identified as 4 kHz and/or 500 kHz — allowing SOH or SOC to be estimated without concurrent temperature measurement. Southwest Jiaotong University (2025) uses the imaginary part of impedance at a first target frequency to estimate battery temperature, then uses the estimated temperature to select a second target frequency most informative for SOH estimation.
A rapidly growing segment of the patent corpus integrates machine learning models directly with EIS data, treating impedance spectra as feature inputs for neural networks or ensemble models rather than fitting them to explicit physical circuit models. Noum Engineering (2021) describes a system where complex impedance measurements are fed directly into a convolutional neural network (CNN) that outputs battery state without intermediate physical model fitting. Nantong Lechuang New Energy (2023) chains PCA dimensionality reduction, CNN spatial feature extraction, BiLSTM temporal sequence modeling, and attention mechanism for feature weighting into a composite SOH prediction architecture.
The distribution of relaxation times (DRT) provides a more granular deconvolution of overlapping electrochemical processes compared to standard ECM fitting. Shandong University's dual patents (2024 and 2025) describe an approach that extends beyond standard ECM fitting by predicting the DRT from impedance data collected at multiple specific frequency bands. Shandong University claims the method enables low-cost, real-time, high-accuracy SOH monitoring with significant implications for electric vehicle battery management systems.
The dominant assignees by frequency of appearance include Samsung SDI, LG Energy Solution, Hyundai Motor Company, Huawei Digital Energy, Texas Instruments, Shandong University, SK On, and Advanced Measurement Technology Inc., among numerous Chinese and Korean university spin-outs. Samsung SDI holds the largest cluster of patents in the dataset, primarily centered on adaptive filter-based G/H parameter extraction. LG Energy Solution files across multiple EIS and impedance modalities including module-level EIS decomposition to cell level and resistance profile eigenvalue analysis.
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References
- Battery Performance Prediction Method and System Based on Impedance Spectroscopy Analysis — Central South University, 2025
- A Lithium-Ion Battery SOH Estimation Method Based on Electrochemical Impedance Spectroscopy — China Inspection and Quarantine Science Research Institute, 2020
- Battery Monitoring and Testing Systems and Methods — Advanced Measurement Technology Inc., 2022
- Battery Monitoring and Testing System and Methods Thereof — Advanced Measurement Technology Inc., 2025
- Battery Health State Estimation Method and System — Shandong University, 2025 (JP)
- Battery Health State Estimation Method and System — Shandong University, 2024 (JP)
- Battery Impedance Spectrum Acquisition Method and Energy Storage System — Huawei Digital Energy, 2024
- Battery Impedance Spectrum Measurement — Texas Instruments, 2022 (CN)
- Estimation Device, Estimation Method, and Vehicle — Isuzu Motors, 2021 (JP)
- Method and Device for Diagnosing the Condition of a Portable Battery — Mintec Co., Ltd., 2025 (KR)
- Battery State Detection Device and Method — Toyota Motor Corporation, 2007 (JP)
- State Detection Method of Power Storage Device — Alps Green Devices, 2016 (JP)
- Lithium Battery SOH Estimation Method — Southwest Jiaotong University, 2025 (CN)
- Apparatus for Estimating Battery State Using EIS Singularity Point Tracking — Sookmyung Women's University, 2025–2026 (KR)
- Apparatus for Diagnosing the State of a Battery — Hyundai Motor Company, 2025 (KR)
- Battery Health State Determination Method — Shenzhen Power Supply Bureau, 2025 (CN)
- Determining State-of-Health of an Energy Storage Device Using Complex Impedance Spectrum — University of Alabama, 2024 (US)
- Estimating Battery State from Impedance Measurements Using Convolutional Neural Networks — Noum Engineering, 2021 (CN)
- Lithium-Ion Battery Life Detection Method Based on EIS Testing — Beijing University of Aeronautics and Astronautics, 2022 (CN)
- Battery State Estimation Method Based on Neural Network and Impedance Identification Correction — Shanghai Jiao Tong University, 2022 (CN)
- Method for Predicting SOH Using Battery EIS Based on Multi-Model Combination — Nantong Lechuang New Energy, 2023 (CN)
- Battery SOH Estimation Method Based on EIS Multiple Correlation Analysis — Jiequan Institute, 2024 (CN)
- Device for Estimating SOH and Operating Method — LG Energy Solution, 2025 (KR)
- Device for Estimating Impedance Spectrum of Battery — LG Energy Solution, 2024 (CN)
- State Estimation Method, Device, and Program — Toyota Motor Corporation, 2024 (JP)
- Battery SOH Correction Method Based on Impedance Spectrum — Hainan Nuclear Power, 2024 (CN)
- Battery Management System and Method — SK On, 2025 (KR)
- Method of Evaluating Impedance Spectroscopy for Used Battery Modules — Soongsil University, 2021 (KR)
- The Electrochemical Society (ECS) — Reference body for electrochemical impedance spectroscopy standards and research
- International Energy Agency (IEA) — Global battery technology and electric vehicle market data
- National Renewable Energy Laboratory (NREL) — Battery performance and state-of-health research publications
All patent data and analysis on this page are sourced from the references above and from PatSnap's proprietary innovation intelligence platform, PatSnap Eureka.
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