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Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion Batteries: A Comprehensive Review

Yiming Xu, Xiaohua Ge, Ruohan Guo, Weixiang Shen

arXiv:2401.16682v1eess.SYcs.SY

TL;DR

LIB fault diagnosis matters for battery safety and reliability, yet reviews differ in their coverage and technical focus. This paper surveys model-based diagnosis methods, from battery models and faulty-battery state-space representations to fault mechanisms, uncertainty, observers, and diagnosis procedures. It identifies future research needs including pack balancing and fault-type identification.

  • Problem

    LIBs face safety risks from abusive operation, and effective fault diagnosis is important to battery health and reliability.

    Source: §1 · p. 2

  • Method

    The paper comprehensively surveys model-based diagnosis methods, including battery models, state-space representation, fault mechanisms, uncertainty, observers, and diagnosis procedures.

    Source: §1 · p. 2, §9 · p. 25

  • Results

    The review synthesizes EM and ECM models, faulty-battery state-space formulations, fault and uncertainty topics, observer designs, and diagnosis methods.

    Source: §1 · p. 2, §9 · p. 25

  • Takeaways & Limitations

    Parallel-pack diagnosis remains challenging because cell coupling complicates effective modeling and branch-current information is difficult to acquire.

    Source: §8.1 · p. 24

    Series-pack diagnosis research has barely examined balancing, motivating models that incorporate active or passive balancing strategies.

    Source: §8.1 · p. 24

Abstract

from arXiv · show

Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods in advanced BMSs. This paper provides a comprehensive review on the model-based fault diagnosis methods for LIBs. First, the widely explored battery models in the existing literature are classified into physics-based electrochemical models and electrical equivalent circuit models. Second, a general state-space representation that describes electrical dynamics of a faulty battery is presented. The formulation of the state vectors and the identification of the parameter matrices are then elaborated. Third, the fault mechanisms of both battery faults (incl. overcharege/overdischarge faults, connection faults, short circuit faults) and sensor faults (incl. voltage sensor faults and current sensor faults) are discussed. Furthermore, different types of modeling uncertainties, such as modeling errors and measurement noises, aging effects, measurement outliers, are elaborated. An emphasis is then placed on the observer design (incl. online state observers and offline state observers). The algorithm implementation of typical state observers for battery fault diagnosis is also put forward. Finally, discussion and outlook are offered to envision some possible future research directions.

1. Introduction

LIB fault diagnosis is presented as important to safety and service life, while this review focuses on model-based methods and their diagnosis procedures. It organizes the review around battery models, state-space representation, fault mechanisms, uncertainties, observers, and diagnosis.

1. Introduction

  • Abusive operation and harsh environments threaten LIB safety, making early fault detection important to battery health and reliability.BMS functions include condition monitoring, thermal management, cell balancing, state estimation, and fault diagnosis.Source: §1 · p. 2
  • This review focuses on model-based LIB fault detection, identification, and estimation, elaborating faulty-battery state-space models, observer design, and the diagnosis procedure.It frames model-based and data-driven approaches as distinct categories of existing fault-diagnosis reviews.Source: §1 · p. 2
  • The review proceeds from battery models and ECM state-space representation to fault mechanisms, uncertainties, observers, diagnosis procedures, future directions, and conclusions.Table 2 compares reviews by literature coverage period and technical focus.Source: §1 · pp. 2–3, Table 2 · p. 3

2. Battery Modeling

Battery models for fault diagnosis must balance fidelity and complexity. The review contrasts electrochemical and equivalent-circuit models, including their computational, physical-interpretability, and adaptability trade-offs.

2. Battery Modeling

  • Battery fault-diagnosis models must balance fidelity against complexity given LIBs’ multilayer structure and fault-related electrothermal coupling.The reviewed model families are electrochemical models (EMs) and electrical equivalent circuit models (ECMs).Source: §2 · p. 3

2.1. Physics-based Electrochemical Models

  • EMs capture electrochemical behavior with coupled PDEs, but their computation and parameter-identification burden motivates reduced-order and simplified models.The P2D model describes mass and charge conservation and electrochemical kinetics; the single-particle model simplifies electrode representation.Source: §2.1 · p. 3

2.2. Electrical Equivalent Circuit Models

  • ECMs use electrical components and are widely studied for online diagnosis because their structures are simplified and parameters easier to identify.They include integral-order and fractional-order models, differentiated by their fitting of electrochemical impedance spectroscopy.Source: §2.2 · p. 4

2.2.1. Integral-Order Models

  • RC pairs extend the Rint model to represent battery dynamics; however, RC-pair IOMs lack physical significance for probing LIB internal mechanisms.The Rint model has an SOC-dependent OCV source in series with constant resistance; an improved Thevenin model accounts for temperature effects on OCV.Source: §2.2.1 · p. 4

2.2.2. Fractional-Order Models

  • FOMs can improve impedance representation over finite-RC IOMs, but complex structures and adaptation demands constrain their practical online use.The review describes computational burden from online parameter searches and characterization and memory demands from parameter maps across operating conditions.Source: §2.2.2 · pp. 4–5

3. State-Space Representation

The review uses an equivalent-circuit state-space model to represent faulty battery dynamics, then discusses state-vector choices and parameter identification. It contrasts offline identification with recursive and non-recursive online methods.

3. State-Space Representation

  • The review favors ECMs for model-based fault diagnosis because their simpler structure supports easier parameter identification, while EMs’ nonlinearity and numerous parameters hinder real-time computation.The general state-space representation applies to both integral-order and fractional-order models.Source: §3 · p. 5

3.1. State Vectors

  • Including fault severity in the state vector enables direct severity estimation and improves fault response time and estimation accuracy over state vectors that omit the fault.Common vectors include [U1, SOC]^T and [U1, SOC, Ut]^T; fault-augmented vectors include [U1, SOC, f]^T.Source: §3.1 · p. 6

3.2. Parameter Matrices

  • The parameter matrices implicitly contain R0, R1, C1, and nominal capacity Cn, so their identification is essential to accurate state estimation.The review categorizes parameter identification as offline or online.Source: §3.2 · p. 6

3.2.1. Offline parameter identification

  • Offline identification uses HPPC discharge and regenerative pulses at each 10% capacity increment, with voltage response used to estimate battery parameters.R0 is calculated from the immediate voltage jump; R1 and C1 are estimated from the subsequent continuous voltage change using least squares.Source: §3.2.1 · p. 7, Figure 2 · p. 7

3.2.2. Online parameter identification

  • Online identification can use recursive methods such as RLS or KF, while non-recursive methods such as GA and PSO search across the dataset for parameter fits.Recursive methods have low computational cost but moderate precision; non-recursive methods can seek global optima and higher accuracy, at the cost of greater computation and runtime.Source: §3.2.2 · pp. 7–8

4. Fault Mechanisms

The review divides electrical fault modes into battery faults and sensor faults. It describes how battery faults arise and affect cells, and how faulty voltage or current measurements can disrupt BMS operation.

4.1.1. Overcharge/overdischarge fault

  • Overcharge and overdischarge continue charging or discharging beyond upper or lower cutoff voltage, respectively, and repeated events accelerate battery degradation.Overcharging can cause lithium deposition, structural change, gas release, and heat generation; overdischarging can damage electrode materials and reduce capacity.Source: §4.1.1 · p. 9

4.1.2. Battery connection fault

  • A battery connection fault is abnormal intercell contact resistance, which can cause uneven cell current, pack imbalance, and increased heat generation.Vibration during EV operation may loosen or degrade electrical connections between cells.Source: §4.1.2 · p. 9

4.1.3. SC faults

  • Short-circuit faults include internal and external shorts, and both can be electrically represented by a resistance shorting the battery terminals.Internal shorts can cause continuous discharge and heat accumulation; external shorts can damage the battery and may cause leakage, fire, or explosion.Source: §4.1.3 · pp. 9–10

4.2. Sensor Faults

  • Voltage and current sensor faults can give the BMS inaccurate battery status, leading to incorrect control signals and potentially abusive operation.Voltage-sensor faults can undermine cutoff protection; current-sensor faults can prompt incorrect working-state judgments and accelerate battery aging.Source: §4.2 · p. 10
  • The sensor fault models represent voltage and current faults using an arbitrary bias signal and parameters a, b, c, and d.The bias signal can be a step, periodic, or intermittent function.Source: §4.2 · p. 10

5. Modeling Uncertainties

Modeling uncertainties make accurate real-time battery-state estimation difficult and also affect residual-based fault diagnosis. The section distinguishes model and measurement errors, aging-related capacity errors, and measurement outliers, and discusses ways to address them.

5.1. Modeling Errors and Measurement Noises

  • Uncertain model parameters create modeling errors, while sensor accuracy limits introduce noise into terminal-voltage and input-current measurements.Stochastic methods assume statistical properties and seek to minimize estimation-error variance; deterministic methods assume hard bounds and seek to suppress uncertainty effects.Source: §5.1 · p. 11
  • Because residuals fluctuate around zero even in fault-free systems, diagnosis needs thresholds, which may be set using 3σ rules or fault-free Monte Carlo ranges.The review notes that residual distributions can be obtained from Monte Carlo simulations for different battery types or drive cycles.Source: §5.1 · p. 11

5.2. Aging Effects

  • SOC estimation accuracy depends strongly on identified battery capacity, so concurrent capacity and state estimation may improve state estimation and fault diagnosis under aging.One reviewed approach uses a second-order EKF for real-time SOC estimation and an offline fourth-order EKF for SOH updates triggered by voltage differences.Source: §5.2 · p. 11

5.3. Measurement Outliers

  • Measurement outliers are uncommon in laboratory settings but can arise in cloud transmission and abrupt real-EV maneuvers, and can completely disrupt voltage-based state estimation and fault diagnosis.A reviewed method detects and classifies outliers using a chi-square test, while an outlier-resistant KF combines EKF with Holt’s smoothing method.Source: §5.3 · pp. 11–12

6. State Observers and Algorithm Implementation

Battery state estimation and prediction require a suitable observer, grouped according to whether gains are computed online or offline. The section reviews representative observers and outlines their implementation.

6. State Observers and Algorithm Implementation

  • State observers support battery-state estimation and prediction, and are grouped as online or offline according to how their gains are computed.The review also provides implementation algorithms for typical observers.Source: §6 · p. 12

6.1. Online State Observers

  • Kalman filters recursively predict state and output, then update the state from output error; EKF linearization supports nonlinear battery models, while SPKF is another nonlinear-state option.The cited EKF implementation has initialization, time-update, and measurement-update steps.Source: §6.1 · p. 12
  • Unlike KF point estimates, an SVO returns an ellipsoidal set that encloses possible states under unknown but bounded modeling uncertainties.The cited discussion notes that KF-type point estimates do not guarantee a reliable confidence region containing the unknown signal at every instant.Source: §6.1 · p. 12

6.2. Offline State Observers

  • Offline observers include LO, SMO, PIO, and HIO designs, each with characteristics suited to specific estimation problems.SMOs reconstruct states by converging to a sliding surface, HIOs suppress effects of initial-state error and modeling uncertainty, and PIOs incorporate an integrator for greater robustness to uncertainties.Source: §6.2 · pp. 12–13

6. State Observers and Algorithm Implementation

  • The EKF implementation discretizes the battery model, initializes estimates, then repeats time and measurement updates using current, battery parameters, and terminal voltage.The measurement update uses the output innovation to correct the state estimate.Source: §6.2 · p. 13

7. Fault Diagnosis

A typical fault-diagnosis procedure comprises detection, identification, and estimation.

7. Fault Diagnosis

  • Fault diagnosis generally proceeds through fault detection, fault identification, and fault estimation.Figure 5 depicts the procedure’s structure.Source: §7 · p. 13

7.1. Fault Detection

Battery fault detection is organized around residual evaluation for individual cells and consistency checks within packs. These approaches use estimated states, measurements, or parameters as indicators, while threshold choice and uncertainty affect their practical trade-offs.

7.1. Fault Detection

  • Battery fault detection is grouped into cell-level residual evaluation and pack-level consistency checking.Residual evaluation is commonly used for cells because estimating every cell state in a pack raises computational complexity and can hinder timely detection.Source: §7.1 · p. 13, §7.1.1 · p. 20

7.1.1. Residual evaluation

  • Residual evaluation alarms when estimation-measurement or reference residuals cross a predefined threshold, with examples including current-sensor and short-circuit detection.Other reviewed approaches compare SOC estimates or prediction and estimation ellipsoids, while separator conductivity can indicate short-circuit severity.Source: §7.1.1 · pp. 19–20

7.1.2. Consistency check

  • Pack consistency checks flag deviations in cell states, parameters, or measurements, but may trade diagnostic sensitivity for lower implementation complexity under uncertainty and parameter inconsistency.Examples use SOC correlation, mean-difference parameters, and inter-cell voltage differences as fault indicators.Source: §7.1.2 · p. 20
  • Threshold selection affects false-alarm rate, missed-alarm rate, and detection time, while fixed thresholds may not suit complex real-world applications.The review identifies adaptive thresholds resilient to erroneous initialization and modeling uncertainties as an area receiving limited attention.Source: §7.1.2 · p. 21

7.2. Fault Identification

Fault identification must distinguish fault types that are unknown before diagnosis, under the assumption that only one fault occurs at a time. The review describes hardware and analysis redundancy as the main identification approaches.

7.2. Fault Identification

  • Identification methods must determine the fault type, which is unknown before diagnosis, assuming only one fault occurs at a time.The review says fault identification is needed to distinguish the source and type of a fault.Source: §7.2 · p. 21

7.2.1. Hardware redundancy

  • Hardware redundancy uses redundant sensors or adjusted sensor topologies, while added sensors increase system complexity and cost.Examples use interleaved voltage measurements and sensor arrangements to distinguish or locate short circuits and sensor faults.Source: §7.2.1 · p. 21

7.2.2. Analysis redundancy

  • Analysis redundancy isolates faults by comparing subsystem residuals with fault signatures and checking their correspondence through an isolation tree or table.Examples include EKF residuals evaluated with CUSUM, nonlinear signature models, sliding-mode observers, and SOC-based decision trees.Source: §7.2.2 · pp. 21–22
  • The review compares hardware and analysis redundancy in terms of practicability and functionality, but the supplied text gives no comparison outcome.Table 10 is identified as the comparison.Source: §7.2.2 · p. 22, Table 10 · p. 22

7.3. Fault Estimation

Fault severity can be assessed through deviations in SOC or capacity, or by incorporating a fault index into state estimation. The review describes these approaches alongside their fault-specific sensitivities and practical limits.

7.3. Fault Estimation

  • SOC or capacity deviations are proposed as quantitative indicators of fault influence and severity when voltage deviations are imperceptible.The review introduces these variables as alternatives for evaluating fault effects.Source: §7.3 · p. 22

7.3.1. SOC difference

  • SOC-difference methods use Coulomb-counting references and model-based estimates to assess short-circuit resistance or current-sensor faults, but are less effective for voltage-fault estimation.For short circuits, SOC depletion supports leakage-current and resistance estimation; voltage–SOC nonlinearity limits SOC-difference fault estimation, and accumulated differences can converge slowly with reduced accuracy for minor faults.Source: §7.3.1 · pp. 22–23

7.3.2. Capacity analysis

  • Capacity analysis estimates short-circuit leakage current from changes in remaining charge capacity or incremental capacity, but is limited to current-related faults and affected by C-rate.The approach may require low-rate charging or discharging over a large range, sacrificing diagnosis time and algorithm practicability.Source: §7.3.2 · p. 23, Figure 8 · p. 23

7.3.3. State estimation

  • State-estimation methods augment the state with a fault index or estimate fault variables through observers, and can address short-circuit and sensor faults.The review notes that fault type should be identified before estimation for dynamic modeling, while research on that prerequisite remains scarce.Source: §7.3.3 · p. 24

8. Challenges and Outlook

The outlook identifies modeling, detection, identification, and cloud-deployment challenges for model-based battery fault diagnosis. It highlights pack balancing, adaptive thresholds, fault-type identification, and cloud-data issues as areas for further work.

8.1. Balancing Battery Pack Modeling

  • Series-pack diagnosis needs models that incorporate active or passive balancing and voltage or SOC balancing strategies, whose importance has received little study.Manufacturing inconsistencies make balancing circuits necessary for pack longevity in real-world applications.Source: §8.1 · p. 24
  • Parallel-pack modeling remains unresolved because cells are coupled, and branch-current information is difficult or nearly impossible to acquire.The review suggests exploiting shared terminal voltage and investigating unknown-input observers for parallel packs.Source: §8.1 · p. 24

8.2. Threshold-Adaptive Fault Detection

  • Fixed thresholds trade missed alarms and short-circuit resistance accuracy against false alarms and computation, while trial-and-error selection limits generalization and repeatability.The review calls for adaptive thresholds that remain disturbance-immune while sensitive to faults.Source: §8.2 · p. 24

8.3. Multi-source Fault Identification

  • Fault-type identification remains a research need because failures can have minimally varying external characteristics and are often unknown in real applications.The review says identifying fault type matters for simplifying modeling and improving fault-estimation accuracy.Source: §8.3 · p. 25

8.4. Cloud-based BMS

  • Cloud-based BMS research must address reduced sampling rates, cyber-safety risks, and ways to use historical data to improve diagnosis.Cloud datasets commonly use about 0.1 Hz sampling versus about 1 Hz in laboratory datasets; malicious attacks or data loss may lead to incorrect state evaluations and instructions.Source: §8.4 · p. 25

9. Conclusion

The conclusion surveys model-based battery fault diagnosis, covering battery models, state-space representation, faults, uncertainties, and observers. It organizes diagnosis methods around detection, identification, and estimation, and summarizes future challenges, including cloud-based BMS.

9. Conclusion

  • The review summarizes physics-based electrochemical and electrical equivalent circuit models and presents a linearly parameterized state-space equation for nonlinear battery dynamics.It discusses state-vector configurations and compares offline and online parameter-matrix identification.Source: §9 · p. 25
  • The survey covers fault mechanisms, uncertainty impacts on state estimation, and observer characteristics and implementations.Source: §9 · p. 25
  • The review analyzes diagnosis methods through detection, identification, and estimation, then summarizes challenges and outlook for cloud-based BMS.Source: §9 · p. 25
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