Aug 2026· Engineering Research Express· Vol 8, pp. 155347· 0 citations· 32 references
Physics
Abstract
Internal short-circuit (ISC) faults in lithium-ion batteries shorten service life and may cause severe safety issues such as thermal runaway. Therefore, this study proposes a purely data-driven method based on terminal voltage during charging. The analysis focuses on the stable mid-to-late stage of low-rate constant-current charging. Multiple discrete time instants within 4200 s–6000 s are selected, and the voltage difference between them is used as the diagnostic feature. A random forest (RF) model is developed, trained, and validated. To improve performance, Bayesian optimization tunes RF hyperparameters, and particle swarm optimization is applied for feature selection. Diagnostic thresholds are established based on model outputs to enable ISC fault identification and prediction. Support vector regression and Gaussian process regression are used for comparison. Results show that the optimized RF model achieves a mean relative error of 4.254%, a root mean square error of 32.688 Ω, and a coefficient of determination (R2) of 98.42%, significantly outperforming the comparison methods.
Internal short circuits (ISCs) in lithium‐ion batteries (LIBs) pose a significant safety threat, necessitating rapid and reliable diagnostic methods. This paper proposes an improved diagnostic model that leverages the electrochemical characteristics of the constant current constant voltage (CCCV) charging phase. By using the pseudo‐steady‐state properties of the constant current stage, the proposed model reduces the system state vector from four dimensions to two dimensions and directly observes the short‐circuit current as a state variable. A Recursive Least Squares (RLS) algorithm is then employed to identify the short‐circuit resistance while suppressing sensor‐noise interference. Experimental results show that the proposed method substantially shortens the detection time and improves resistance‐identification accuracy compared with the traditional second‐order RC‐model‐based method. In addition, the proposed model reduces the theoretical computational cost from 553 to 129 FLOPs per iteration, supporting online implementation in battery management systems.
Xiulan Liu, Qian Zhang, Shengjia Li et al.· Energy Storage· 0 citations
In the age of rapid digital transformation, electric vehicles have become more and more common, and have gradually displaced the status of diesel. It is essential for people to pay more attention to the safety of lithium batteries. To illustrate, a lithium-ion battery is prone to error under long-term charging or discharging and high temperature. If the fault diagnosis is not at the right time, it might lead to the declining performance of the lithium-ion battery and shorten the servicing life. This study aims to discuss the technologies for detecting the error in lithium batteries, which are categorized into three forms: Model-based method, Signal processing-based method and Machine learning method. Meanwhile, these technologies have been widely applied in the area of electric vehicle and battery management systems, detecting the state of batteries in real-time. Overall, this study considers that there are no any fault diagnosis methods that are suitable for all the application scenarios, and researchers should choose the approach according to their needs. The future development trend is the combination of various fault diagnosis techniques, improving the accuracy, reliability and performance.
Shunpang Zeng· Applied and Computational En...· 0 citations
Fast charging of lithium-ion battery packs involves a compromise between charging speed, temperature rise, and aggressive current profiles that may accelerate battery degradation. This paper presents a current-stress-aware fuzzy logic control framework for safe fast charging of series-connected lithium-ion battery cells. The proposed controller uses a physically interpretable two-input, one-output fuzzy structure in which the highest cell-voltage difference, Vd, and the lowest single-cell voltage, VB, are used to determine the charging-current command, Icharge. Unlike conventional fuzzy charging approaches that rely on manually selected membership functions or weighted single-objective tuning, the proposed method simultaneously optimizes the Gaussian membership-function parameters and the input/output scaling gains using a Pareto-based multi-objective optimization framework. The resulting design vector contains 21 decision variables, including 18 membership-function parameters and three scaling gains. Three conflicting objectives are minimized: the time required to reach 95% state of charge, the maximum temperature rise above the reference temperature, and a normalized current-stress index based on the integral of the squared charging current. The framework is implemented in MATLAB/Simulink using a three-cell Panasonic NCR18650PF lithium-ion battery pack model. The obtained Pareto front reveals the expected trade-off between fast charging and battery protection. The fastest solution reaches 95% SOC in 5440 s but produces the highest temperature rise and current-stress index, whereas the selected knee-point controller reaches the target in 6880 s while reducing the maximum temperature rise and current-stress index compared with the fastest solution. Robustness tests under variations in initial SOC, cell imbalance, initial temperature, capacity scaling, and internal-resistance scaling show that the knee-point controller maintains stable charging behavior and satisfies the imposed thermal safety constraint. The results demonstrate that the proposed current-stress-aware Pareto-optimized fuzzy controller provides a systematic and interpretable approach for balancing charging speed, thermal safety, and battery stress in lithium-ion battery fast charging.
Reliable experimental platforms are essential for lithium-ion (Li-Ion) battery characterization, equivalent circuit model (ECM) identification, and state-of-charge (SOC) estimator validation. However, access to commercial battery cyclers and high-end instrumentation can be limited in academic and applied research environments, which motivates the development of low-cost and reproducible test benches. This work presents the development and validation of a low-cost experimental platform for Li-Ion battery characterization, SOC-dependent ECM identification, voltage model validation, and SOC estimator assessment. The proposed platform integrates constant-current–constant-voltage (CC-CV) charging, a controlled-current electronic load implemented on a printed circuit board (PCB), ESP32-based embedded acquisition, and MATLAB-based data processing. A Samsung INR18650-35E cell was characterized through full-discharge tests at different C-rates, pulse discharge tests (PDTs), and dynamic current profiles. The measured capacity at 0.2C was 3345.1 mAh, showing close agreement with the manufacturer-reported minimum nominal capacity of 3350 mAh. First- and second-order Thévenin ECMs were identified from PDT data, parameterized as SOC-dependent models, and validated under Scaled Dynamic Stress Test (DST) and Modified Pulsed Dynamic Stress Test (P-DST) profiles. The second-order ECM identified from the most complete PDT dataset achieved voltage RMSE values of 23.24 mV and 12.14 mV under the DST and P-DST profiles, respectively. The platform was further used to evaluate SOC estimators based on extended Kalman filters (EKF) and a hybrid Extended Kalman Filter-Artificial Neural Network (EKF-ANN) residual correction method. The EKF based on the second-order ECM achieved SOC RMSE values of 0.5088% and 1.0890% under the complete dynamic profiles, while the hybrid EKF-ANN reduced the RMSE to 0.2276% and 0.2788% over the dynamic test blocks. These results show that the proposed platform provides an accessible experimental framework for connecting battery testing, ECM identification, voltage validation, and BMS-oriented SOC estimator evaluation within a single reproducible workflow.
Jhoan Sebastián Valderrama-Vélez, K. Lemmel-Vélez, Juan Camilo Mazo-Arenas et al.· Clean Technology· 0 citations
(English) The decarbonisation of energy systems, the increasing penetration of renewable generation, and the electrification of transport have made lithium-ion batteries a key enabling technology in modern power applications. Their versatility and high energy density explain their central role in electric vehicles and stationary energy storage. However, their safe and efficient operation depends on battery-management systems capable of estimating internal quantities that cannot be measured directly, such as state of charge, open-circuit voltage, and relevant electrical parameters. This task is difficult because battery behaviour is nonlinear, operating conditions are variable, and the measurements normally available in practice are limited.
This thesis addresses these challenges through a practical estimation framework for real-time battery-management applications. It reviews the main modelling and estimation approaches for lithium-ion batteries and justifies the use of low-order equivalent circuit models as a suitable compromise between physical relevance and online implementability. It also reviews the estimation-theoretic basis required to work under realistic operating conditions, with particular attention to observability and excitation. A central point is that classical recursive estimators often rely on persistent excitation, whereas battery current profiles are usually dictated by operation and may only be informative over finite intervals. To bridge this gap, the thesis adopts novel regression-based tools such as the generalised parameter estimation based observer, dynamic regressor extension and mixing, and non-asymptotic least squares estimators.
On this basis, two application-oriented estimation architectures are developed. The first jointly estimates the dominant parameters of a first-order equivalent circuit model and the instantaneous open-circuit voltage from online current and terminal-voltage measurements, and is validated through simulations and experiments. The second extends the plug-and-play perspective to the online identification of parametrised open-circuit-voltage/state-of-charge relations from finite informative intervals. Overall, the thesis shows that useful battery monitoring can be achieved without extensive offline parametrisation campaigns or artificially persistent excitation, thereby supporting more practical battery-management solutions.
(Català) La descarbonització dels sistemes energètics, la creixent penetració de la generació renovable i l'electrificació del transport han convertit les bateries d'ió liti en una tecnologia clau en les aplicacions energètiques modernes. La seva versatilitat i elevada densitat energètica expliquen el seu paper central en els vehicles elèctrics i en l'emmagatzematge estacionari d'energia. Tanmateix, el seu funcionament segur i eficient depèn de sistemes de gestió de bateries capaços d'estimar magnituds internes que no es poden mesurar directament, com ara l'estat de càrrega, la tensió de circuit obert i paràmetres elèctrics rellevants. Aquesta tasca és difícil perquè el comportament de la bateria és no lineal, les condicions d'operació són variables i les mesures disponibles en la pràctica són limitades.
Aquesta tesi aborda aquests reptes mitjançant un marc pràctic d'estimació orientat a aplicacions de gestió de bateries en temps real. El treball revisa els principals enfocaments de modelatge i estimació per a bateries d'ió liti i justifica l'ús de models de circuits equivalents de baix ordre com un compromís adequat entre rellevància física i implementabilitat en línia. També revisa desenvolupa la base teòrica d'estimació necessària per treballar en condicions d'operació realistes, amb una atenció especial a l'observabilitat i a l'excitació. Un punt central és que els estimadors recursius clàssics sovint depenen de l'excitació persistent, mentre que els perfils de corrent de la bateria solen venir imposats per l'operació i poden ser informatius només durant intervals finits. Per superar aquesta limitació, la tesi adopta noves eines basades en regressió com el generalised parameter estimation based observer, el dynamic regressor extension and mixing i els estimadors de mínims quadrats no asimptòtics.
Sobre aquesta base, es desenvolupen dues arquitectures d'estimació orientades a l'aplicació. La primera estima conjuntament els paràmetres dominants d'un model de circuit equivalent de primer ordre i la tensió instantània en circuit obert a partir de mesures en línia de corrent i tensió terminal, i es valida mitjançant simulacions i experiments. La segona amplia la perspectiva plug-and-play a la identificació en línia de relacions parametritzades entre la tensió en circuit obert i l'estat de càrrega a partir d'intervals finits informatius. En conjunt, la tesi mostra que és possible dur a terme un monitoratge útil de bateries sense recórrer a campanyes extenses de parametrització fora de línia ni a excitació persistent artificial, i així donar suport a solucions de gestió de bateries més pràctiques.
(Español) La descarbonización de los sistemas energéticos, la creciente penetración de la generación renovable y la electrificación del transporte han convertido las baterías de ion-litio en una tecnología clave en las aplicaciones energéticas modernas. Su versatilidad y elevada densidad energética explican su papel central en los vehículos eléctricos y en el almacenamiento estacionario de energía. Sin embargo, su funcionamiento seguro y eficiente depende de sistemas de gestión de baterías capaces de estimar magnitudes internas que no pueden medirse directamente, como el estado de carga, la tensión en circuito abierto y parámetros eléctricos relevantes. Esta tarea es difícil porque el comportamiento de la batería es no lineal, las condiciones de operación son variables y las medidas normalmente disponibles en la práctica son limitadas.
Esta tesis aborda estos retos mediante un marco práctico de estimación orientado a aplicaciones de gestión de baterías en tiempo real. El trabajo revisa los principales enfoques de modelado y estimación para baterías de ion-litio y justifica el uso de modelos de circuitos equivalentes de bajo orden como un compromiso adecuado entre relevancia física e implementabilidad en línea. También revisa la base teórica de estimación necesaria para trabajar en condiciones de operación realistas, con especial atención a la observabilidad y a la excitación. Un punto central es que los estimadores recursivos clásicos suelen depender de la excitación persistente, mientras que los perfiles de corriente de la batería suelen venir impuestos por la operación y pueden ser informativos solo durante intervalos finitos. Para salvar esta limitación, la tesis adopta nuevas herramientas basadas en regresión como el generalised parameter estimation based observer, el dynamic regressor extension and mixing y los estimadores de mínimos cuadrados no asintóticos.
Sobre esta base, se desarrollan dos arquitecturas de estimación orientadas a la aplicación. La primera estima conjuntamente los parámetros dominantes de un modelo de circuito equivalente de primer orden y la tensión instantánea en circuito abierto a partir de medidas en línea de corriente y tensión terminal, y se valida mediante simulaciones y experimentos. La segunda amplía la perspectiva plug-and-play a la identificación en línea de relaciones parametrizadas entre la tensión en circuito abierto y el estado de carga a partir de intervalos finitos informativos. En conjunto, la tesis muestra que es posible llevar a cabo una monitorización útil de baterías sin recurrir a campañas extensas de parametrización fuera de línea ni a excitación persistente artificial, y así apoyar soluciones de gestión de baterías más prácticas.
Lithium-ion batteries are the backbone of electric vehicles, renewable energy storage, and new emerging smart grid applications. However, the safety and the economic value of such batteries depend heavily on the proper assessment of State of Health (SOH). Conventional invasive measurements provide detailed information; however, they are difficult to apply to sealed or in-service battery packs. This makes non-invasive techniques such as voltage, current, temperature, impedance, and data-driven modeling better suited for battery management at scale. In this paper, three representative research directions, including comprehensive SOH characterization, fast impedance-based SOH estimation, and machine-learning diagnosis of degradation patterns from electrochemical impedance spectroscopy, are reviewed and synthesized. Rather than analyzing each study in isolation, the paper proposes a comparative framework to identify methodological strengths, data requirements, limitations, and potential for practical deployment. A unified SOH evaluation framework is proposed, which combines Gaussian Process Regression-Automatic Relevance Determination (GPR-ARD)-based feature selection, fast impedance calculation, lightweight Extreme Learning Machine (ELM)-based online estimation, and cloud–edge model updating. The practical feasibility is discussed in terms of computational cost, latency, bandwidth, temperature variation, data quality, cybersecurity, and real-time battery management constraints. The study concludes that, to support safer electric vehicles, second-life battery use and sustainable energy storage systems, future non-invasive battery diagnostics should combine physical interpretability, multi-source data fusion, and deployment-oriented model design.
Junqi Zhang, R. Diao· Journal of Environmental &am...· 0 citations