Search Results (102)

In electric vehicles, power batteries consist of numerous individual cells connected in series or parallel. Variations in manufacturing, operating conditions, and aging can lead to differences among these cells. Such inconsistencies can compromise the battery pack’s performance, safety, and overall service life. Therefore, accurately diagnosing inconsistencies among battery cells is of great significance for enhancing the reliability of the battery system and ensuring the operational safety of the vehicle. To address the limited computational resources available in vehicles, this paper proposes an end-cloud collaborative fault diagnosis framework and validates its effectiveness using real-world vehicle driving data. On the cloud side, a deep learning-based reconstruction network is developed to enable high-precision reconstruction of cell voltages. On the vehicle side, a second-order equivalent circuit model is used to represent battery dynamics. An adaptive forgetting factor recursive least squares method is introduced for online estimation of the model parameters, enabling accurate local prediction of individual cell voltages. Using the cloud-reconstructed and vehicle-predicted cell voltages, the extreme difference value of voltage for each cell is computed. A comprehensive diagnosis of inconsistency faults is then performed by fusing the extreme difference in voltage results from both the cloud and vehicle sides via the Extended Kalman Filter (EKF); threshold judgment is conducted based on the fused results, and the Cumulative Sum (CUSUM) algorithm is designed to identify cell inconsistency faults. Experimental results show that the proposed method effectively detects battery inconsistency faults and demonstrates strong engineering applicability and practical potential. Full article

Admittance-based analysis of wind farm-integrated power systems is inaccurate across varying operating points (OPs) resulting from wind speed fluctuations and shifting grid conditions. Existing methods can be classified as model-driven, which require detailed system modeling and struggle with parameter extraction, and as data-driven, which often lack physical interpretability, suffer from high dimensionality, and provide insufficient coverage of training frequency points. This study introduces an AM reconstruction framework that integrates equivalent circuits with a support vector machine (SVM). The approach first applies vector fitting and an equivalent-circuit transformation to decompose the admittance response into first- and second-order subcircuits, thereby representing the frequency-domain characteristics with low-dimensional, more physically interpretable parameters. Subsequently, an SVM establishes a nonlinear mapping between OPs and equivalent-circuit parameters, enabling the reconstruction of continuous admittance transfer functions for new OPs. This framework transforms the modeling of high-dimensional frequency-domain data into a low-dimensional physical parameter prediction problem, thereby avoiding error accumulation from interpolation over discrete frequency points. The proposed method is validated using a direct-drive permanent magnet synchronous generator (PMSG) wind turbine model connected to the IEEE 14-bus test system. Frequency-domain simulations and error analyses under previously unseen OPs confirm the method’s high prediction accuracy and strong generalization capability. Full article

Accurate state of charge (SOC) estimation is essential for reliable battery management systems operating over a wide temperature range. This study proposes a joint estimation framework that combines a temperature-matched dual open-circuit-voltage (OCV)-SOC model, online forgetting-factor recursive least squares (FFRLS), and an adaptive improved multi-innovation unscented Kalman filter (AIMIUKF). The dual OCV-SOC model separately calibrates charging and discharging branches at 0 °C, 25 °C, and 45 °C, reducing the voltage bias caused by thermal dependence and charge–discharge hysteresis. On this corrected voltage baseline, FFRLS identifies the time-varying parameters of the second-order RC equivalent circuit model. The updated parameters are then used by AIMIUKF, where a finite multi-innovation window improves convergence under initial SOC deviation, and covariance matching adjusts process and measurement noise online. Validation on the CALCE 18650 dataset under the Dynamic Stress Test (DST) profile shows sub-1% SOC errors at all tested temperatures. Full article

Equivalent circuit models (ECMs), owing to their simple structure, high computational efficiency, and ease of embedded implementation, have become the most practically applicable modeling approach in lithium-ion battery management systems (BMSs). This paper provides a systematic review of the research progress in lithium-ion battery ECMs along the main line of model construction, parameter identification, and state estimation. First, the topological characteristics, mathematical representations, and application scenarios of the Rint, Thevenin, partnership for a new generation of vehicles (PNGV), dual-polarization, high-order RC, Randles, and fractional-order models are summarized and compared, thereby revealing the inherent trade-off among model accuracy, complexity, and real-time performance. Second, open-circuit voltage–state of charge (OCV–SOC) calibration, offline/online parameter identification, and ECM-based state of charge (SOC) estimation methods are reviewed, with particular emphasis on the advantages and limitations of least squares, recursive least squares, Kalman filtering, particle filtering, sliding-mode observers, and model–data fusion methods. Furthermore, based on model validation and comparative performance results, it is shown that simple models possess high real-time capability but limited dynamic characterization ability; the first-order RC model achieves a more favorable balance between accuracy and complexity; and although high-order models can improve dynamic fitting and state estimation accuracy, they also increase parameter dimensionality and implementation cost. Finally, the key issues faced in this field are distilled, including insufficient adaptability under full operating conditions and across the full lifecycle, inadequate multi-physics coupled modeling, limited integration depth between physical constraints and data-driven methods, and the lack of a unified standardized validation framework. Future research is expected to further advance toward adaptive variable-structure modeling, multi-physics coupling, intelligent hybrid modeling, and unified benchmark testing. This review can provide a systematic reference for ECM design, parameterization method selection, and the development of BMS state estimation strategies for lithium-ion batteries. Full article

To improve the state-of-charge (SOC) estimation accuracy of sodium-ion batteries under complex operating conditions, this paper proposes a particle swarm optimization-based heterogeneous adaptive extended Kalman filter. A hardware-in-the-loop (HIL) validation platform is also established to reproduce the sampling-chain constraints of a practical battery management system. In addition, a second-order equivalent circuit model (ECM) serves to characterize battery dynamics and generate validation data. Within this framework, the degradation in estimation performance from the theoretical environment to practical hardware execution is quantitatively analyzed. The feasibility of using ECM-generated data for SOC estimation algorithm validation is also evaluated. Using measured Federal Urban Driving Schedule data at 25 °C, the proposed method achieves high estimation accuracy and stable convergence in both environments. Specifically, the mean absolute error and root-mean-square error in the theoretical environment are 0.11% and 0.25%, respectively. Under HIL conditions, the corresponding values are 0.60% and 0.63%. Additional tests under different temperatures and composite disturbance conditions further verify the adaptability and robustness of the proposed algorithm. The results also show that practical hardware constraints introduce non-negligible performance degradation. In addition, ECM-generated data remain highly consistent with measured data in terms of error-evolution trends. Therefore, ECM-generated data can serve as a feasible validation data source for SOC estimation algorithm performance evaluation and rapid validation. Full article

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework. Full article

This study introduces two compact wideband substrate-integrated waveguide (SIW) filters fabricated using thin-film technology. The wideband bandpass response is achieved by incorporating interdigital capacitor (IDC) structures into a half-mode SIW (HMSIW) transmission line. An equivalent LC circuit model is formulated to analyze the influence of IDC parameters on the generation of transmission zeros. For the first filter (BPF 1), a third-order IDC coupling configuration is employed, resulting in a 1 dB passband spanning 11 GHz to 18 GHz, a minimum insertion loss of 0.66 dB, three transmission zeros that enhance stopband performance, and a compact core dimension of $0.49{\lambda }_g\times 0.29{\lambda }_g$. For further miniaturization, a modified HMSIW transmission line incorporating a metal-insulator-metal (MIM) capacitor at the equivalent magnetic wall is proposed. This design effectively reduces the transverse dimension of the waveguide while maintaining the original cutoff frequency. Utilizing this configuration, the second bandpass filter (BPF 2) was designed and fabricated employing double-layer ceramic thin-film technology. The resulting filter exhibits a 1 dB passband spanning 10 GHz to 18 GHz, a compact footprint measuring $0.44{\lambda }_g\times 0.23{\lambda }_g$, a minimum insertion loss of 0.58 dB, and features three transmission zeros. The fabricated and measured results of both filters show good agreement with simulations. Compared with previously reported wideband SIW filters, the proposed designs demonstrate comprehensive advantages in fractional bandwidth, insertion loss, out-of-band suppression, and circuit size, providing effective filtering solutions for high-density integration of microwave and millimeter-wave RF systems. Full article

State-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries are critical parameters in battery management systems, directly impacting the driving range, performance stability, and safety of electric vehicles. To improve the accuracy and stability of SOC and SOH estimation simultaneously, this paper proposes a joint estimation method with constant-current bias compensation. First, based on a second-order RC equivalent circuit model, a constant-current bias compensation term is introduced into the Kalman filter framework. The estimation accuracy and robustness of SOC are validated under multiple operating conditions and noise levels. Then, a model integrating Transformer and gated recurrent unit is constructed. The fata morgana algorithm (FATA) is adopted for hyperparameter optimization. Ablation studies and multi-model comparative experiments are conducted to verify the model’s accuracy. Finally, capacity correction is performed using SOH results. By combining current bias compensation and precise temporal features extracted from aging data, joint estimation of SOC and SOH is achieved. Results show that after introducing current bias compensation and aging-based capacity correction, the accumulated SOC estimation error is reduced by more than 10%, while SOH estimation achieves a MAPE below 0.90% and an RMSPE below 1.10%. The proposed joint method is thus verified to be accurate and practical. Full article

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries is one of the core functions of a battery management system and is of great significance for ensuring the safe operation of electric vehicles and optimizing energy utilization. However, due to the strong nonlinearity, time-varying characteristics, and interference from complex operating conditions within the battery, high-precision SOC estimation faces severe challenges. To address the problems that a single data-driven method lacks physical constraints and a single model-driven method struggles to characterize complex nonlinearities, this paper proposes a series-connected LSTM-UKF fusion estimation method. This method first utilizes a Long Short-Term Memory network to learn the dynamic characteristics of the battery from historical voltage and current data, capturing the long-term dependencies of SOC changes to achieve an initial prediction. Subsequently, using this predicted value as the observation input, an Unscented Kalman Filter based on a second-order RC equivalent circuit model is introduced for optimal state correction, effectively suppressing model uncertainty and measurement noise. Simulation validation under various dynamic conditions, such as constant current discharge and FUDS, shows that compared to single LSTM or UKF algorithms, the proposed fusion method has significant advantages in estimation accuracy, convergence speed, and robustness. Its root mean square error is reduced to 0.0031, and it maintains stable estimation performance under different operating conditions. This study provides an effective data-model fusion solution for high-precision SOC estimation of lithium-ion batteries under complex operating conditions. Full article

Accurate estimation of the state of charge (SOC) is essential for the safe and efficient operation of lithium-ion batteries. Conventional Adaptive Unscented Kalman Filter (AUKF) methods often exhibit limited accuracy, primarily due to the empirical selection of process and measurement noise covariance matrices. To overcome this limitation, this study proposes a QPSO-AUKF algorithm based on a second-order RC equivalent circuit model, which integrates Quantum-behaved Particle Swarm Optimization (QPSO) with online parameter identification. In this approach, the QPSO algorithm optimizes the noise covariance matrices, which are subsequently used within the AUKF framework for SOC estimation. MATLAB R2020a simulations conducted on the Maryland and Wisconsin datasets demonstrate that the QPSO-AUKF reduces the root mean square error (RMSE) by more than 60% compared with the conventional AUKF, indicating a significant improvement in SOC estimation accuracy. Full article

Accurate state-of-charge (SoC) estimation is essential for ensuring the safety, efficiency, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional methods such as ampere-hour (AH) integration and the extended Kalman filter (EKF) often suffer from error accumulation, sensitivity to initial conditions, and inadequate handling of strong nonlinearities and time-varying noise. To overcome these limitations, this paper proposes a novel LDL-Decomposition-Based Multi-Innovation Adaptive Unscented Kalman Filter (LDL-MIAUKF) algorithm that integrates three key innovations: (1) multi-innovation theory to exploit historical measurement sequences for enhanced state correction; (2) an adaptive mechanism to dynamically adjust process and observation noise covariances in real time; and (3) LDL decomposition (instead of Cholesky) to guarantee numerical stability and positive definiteness of the covariance matrix during sigma point generation. A second-order RC equivalent circuit model is established for the lithium battery, and its parameters are identified online using the forgetting factor recursive least squares (FFRLS) method under Hybrid Pulse Power Characterization (HPPC) test conditions. The proposed LDL-MIAUKF algorithm is then applied to estimate SoC using real battery data. Experimental results demonstrate that the LDL-MIAUKF achieves a maximum SoC estimation error of less than 1% at 25 °C and effectively tracks the reference SoC with high robustness. Furthermore, the terminal voltage prediction error of the identified model remains within ±0.1 V, confirming model accuracy. These results validate that the proposed LDL-MIAUKF algorithm significantly improves estimation accuracy, stability, and adaptability, making it a promising solution for advanced battery management systems. Full article

An equivalent circuit model (ECM) is a highly practical tool for simulating Li-ion battery behavior. There are many relevant studies which compare different ECM variants or suggest algorithms to extract model parameters from the experimental data. However, little attention has been given to the battery tests used for identification of the ECM parameters. Therefore, here the influence of experimental test pulse characteristics on the parameterized ECM accuracy was systematically studied. The test pulse duration was varied in a wide range from 9 s to about 2.5 min. The portion of the relaxation phase data used by the parameter optimization algorithm was also varied in an even wider range. Total 168 ECM parameter sets were obtained. Each parameter set was validated using nine diverse current profiles representing different battery operation conditions, including one based on Urban Dynamometer Driving Schedule (UDDS). The validation results prove that the impact of the test pulse choice on the parameterized ECM accuracy is great to the point that it can overshadow the use of a higher-order Thevenin model. By choosing the optimal parameter set, the simulated voltage root mean square error (RMSE) was reduced to as low as 3.0 mV and 1.2 mV for first- and second-order ECM, respectively, while the second-order model based on arbitrary chosen test pulse on average yields RMSE value above 5 mV. Full article

This study presents a comparative analysis of the performance and modeling differences among lithium titanate oxide (LTO) batteries with three different cathode materials. An evaluation was conducted by performing performance tests over −20 °C to 25 °C at various current rates. Differences in open-circuit voltage curves, as well as charge and discharge capacities under different temperatures and C-rates, were systematically compared. At 25 °C, the NCM cathode enabled superior rate capability, retaining over 90% of its capacity at 8 C discharge, whereas the LCO-based cells exhibited significant capacity fade. Conversely, at −20 °C, the LCO cathode demonstrated better low-temperature performance, delivering almost 80% of its room-temperature capacity at 4 C, compared to less than 5% for the NCM cathode. The batteries were modeled using a second-order equivalent circuit model, and variations in model parameters were analyzed from the perspectives of internal resistance and electrode kinetics. The second-order equivalent circuit model revealed that the NCM-based cells had lower ohmic resistance and faster electrode kinetics. By correlating battery performance with cathode materials, this study evaluates the suitability of LTO batteries with different cathodes for various application scenarios, providing valuable insights for battery application and management. Full article

Accurate state of charge (SOC) estimation is crucial for the safety, reliability, and energy efficiency of lithium-ion battery systems. However, variations in battery parameters and the loss of historical information during the update steps of traditional unscented Kalman filters (UKFs) often lead to decreased estimation accuracy under dynamic operating conditions. To address these issues, this paper proposes a variable forgetting factor recursive least squares (VFFRLS) algorithm combined with a multi-innovation unscented Kalman filter (MIUKF) algorithm. First, a second-order RC equivalent circuit model is established, and the battery parameters are identified online using the VFFRLS method, enabling the model to dynamically adapt to changing operating conditions. Then, multi-innovation theory is incorporated into the standard UKF, extending the single-innovation matrix to a multi-innovation matrix, effectively enhancing the utilization of historical residuals and improving robustness to measurement noise and model uncertainty. Experimental validation under four typical dynamic operating conditions (FUDS, DST, BJDST, and US06) demonstrates that the proposed method significantly improves SOC estimation accuracy. Compared to the traditional UKF, MIUKF reduces MAE and RMSE by 25–30% while maintaining real-time performance, with single-step computation time reaching the microsecond level. Robustness tests under different initial SOC errors further validate MIUKF’s strong robustness to initial biases. In summary, the proposed method provides an effective solution for high-precision SOC estimation of batteries and has the potential for application in battery management systems. Full article

Because of the problem that the traditional unscented Kalman filter algorithm (UKF) may terminate the iteration due to the non-positive definite error covariance matrix during state of charge (SOC) estimation of lithium-ion battery, considering the unknown noise and current mutation during the actual operation of the battery, an SOC estimation method based on covariance adaptive singular value decomposition unscented Kalman filter (CA-SVDUKF) algorithm was proposed. Based on the singular value decomposition traceless Kalman filtering algorithm, the proposed CA-SVDUKF algorithm introduced an adaptive method of covariance matching to improve the algorithm’s anti-interference capability to unknown noise. Accordingly, an error covariance matrix adaptive method with adaptive scaling factor was proposed, which could reduce the influence of current mutation exerting on the estimated convergence rate. Taking the lithium-ion battery as the research object, the second-order RC equivalent circuit model of the lithium-ion battery was first built, and then the online parameters of the battery were identified. Finally, the CA-SVDUKF algorithm was used to complete the SOC estimation. The algorithm was simulated and verified under three working conditions: ordinary pulse condition, DST working condition, and US06 working condition. The experimental results showed that the algorithm had higher accuracy and stability compared with the traditional extended Kalman filter algorithm (EKF) and the UKF algorithm. The maximum absolute error was less than 0.6%, and the root mean square error was less than 0.3%, which could verify the effectiveness and superiority of the algorithm. Full article