Search Results (401)

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

As power grids transition towards highly renewable generation on a global scale, maintaining dynamic stability is becoming a major challenge. Replacing traditional synchronous generators with inverter-based renewables strips the grid of rotational inertia, leaving active distribution networks highly vulnerable to frequency deviations and voltage spikes. To avoid expensive poles and wires upgrades, Battery Energy Storage Systems (BESS) are increasingly being deployed as Non-Network Solutions (NNS). However, the current literature reveals a distinct gap between the macro-scale economic planning of these storage assets and the micro-scale dynamic control actually required to keep the grid resilient. To address this gap, this review proposes a multi-layer deterministic synthesis framework that links physical renewable modelling, degradation-aware techno-economic planning, deterministic forecasting, and EMS dispatch through offline time-domain control validation for AC-microgrid energy storage integration. The research examines how advanced central control units within battery management systems can rigorously and jointly estimate State of Charge (SoC) and State of Energy (SoE) to ensure accurate grid-aware dispatch. Furthermore, the study explores the integration of degradation-aware economic modelling in HOMER Pro with dynamic transient control in MATLAB/Simulink R2025b, driven by hybrid metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). This analysis demonstrates that integrating energy storage must be treated as a tightly coupled multidimensional optimization problem to successfully deliver the secure and sustainable infrastructure needed to solve the modern energy trilemma. Full article

The accurate estimation of lithium-ion battery state of health (SOH) is crucial for battery monitoring, safety, and degradation assessment; however, it remains challenging because of the nonlinear nature of battery degradation, measurement noise, and variability in the battery aging trajectory. This study aims to solve these problems by proposing a hybrid attention-based BiLSTM–RF model, which combines wavelet-based signal denoising, incremental capacity analysis (ICA)-based feature extraction, stacked Bidirectional Long Short-Term Memory (BiLSTM) networks, multi-head self-attention, principal component analysis (PCA)-based feature compression, and ensemble regression using a Random Forest (RF) model with adaptive weighted fusion. The proposed framework was tested on the NASA battery datasets (B0005, B0006, B0007 and B0018) and was further validated on the Oxford Battery Degradation Dataset using leave-one-battery-out cross validation conditions. Experimental results indicated that, in general, the proposed framework outperformed the evaluated benchmark models (CNN-LSTM, BiLSTM, and RF models) in terms of the prediction error, with a minimum RMSE value of 0.0229 for NASA battery B0007 and 0.0024 for Oxford Cell3. Ablation analysis also showed that the combination of wavelet denoising, PCA compression, temporal sequence learning and ensemble regression played a role in the overall SOH estimation performance. These results show that the proposed hybrid approach is effective and stable for SOH estimation in different battery degradation trajectories under the tested experimental conditions. Full article

As lithium-ion batteries are widely deployed, thermal runaway (TR) poses severe safety risks, making early and accurate warning systems critical. While machine learning (ML) has advanced data-driven TR prediction, challenges remain regarding model interpretability, generalization under unseen conditions, and real-time deployment. This review evaluates recent progress in ML-driven TR warning technologies, moving beyond a mere compilation of algorithms to provide an organized synthesis of the field. As a key contribution, we critically analyze the paradigm shift toward physics-informed ML, demonstrating how embedding electrochemical and thermodynamic principles into neural networks reduces prediction errors by 40–60% while enhancing robustness. Furthermore, we synthesize a Battery Digital Twin (BDT) framework integrating Internet of Things (IoT), cloud computing, and on-board master BMS for closed-loop collaboration, effectively balancing low-latency control with high-precision health assessment. Finally, we outline strategic pathways for future breakthroughs: advancing physics-informed cross-scale modeling, optimizing cloud-edge architectures, and establishing open access benchmark databases. By calling for standardized evaluation protocols to break down data silos, this review provides a comprehensive roadmap and actionable insights to accelerate the industrial implementation of next-generation intelligent battery safety management. Full article

Effective thermal management is crucial for the performance, thermal safety, and lifespan of lithium-ion batteries in electric vehicles (EVs). Thermal management strategies are essential for preventing overheating, thermal imbalance, and the associated risk of thermal runaway. Nanofluids are emerging and attracting considerable attention as potential coolants for high-power energy storage and electronics systems. This review updates and summarizes the most recent advances in nanofluid-based cooling strategies for battery thermal management systems (BTMSs) over the past five years, emphasizing their implications for battery thermal safety. Three main nanofluid-based cooling strategies have been evaluated in depth, including nanofluid-based indirect liquid cooling, nanoparticle-enhanced PCM cooling, and nanofluid-based heat pipe cooling. Various nanofluid formulations, including mono, hybrid, and ternary nanofluids, have been considered and evaluated for their heat dissipation under high charge/discharge and abuse-relevant conditions. Thermal and hydraulic performance characteristics, including maximum temperature, maximum temperature difference, and pressure drop, have been comprehensively evaluated for different nanofluid-based cooling strategies. The findings demonstrated that nanofluids significantly improved heat transfer rates and enhanced temperature control efficiency. In particular, hybrid and ternary nanofluids exhibit superior thermal performance and effectively suppress the escalation of safety-critical temperatures. Beyond summarizing cooling performance, this review further discusses the role of nanofluid-based cooling strategies as functional thermal-control layers within intelligent BTMS architectures. Particular attention is given to their compatibility with sensing networks, BMS-/VCU-level supervisory control, predictive thermal models, actuator responsiveness, fault-warning algorithms, and long-term reliability under realistic driving and fast charging conditions. Therefore, this review provides architecture-oriented insights for developing safe, energy-efficient, and control-ready BTMSs for next-generation high-power and connected EVs. Full article

By representing the battery pack as a networked system, the battery management system (BMS) is formulated as a multi-agent system, and voltage equalization is thereby transformed into cooperative control among multiple agents. Furthermore, some potential uncertainties in practical applications are taken into consideration. Specifically, we investigate prescribed performance control (PPC) for multiple parametric strict feedback (PSF) systems subject to time-varying uncertainties, such as polarity reversal and parameter variations, which are common in battery packs. The main contributions of this paper are threefold: (1) It addresses a more challenging case in which the uncertainties in the agents’ models are time-varying, including unknown control coefficients and uncertain parameters. (2) Both the steady-state control objective and the transient performance are guaranteed simultaneously. (3) The analysis is simplified by designing a one-dimensional parameter estimator and eliminating the constraint on initial conditions through a simple and reasonable setting. Simulation studies are conducted to demonstrate the effectiveness of the proposed control scheme, and a comparison with traditional methods is presented. This work provides a theoretical basis for the design of BMS. Full article

The reduction in driving range during winter remains a major barrier to the widespread adoption of battery electric buses (BEBs), as battery performance degradation and increased Heating, Ventilation and Air Conditioning (HVAC) energy demand significantly raise total energy consumption. This study investigates the use of proton exchange membrane fuel cells (PEMFCs) as auxiliary power units for range-extended electric buses (FC-REEBs) under low-temperature conditions to address this challenge. A comprehensive dynamic model was developed in MATLAB/Simulink 2025a version, integrating a fuel cell system, lithium-ion battery, power conversion unit, vehicle dynamics, and cabin thermal model. The model was evaluated under the World Harmonized Vehicle Cycle (WHVC) to compare three fuel cell operation strategies defined by fuel cell capacity and operating modes for cabin heating and battery charging. Performance was compared in terms of SOC variation, fuel cell loading patterns, hydrogen consumption, and equivalent fuel economy. Results indicate that the high-capacity strategy improves SOC stability but increases hydrogen consumption and reduces overall efficiency. In contrast, the strategy prioritizing cabin heating with minimal battery charging effectively utilizes waste heat and achieves the highest equivalent fuel economy. These findings highlight key trade-offs among different operating strategies and demonstrate that fuel cells can significantly enhance BEB efficiency and driving performance in cold environments while reducing battery load. Full article

Lithium-ion batteries (LIBs) are widely used in the electric bicycle/vehicle sector, but fire accidents frequently caused by thermal runaway of LIBs have become a severe public concern. From a reverse perspective of safety engineering, investigation of fire accidents based on the historical data recorded by the Battery Management System (BMS) and exploration of the causes of thermal runaway can enhance the safety of LIBs and electric bicycles/vehicles. This study aims to provide support for fire investigation through the analysis of the BMS. By conducting electrical, thermal and mechanical abuse experiments, the variations of the electrothermal parameters involving voltage, current and temperature are examined. The results reveal that these electrothermal parameters exhibit unique time-sequential inter-relationships under each specific abuse mode. A secured relationship can be solidified between the variation features of the electrothermal parameters and the specific cause of thermal runaway, i.e., whether the abuse mode is electrical, thermal or mechanical abuse. Such peculiar time-series variations or inter-relationships can be used for post hoc fire investigation to trace the fire reasons. Based on the findings of this study, a real fire case was analyzed to validate the feasibility of the proposed tracing method by means of BMS analysis. The resultant fire reason confirmed the one given by the authority, thus validating the effectiveness of the fire investigation method. Full article

Accurate battery state-of-charge (SOC) estimation is a core function of battery management systems (BMSs) for electric vehicles (EVs), as it directly affects energy management, safety, and reliability. However, battery aging significantly degrades the accuracy of conventional SOC estimation methods by causing capacity loss, increased internal resistance, and changes in voltage response characteristics. To address these issues, this study proposes an aging-aware SOC estimation method that combines an equivalent-circuit model (ECM) with an extended Kalman filter (EKF). In the proposed framework, aging effects are explicitly incorporated by using offline-identified SOH-dependent model parameters, including effective capacity, RC parameters, and SOC–OCV characteristics, and scheduling these parameters within the EKF prediction and correction process according to the available SOH information. Furthermore, the performance of the proposed method is experimentally validated under an Urban Dynamometer Driving Schedule (UDDS) using cylindrical lithium-ion cells with large current fluctuations. The experimental results demonstrate that the proposed aging-aware EKF maintains stable SOC estimation performance not only in the initial battery state but also throughout the gradual aging process and up to the end of battery life. These results demonstrate the potential of SOH-scheduled, aging-aware EKF-based SOC estimation to improve SOC accuracy in aged batteries under the investigated laboratory and dynamic load conditions. Full article

Cell imbalance in battery packs can cause premature termination during battery discharge and recharge processes. This condition can decrease the usable energy of the battery. The cost of batteries can reach 30–40% of the price of an electric vehicle, so battery cell balancing in a battery management system (BMS) and a battery thermal management system (BTMS) is very important to maximize battery capacity, safety, and life. In conventional active balancing studies, the cell-balancing process draws energy from the cells or battery pack, resulting in a reduction in battery pack energy due to power losses during the balancing process. This condition can reduce the range of electric vehicles. In this paper, a battery balancing system with a reduced number of switches and low cost, as well as the use of a cell charger, is proposed. The cell charger will draw energy from the electrical grid so that it can maximize the energy in the battery pack. A balancing current of 3 A from the cell charger is used in the balancing process. A 23S1P 100 Ah LiFePO4 battery pack, consisting of 23 cells, is used for validation. Test results show that the proposed battery balancing system can balance the voltage of 23 battery cells for 40 minutes from the highest and lowest voltage difference of 116.7 mV to 11.8 mV. Full article

The battery management system (BMS) is the core of ensuring the safety and performance of new energy vehicles, and real-time high-precision estimation of battery state of charge (SOC) is its key function, which directly affects battery safety, endurance, and service life. Faced with the challenges brought by high energy density and ultra-fast charging technology, lithium-ion batteries exhibit strong nonlinear and time-varying characteristics, making it difficult for existing SOC estimation methods to balance computational efficiency and accuracy. This study proposes a Bayesian-based Hammerstein multi-model (MM) fusion algorithm for accurate lithium battery SOC estimation across a wide temperature range, especially under low-temperature conditions. First, two Hammerstein SOC submodels are constructed: a traditional polynomial Hammerstein model and a TPA-Hammerstein model incorporating the temporal pattern attention mechanism. Second, KV-ADAM is employed for parameter training and identification of the submodels. Finally, a Bayesian weighted fusion strategy is used to dynamically integrate the outputs of the two submodels. The experimental results show that this method significantly improves the accuracy and robustness of SOC estimation, overcomes the limitations of a single model under complex dynamic conditions, provides an effective solution for lithium battery SOC estimation, and helps the safe operation of electric vehicles and the sustainable development of the industry. Full article

State of charge (SOC) estimation is a key function in battery management systems (BMSs) because it directly affects safe operation and available energy prediction. In embedded BMS platforms, information from multiple cells must be processed within tight computation and memory budgets. The estimator therefore needs to balance accuracy and implementation cost. This paper presents a lightweight SOC estimation method based on the relationship between open circuit voltage and state of charge (OCV–SOC) in lithium-ion batteries, together with a standalone gauge IP based on finite-state machine (FSM) control. The reference OCV–SOC curve of a commercial 3.7 V lithium-ion cell is approximated by a two-region quadratic model. The IP estimates OCV from the measured terminal voltage with equivalent series resistance (ESR) correction and updates SOC iteratively. To obtain predictable runtime behavior and to suppress oscillatory behavior near convergence, the hardware combines a 1-LSB termination rule with a guard based on a maximum iteration count of $N_{max}=10$. Real-time validation on an FPGA-based battery measurement testbed achieves an overall normalized mean absolute error (NMAE) of 1.6% over charge and discharge data. When synthesized for an Artix-7 XC7A100T, the proposed gauge IP used only 504 LUTs (0.79%) and 580 FFs (0.46%). A TSMC 28 nm MPW implementation further demonstrates feasibility for integration at chip level. Full article

One of the most important functions of a battery management system (BMS) is cell balancing. The limitations of active balancing systems arise from reactive control strategies that rely exclusively on instantaneous measurements of cell voltage or state of charge (SOC). Such strategies do not account for short-term voltage dynamics, which can lead to unnecessary energy transfers. This paper proposes a predictive cell balancing strategy based on cell voltage estimation, intended for active balancing systems, particularly those employing flyback converters. The proposed predictive model uses historical voltage and current measurements, as well as operating temperature information, to estimate the short-term evolution of the cell voltage. The model is trained using experimental datasets obtained from NCR18650B lithium-ion cells (Panasonic, Osaka, Japan) subjected to multiple current profiles and temperature conditions. The proposed strategy is implemented on the DC2100B-C module (Linear Technology, Milpitas, CA, USA), which employs the LTC3300-1 integrated circuit (Linear Technology, Milpitas, CA, USA), and is experimentally validated on a battery pack consisting of 12 NCR18650B cells connected in series. The experimental results demonstrate that the use of short-term voltage prediction improves the balancing process by reducing the voltage equalization time and the number of balancing command reconfigurations. 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

In the context of the rapid growth of the electric and hybrid vehicle fleet, ensuring the reliability, safety, and durability of traction lithium-ion battery packs has become a key scientific and engineering challenge. The technical condition of battery packs, characterized by such parameters as state of charge (SOC), state of health (SOH), and remaining useful life (RUL), directly affects vehicle performance and the total cost of ownership of electric vehicles. This review article systematizes and analyzes current approaches to assessing the technical condition of battery packs. Fundamental degradation mechanisms and factors are considered, including operational, thermal, and mechanical effects. A detailed analysis is presented for the three main classes of diagnostic methods: model-based approaches, data-driven approaches (machine learning and deep learning), and hybrid methods combining the advantages of the former two. Particular attention is paid to methods for early fault detection, thermal runaway prediction, and condition assessment based on real-world operational data. The article presents quantitative results demonstrating the accuracy and effectiveness of various algorithms and also discusses key challenges and promising research directions, such as the use of cloud platforms, digital twins, and explainable artificial intelligence methods. Full article