Search Results (187)

Accurate state of health (SOH) estimation of lithium-ion batteries is essential for ensuring the safe and efficient operation of electric transportation and grid-scale energy storage systems (ESS). However, extracting reliable degradation information from Battery Management System (BMS) data remains challenging due to measurement noise, operating variations, and nonlinear characteristics of charging signals. To address this issue, a multi-scale spatiotemporal deep learning framework based on a multi-scale convolutional neural network and bidirectional long short-term memory (MS-CNN-BiLSTM) is proposed. A degradation-aware sliding-window strategy is first designed to extract aging-related features from charging signals, while parallel multi-scale convolution branches with different receptive fields are employed to capture temporal characteristics at multiple scales. Subsequently, an attention-enhanced BiLSTM module is introduced to aggregate long-term degradation dependencies and adaptively capture informative temporal representations. The proposed framework is evaluated using lithium-ion batteries with different chemistries, including the NASA LCO and MOLICEL NCM datasets. Experimental results demonstrate that the proposed method achieves accurate SOH estimation with MAE values as low as 0.0026 and R² values above 0.979. Furthermore, the model maintains consistent estimation performance across different battery chemistries with relatively low computational complexity, suggesting its potential suitability for embedded BMS applications. Full article

This paper investigates the memory usage of battery management system (BMS) software functions required for operating the high-voltage battery system (HVBS) of a light-duty electric vehicle (EV). Seven BMS software functions—battery balancing and monitoring, state of charge (SoC) and power estimation, fault diagnosis, contactor control, and communication management—are developed, validated through processor-in-the-loop (PIL) testing, and evaluated for memory usage when deployed on the automotive-grade NXP S32K358 microprocessor unit. A comprehensive high-voltage battery and management system for the RAM ProMaster EV is developed to ensure the memory assessment reflects real-world deployment conditions, including a detailed mechanical and electrical design of the battery module, battery disconnect unit (BDU), high-voltage mechanical structure, and a thorough software architecture. A high-fidelity plant model of the HVBS is implemented in the MATLAB/Simulink 2023b environment to capture the electrical and structural characteristics of the system, enabling validation of the BMS software functions prior to deployment. An experimental dataset for 21,700 cylindrical lithium-ion cells from the RAM ProMaster EV is used to parameterize and test state estimation algorithms. The dataset covers a wide range of operational conditions, including multiple standard and non-standard drive cycles (UDDS, LA92, US06, HWFET, HWCUST and HWGRADE) and characterization tests (C/20 discharge, four-pulse HPPC and GITT). The results show that the SoC estimation system accounts for the majority of the memory used by BMS software functions, reaching 31.4 kB of flash and 52.2 kB of RAM, while the remaining functions exhibit significantly lower memory demands, consuming no more than a half of that amount. Overall, the findings suggest that the BMS software functions occupy a relatively small portion of the S32K358’s 8 MB of flash and 1125 kB of RAM (0.84%-flash and 8.63%-RAM) compared with the system overhead (3.89%-flash and 6.30%-RAM), leaving sufficient margin for integrating more advanced estimation and protection functions in future EV battery systems. Full article

Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and life cycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy storage systems. Against this backdrop, this study proposes a hybrid approach combining an electro-thermal model with a feedforward neural network (FNN) to improve the estimation of key lithium-ion battery performance indicators within a temperature range of 0 °C to 40 °C. The developed methodology was implemented in MATLAB/Simulink and applied to the analysis of the vehicle’s power profile, as well as the evolution of SoC, SoH, and battery life cycle. The results demonstrate an accuracy of 95.3% for state of charge (SoC) estimation, with a mean absolute error of 4.7%. For state of health (SoH) estimation, the accuracy is 95.8% accompanied by a mean absolute error of 4.2%. Lastly, for life cycle prediction, the accuracy is 92.5% with a mean absolute error of 7.5%. The performance results demonstrate the robustness of the proposed approach and its ability to replicate battery dynamic behavior under climatic conditions representative of the African context. This contribution opens up promising avenues for optimizing battery management systems and advancing the sustainable development of electric mobility. Full article

Accurate online state of health (SoH) estimation, especially for highly degraded second-life batteries (SLBs), is critical for the safe and efficient operation of any Battery Management System (BMS). However, existing data-driven estimation methods typically rely on complete charge/discharge cycles or assume a fixed starting state of charge (SoC) and fixed voltage windows. These assumptions are highly restrictive and rarely align with the random, partial charging behaviors of real-world electric vehicle (EV) operations and Battery Energy Storage System (BESS) applications. To overcome this limitation, this paper presents the following framework: an online SoH estimation approach utilizing long short-term-memory (LSTM)-ensembled learning using consecutive constant current (CC) charging segments. Instead of relying on a rigid voltage window, the presented method utilizes nested, expanding slices of highly flexible CC charging intervals to capture sequential degradation dynamics. Furthermore, this study maps suggestive ranges of optimal voltage intervals that dynamically adapt to different battery health conditions, ensuring high estimation accuracy despite the type of degradation. The framework is validated using four commercially available lithium-ion batteries (LIBs), with a moderate to low SoH down to ~40%. To ensure practical viability, the hybrid deep neural network (DNN) and LSTM architecture has been highly optimized, requiring only 474 trainable parameters. The framework has been evaluated utilizing multiple performance matrices, showing minimal discrepancy with excellent correlation to the true SoH throughout the lifespan of the testing cell. Full article

In electric vehicles (EVs), the battery management system (BMS) plays a central role in ensuring safe, efficient, and reliable battery operation under varying driving and environmental conditions. The effectiveness of a BMS largely depends on the availability of an accurate battery model, whose performance is strongly influenced by the precision of its identified parameters. However, estimating these parameters remains a difficult nonlinear optimization problem, especially under low state of charge (SOC) operation. Classical identification approaches often have limited robustness under such conditions, while metaheuristic algorithms provide a promising alternative because of their ability to handle nonlinear and multimodal search spaces. Even so, many existing methods still encounter drawbacks related to convergence speed and susceptibility to local optima. Motivated by these challenges, this study investigates the recently introduced Artificial Lemming Algorithm (ALA) for parameter identification of a second-order equivalent circuit model (2RC-ECM) under EV-oriented low-SOC operating conditions. Experimental validation is conducted using two independent dynamic datasets, namely the High Dynamic Profile (HDP) at 25 °C and the Urban Dynamometer Driving Schedule (UDDS) at −5 °C, involving different lithium-ion cells and operating conditions. ALA is benchmarked against nine competing metaheuristic algorithms under identical search boundaries and computational settings. Performance is assessed using RMSE, MAE, MaxAE, bias, convergence behavior, error distributions, execution time, and sensitivity to the number of independent runs, population size, and maximum number of iterations. The results show that ALA achieves the lowest minimum, mean, and maximum RMSE for both datasets, with minimum RMSE values of $0.01075 \mathrm{V}$ for HDP and $0.03534 \mathrm{V}$ for UDDS. Unseen-data validation further yields RMSE and MAE values of $0.0082$ and $0.0061 \mathrm{V}$, respectively, for HDP, and $0.0416$ and $0.0299 \mathrm{V}$, respectively, for UDDS. In addition, convergence, error-distribution, and sensitivity analyses show that ALA maintains competitive and consistent performance across the investigated configurations. Overall, the results demonstrate that ALA provides a favorable balance between estimation accuracy, robustness, convergence behavior, and computational cost for offline lithium-ion battery parameter identification. Full article

Accurate State-of-Health (SOH) prediction is essential for deploying retired electric vehicle batteries into reliable second-life energy storage systems. However, this task is challenged by sparse and noisy operational data from onboard Battery Management Systems (BMS). This study systematically evaluates a spectrum of deep learning architectures for SOH forecasting under BMS-style data constraints derived from laboratory cycling data: a BiLSTM on aggregated cycle statistics (Model A), preliminary zero-shot transfer to a single unseen cell (Model B), a waveform BiLSTM with full intra-cycle voltage, current, and temperature trajectories (Model C), a baseline TCN (Model D) and a probabilistic TCN-GPR hybrid (Model E). All models are constrained to identical low-fidelity BMS-style variables extracted from the NASA battery aging dataset. Model C achieves the lowest point accuracy error of 0.46% ± 0.18% MAE across five random seeds, demonstrating that high-resolution waveform inputs capture degradation signatures, notably voltage plateau morphology, transient dynamics, and implicit SOC information, that aggregated features irreversibly lose. Model D using the same waveform inputs and evaluation protocol as Model C, achieves a MAE of 2.99% at a single seed, providing direct architectural comparison evidence that the BiLSTM’s position-sensitive temporal summarization outperforms GlobalAveragePooling1D under these conditions. Model E achieves a higher MAE of 2.12% ± 0.33% but uniquely provides calibrated predictive distributions of 99.4% ± 1.2% coverage, NLL = −1.877 ± 0.038, with approximately uniform 95% predictive intervals (mean width 19.83% SOH across 34 test cycles at seed = 42), reflecting the near-constant posterior variance produced by the large optimized GPR length-scale under the frozen two-stage training design. A paired t-test confirms that Model C statistically significantly outperforms Model E on point accuracy (p < 0.01). Isotonic regression recalibration reduces mean calibration error from 0.138 to 0.010, demonstrating that shape-level miscalibration is correctable post hoc. The central implication for second-life battery deployment is a clear accuracy–uncertainty trade-off: Model C is preferred when point estimates suffice, while Model E is essential for risk-aware decisions requiring confidence intervals. Full article

The intensive development of electric vehicle (EV) technology, renewable energy systems, and stationary energy storage solutions has amplified the demand for advanced Battery Management Systems (BMS). The imbalance in cells within lithium-ion battery packs, due to manufacturing tolerances, varying aging, and thermal gradients, reduces available capacity, cycle life, and can cause thermal runaway. Active charge equalization with DC–DC converters has become a recent research focus among various balancing techniques because it has a better capability of redistributing energy. This paper gives a detailed study of converter-based cell-balancing topologies with a specific focus on the bidirectional flyback converter and the interleaved two-phase variant. Non-isolated topologies (buck–boost, Cuk converter topology, interleaved buck–boost) and isolated topologies (flyback, push–pull, dual-active bridge, LLC resonant) are compared concerning functional efficiency, component reduction, galvanic isolation, scalability, and bidirectional capability. The concept of soft-switching, including zero-voltage switching (ZVS) and zero-current switching (ZCS), and their circuit realizations are discussed. Advanced control models and artificial intelligence (AI) for the estimation of state-of-charge (SoC) and real-time optimization are mentioned. Thermal issues, scalability, reliability, and wide-bandgap semiconductor devices (SiC/GaN) are discussed. Full article

In the context of escalating implementation of electric vehicles (EVs), further research is required to investigate the impact of empirical driving conditions on energy demand and battery performance. In spite of the fact that microscopic traffic simulation and EV energy modelling are already used extensively, their use is still limited in studies focusing on batteries. Specifically, in most existing approaches, the effect of traffic-induced variability on battery stress is not explicitly accounted for. The study presented here examines a systematic framework that combines energy demand, traffic dynamics and battery behaviour. Using the SUMO simulator, vehicle trajectories are converted into electric vehicle (EV) energy profiles via a physics-based longitudinal model, thereby estimating battery power, energy consumption, regenerative effects and changes in state of charge (SOC). Next, a variety of indicators related to the battery management system (BMS) are introduced, including the Battery Stress Index (BSI), a traffic–energy severity (TES) indicator and event-based measures for transient conditions. The results show that traffic variability leads to significant fluctuations in battery load, which are not fully captured by conventional energy metrics. The proposed indicators provide additional information on cumulative and dynamic battery solicitation while remaining physically interpretable. Taken together, this framework links traffic conditions and battery solicitation in a coherent approach, thereby creating a scalable approach to traffic-aware energy analysis. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for battery management systems (BMS), as it directly affects system safety, operational reliability, and remaining useful life prediction. Traditional SOH estimation methods generally rely on complete charge–discharge cycle data, which limits their practicality in real-time and online applications. To address this limitation, this paper proposes a novel voltage-charge increment curve-based dual-branch bidirectional long short-term memory network (VCIC-DB-BiLSTM) for battery SOH estimation under incomplete data. First, non-uniformly sampled battery current-voltage data are processed into standardized sequences with equal voltage intervals via voltage-charge increment curves based on ampere-hour integration and cubic spline interpolation. Subsequently, sliding window segmentation is applied to extract fixed-length curve segments from continuous voltage intervals as input features, while the corresponding complete voltage interval curves are used as labels. Finally, the VCIC-DB-BiLSTM network is designed, which uses a dual-branch structure to integrate feature extraction from both patch-processed and raw data, combined with bidirectional sequential modeling. Experimental validation on four benchmark datasets, CALCE, Oxford, XJTU, and TJU, demonstrates that the proposed method achieves competitive performance in SOH estimation under incomplete discharge data conditions, confirming its effectiveness and practical applicability. Full article

Accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is critical to ensuring the safety and reliability of battery management system (BMS). To achieve precise estimation, this study proposes a hybrid framework that integrates variational mode decomposition (VMD), kernel principal component analysis (KPCA), polar lights optimizer (PLO), and physics-informed neural network (PINN) for SOH estimation. First, multidimensional health features are extracted and decomposed by VMD into intrinsic mode functions (IMFs), which are then compressed into a one-dimensional principal component via KPCA, retaining over 95% of the original information. Subsequently, the PLO algorithm is used to adaptively optimize three key hyperparameters of the PINN-based model: the learning rate, the number of collocation points, and the regularization loss weight. Finally, the optimized PINN is deployed to predict the SOH of the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. Experimental results demonstrate that the proposed VMD-KPCA-PLO-PINN exhibits high prediction accuracy under both 7:3 and 5:5 training-to-testing data partitions. For example, under the 5:5 partition, the proposed model achieves an average $R^2$ of 0.983 and an average RMSE of 0.0085 on the tested CALCE cells. Full article

Reliable state of health (SOH) and remaining useful life (RUL) estimation is essential for lithium-ion battery diagnostics, prognosis, and management across cell, module, and pack levels. Yet the reported performance metrics often remain tied to controlled cell datasets, with batteries degrading due to chemistry shifts, protocol variation, temperature changes, inconsistent and insufficient measurements, and pack-level heterogeneity. This critical review investigates what evidence is required before an SOH/RUL estimator can be considered trustworthy, transferable, and suitable for battery management system deployment. Based on a de-duplicated classified set of 176 scientific works and a supplementary evidence audit workbook, this review synthesizes model-based, machine learning, deep learning, transfer learning, physics-informed, impedance-based, thermographic, relaxation-based, and digital twin approaches through observability, robustness, uncertainty calibration, transferability, and deployment feasibility. A compact mathematical framework formalizes the health inference, domain shift, cross-fidelity degradation, calibrated uncertainty, and BMS-facing validation criteria. The analysis argues that deployment-ready battery health intelligence should be evaluated as an evidence system rather than as a point prediction task. The proposed multi-fidelity validation framework links synthetic cells, controlled aging, module (pack) testing, fleet shadow operation, and closed-loop safety-governed deployment using acceptance criteria, based on worst-domain error, calibration data, warning risk, and computational feasibility. Full article

Accurate state-of-charge (SOC) estimation is essential for preventing battery degradation, improving energy management, and providing reliable driving-range predictions in electric vehicles (EVs). The extended Kalman filter (EKF) is a widely adopted model-based estimation technique and remains an industry-standard approach in EV battery management systems (BMS). However, its performance can be degraded by model nonlinearities, parameter uncertainties, measurement noise, and interference bursts commonly encountered in real-world operating environments. To overcome these limitations, this paper proposes an event-triggered adaptive SOC estimation framework that integrates a recursive least squares (RLS) filter with the EKF. In the proposed approach, the RLS filter recursively updates its weighting coefficients in real time to compensate for model uncertainties and measurement disturbances, thereby generating an alternative residual signal for SOC estimation. An event-triggered mechanism dynamically selects the most reliable innovation sequence for updating the EKF state estimate, enhancing estimation robustness under adverse operating conditions. A second-order RC equivalent circuit model (ECM) is employed as the nominal battery model, and a Hybrid Pulse Power Characterization (HPPC)-based current profile is used to evaluate performance over the entire SOC operating range. Extensive simulations are conducted to assess the effectiveness of the proposed event-triggered RLS-EKF algorithm under various noise levels and interference-burst scenarios. The estimation accuracy is compared with that of the conventional EKF, cubature Kalman filter (CKF), and square root cubature Kalman filter (SR-CKF) using root mean square error (RMSE) and mean absolute error (MAE) as performance metrics. Simulation results demonstrate that, under regular noise conditions and short-term interference bursts, the proposed event-triggered RLS-EKF achieves estimation performance comparable to that of the SR-CKF while consistently outperforming the EKF and CKF in both RMSE and MAE. Under long-term interference-burst conditions, the proposed method further surpasses the SR-CKF, achieving approximately 10% improvement in overall estimation accuracy as measured by RMSE and MAE. These results confirm the effectiveness and robustness of the proposed framework, highlighting its potential for practical implementation in advanced EV battery management systems. 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

Accurate State of Energy (SOE) estimation is a critical yet challenging requirement for reliable Battery Management Systems (BMSs), as traditional model-based algorithms like the Extended Kalman Filter (EKF) are severely limited by their dependence on precise, static noise covariance matrices. To address this deficiency, this study developed an Improved Sage–Husa Extended Kalman Filter (SHEKF) to enhance the precision and stability of SOE estimation in lithium-ion batteries. The methodology began by simulating a second-order (2RC) equivalent circuit model utilizing battery parameters accurately identified through HPPC test data. Unlike traditional filters, the proposed SHEKF overcomes the limitations of fixed noise profiles by using filter innovation to dynamically update its noise covariances in real time. The adaptive algorithm was then evaluated against a standard EKF and a Strong Tracking EKF (STEKF) using dynamic FUDS and UDDS drive cycle datasets. The results demonstrated that the SHEKF consistently outperformed both the EKF and STEKF across all metrics. The on-line adaptive mechanism successfully reduced the SOE Root Mean Square (RMS) Error by 75% to 0.58% and achieved a similar 75% reduction in RMS voltage error compared to the EKF. Furthermore, the SHEKF maintained highly stable error traces, recording a maximum absolute SOE error of just 0.84%. By automatically adjusting noise parameters without manual tuning, the algorithm avoids the filter divergence commonly seen when noise characteristics change. These findings establish the Improved SHEKF as a highly robust, practical, and superior method for real-time SOE estimation in modern real-world BMS applications. 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