SOH Estimation of Lithium Battery Under Improved CNN-BIGRU-Attention Model Based on Hiking Optimization Algorithm

An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values.

Lithium-ion battery health state and remaining useful life prediction based on hybrid model MFE-GRU-TCA

Accurate estimation of the state of health (SOH) is a critical function of battery management system (BMS), essential for ensuring the safe and stable operation of lithium-ion batteries. To improve estimation precision, this paper proposes a novel health indicator (HI) construction method and an improved support vector regression (SVR) approach. First, the convolution operation is applied to discharge voltage data to extract new HIs that characterize battery aging; their correlations are then verified. Second, principal component analysis (PCA) is employed to reduce input dimensionality and computational burden. Third, to address the challenge of SVR parameter selection, an improved sparrow search algorithm (ISSA) is proposed for parameter optimization. Finally, the proposed method is validated using both the NASA dataset and a laboratory experimental dataset, with comparisons against existing approaches. The results show that the method achieves accurate SOH estimation under various aging conditions, demonstrating its effectiveness, robustness, and practical potential.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm

Accurate State of Health (SOH) diagnosis and forecasting are critical for ensuring the safety, efficiency, and longevity of batteries in electric vehicles (EVs). This novel research provides a comprehensive simulation-based comparative analysis of two Lithium Iron Phosphate (LFP) and Nickel Manganese Cobalt Oxide (NMC) batteries' degradation under realistic EV cycling conditions across an extended temperature range (25°C to 65°C). The design model used in this research, rigorously parameterized using published experimental data, tracks capacity fade and internal resistance growth, as well as coulombic efficiency decline, as key SOH indicators. Results of this novel and useful study demonstrate that both chemistries degrade faster with rising temperature, but NMC exhibits significantly accelerated aging above 50°C, reaching 80% SOH in fewer than half the cycles of LFP at 65°C. LFP maintains superior thermal resilience in a perfect manner, retaining higher SOH and lower resistance, including more stable coulombic efficiency across all temperatures. Achieved results also confirm and show greater degradation variability in NMC under thermal stress as well as increasing failure risk. This useful research quantifies critical temperature thresholds of batteries for end-of-life (SOH = 80%) and provides clear, data-driven guidance for battery selection and thermal management in EVs, particularly in hot climates or high-duty applications. By establishing a validated, multi-temperature simulation benchmark, this novel research work fills a major gap in chemistry-specific degradation forecasting and supports the design of adaptive battery management systems tailored to regional operating environments.

Accurate State of Health (SOH) estimation of lithium-ion batteries (LIBs) is critical for ensuring the safety of electric vehicles and improving the reliability of battery management systems (BMS). However, the use of individual health features (HFs) and the selection of hyperparameters can increase the data processing burden on the BMS and reduce the accuracy of data-driven models. To address the above issue, this paper proposes a novel SOH estimation method for lithium-ion batteries based on the PSO–GWO–LSSVM prediction model with multi-dimensional health feature extraction. To comprehensively capture the battery aging mechanisms, four categories of health features—time, energy, similarity, and second-order features—are extracted from the LIBs charging segments. The correlation between HFs and SOH is comprehensively evaluated through Pearson and Spearman correlation analyses, followed by Gaussian filtering and outlier detection to enhance feature quality. With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. The generalization performance of the proposed method is validated through comparative experiments using a battery dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland. Experimental results show that the coefficient of determination (R2) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. The comparative experiments demonstrate that the proposed method has a certain degree of accuracy, robustness, and generalization capability.

Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

Battery health prognosis using improved temporal convolutional network modeling

Uncertainties in a battery would result in unreliable state of health (SOH) estimation. Considering the greater risk after reaching the end of life (EOL), designing a suitable ensemble learning to provide early warning before reaching EOL with uncertainty measurement is desirable for confidence estimation. In this paper, a novel probabilistic ensemble learning method-Gaussian process-based neural networks is proposed for the SOH confidence estimation by describing the uncertainties in probabilistic form. First, different neural networks are built based on health features. Second, battery data are classified under the recovery of capacity and normal operation conditions to characterize the uncertainties of the data under different operation conditions. Besides, the Gaussian process-based neural networks method is constructed based on the data from different conditions for neural networks weighted ensemble with the probabilistic form of Gaussian distribution. Therefore, the uncertainties are measured in the probabilistic form considering different operation conditions which is different from other methods. With the probabilistic form, the confidence interval could be determined to ensure the real SOH within the confidence interval, which improves the estimation performance of the proposed method because of the early warning near the EOL. Finally, the effectiveness is validated by NASA data sets and our experiment with the commercial 18650 lithium-ion battery. From the results, the mean error is less than 1% and real SOH is within the confidence interval.

As a momentous indicator for the working status of Lithium-ion battery, state of health (SOH) has been adopted by various new energy enterprises. Especially in the electric vehicle industry, SOH is an essential guarantee for the safety and robustness of operating batteries. However, working in a complex open environment, the estimation model is often confronted with dynamic changes and abrupt disturbs, which demands an outstanding global information perception ability. Therefore, this study proposes a convolution Transformer-based multi-view information perception framework (MVIP-Trans) for SOH estimation. MVIP-Trans is dedicated to integrating the benefits of both Transformer and CNN architectures to learn global and local features with multi-view, thus achieving a comprehensive information perception. First, a local information perceptron based on Parallel Multi-Scale Attention (PMS-A) is constructed in order to extract features from local detailed information in voltage and current signals. With the Multi-scale Attention block, the perceptron has an excellent feature screening ability, thereby enhancing valuable information and restraining useless noise. Subsequently, to get an effective global information perception, a Transformer-attention architecture is introduced to encode the global dependencies between the filtered local features. Via multi-view learning mechanism, MVIP-Trans adequately modeled the internal correlation between SOH and the physical signals of batteries. Experiments by working through real operation datasets such as NASA PCoE and Oxford prove that this model can perfectly accomplish the SOH estimating task. During the experiments, MVIP-Trans obtained the best performance on NASA dataset, as RMSE is 0.005 and R2-score reaches 0.989. On oxford dataset, MVIP-Trans got a RMSE 0.003 and R2-score 0.997. These results prove that our model has a better performance than other existing methods.

Battery Management System (BMS) is an essential component for lithium-ion battery-based devices. It provides a variety of functionalities that help to improve the overall lifespan of the battery, including states estimation algorithms. An accurate state of health is one of the essential features an advanced BMS provides, in order to track long-term performance and ensure reliable operation of the battery. This paper aims to provide a structured review of ageing mechanisms that affect the battery performance and also some of the most relevant algorithms that were used in the recent literature to predict the actual health of the battery. Finally, based on the different approaches that were studied, evaluation criteria are proposed to help judge the performance of State Of Health (SOH) estimation algorithm.

A Contrastive Learning Battery State of Health Estimation Method Based on Self-supervised Aging Representation

Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression

As a key indicator of aging, accurate state of health (SOH) estimation of lithium-ion batteries helps to keep the safety and reliability of electrical vehicles. Capacity regeneration phenomenon, which unavoidably occurs in the battery aging, will change the degradation rate of batteries and reduce the SOH estimation accuracy. To solve this issue, this article proposes a maximum correntropy filter-based adaptive data-model fusion SOH estimation considering capacity regeneration. First, original capacity degradation data is decomposed into a residual reflecting degradation trend, and uncertain term containing local fluctuation and regeneration. Then, a data-driven SOH model is established by adopting a long short term memory neural network and Lévy process to fit the residual and uncertain term, respectively. Hence, the nonlinearity and long-term time dependence can be captured, and the uncertainty caused by regeneration can be well modeled. Then to reduce the accumulated error, a maximum correntropy filter is designed to achieve adaptive fusion prediction of the data-driven and empirical models. Experiment and simulation results shows the effectiveness of the proposed fusion method considering regeneration.

Accurate state of health (SOH) estimation of Li‐ion batteries is essential for ensuring safety, reliability, and prolonging battery lifespan in energy storage systems and electric vehicles. This study proposes a hybrid temporal convolutional network (TCN)–transformer framework that effectively captures both short‐term temporal dynamics and long‐term degradation trends in battery degradation. To enhance model interpretability, SHAP (SHAPley Additive exPlanations) analysis is incorporated, allowing a detailed quantification of each input feature's contribution, such as internal resistance, capacity, Constant current charge time, and constant voltage charge time, to SOH estimation. Experimental validation using the dataset from University of Maryland demonstrates that the proposed model outperforms traditional methods, including long short‐term memory, recurrent neural network, gated recurrent units, transformer, and TCN, across various evaluation metrics (mean absolute error, mean squared error, root mean squared error, R ). Beyond improved predictive accuracy, SHAP analysis offers valuable insights into battery degradation mechanisms by highlighting nonlinear relationships between key features and SOH estimation results. It reveals critical factors, including electrochemical aging, capacity fading, and voltage instability, that influence battery health. This interpretable and robust framework not only enhances SOH estimation performance but also supports the development of more reliable battery management systems by providing actionable insights into degradation patterns and health indicators.