Battery State Of Health Estimation Research Articles

A lithium-ion battery SOH estimation method based on temporal pattern attention mechanism and CNN-LSTM model

Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for ensuring the availability and efficiency of the energy storage systems based on lithium-ion batteries. This paper proposes a method based on a temporal pattern attention (TPA) mechanism and a CNN-LSTM model to improve the SOH estimation accuracy. Firstly, the health factors (HFs) related to capacity degradation are selected based on the charge–discharge curves of lithium-ion batteries, and the local outliers in the health factor data are detected through the local outlier factor (LOF) method. The Lagrange interpolation is then applied to correct these outliers to ensure the continuity of the health factor data. Secondly, the effectiveness of the health factor is evaluated by using the Pearson and Spearman correlation analyses, with the valid health factor forming the HFs dataset. Thirdly, the TPA mechanism is integrated into the CNN-LSTM model to form a CNN-LSTM-TPA model to enhance its ability to capture key information. The whale optimization algorithm (WOA) is employed to optimize the model’s hyperparameters. Finally, the proposed method is tested on the NASA and CALCE lithium-ion battery datasets. The experimental results show that on the NASA dataset, the proposed method improves the SOH estimation accuracy by approximately 26.39% to 46.05% compared to the LSTM method; on the CALCE dataset, the accuracy improves by approximately 56.05% to 73.32% compared to the LSTM method, respectively. These experimental results indicate that the proposed method achieves higher accuracy.

Robust lithium-ion battery state of health estimation based on recursive feature elimination-deep Bidirectional long short-term memory model using partial charging data

Accurate perception of the state of health (SOH) of lithium-ion batteries is crucial for their safety and reliable operation. To meet this demand, a recursive feature elimination-deep bidirectional long short-term memory (RFE-DBiLSTM) model suitable for partial charging data is proposed to effectively estimate the SOH of lithium-ion batteries. In this study, the recursive feature elimination (RFE) method is used to screen multiple charging features for obtaining the key features that best represent the SOH under two scenarios with different charging segment lengths. Due to the robust noise-filtering capability and strong ability to capture complex and multi-level temporal dependencies, the deep bidirectional long short-term memory (DBiLSTM) model is used for time series data training, verification, and testing during aging. Experimental results show that compared with benchmark time series models such as long short-term memory (LSTM) and gated recurrent unit (GRU), the proposed method significantly reduces the estimated mean absolute error (MAE) and root mean square error (RMSE) on diverse batteries in the above scenarios. In the scenario for missing partial constant current (CC) charging data, the MAE and RMSE of B0005 cell are 0.0062 and 0.0094, the MAE and RMSE of B0006 cell are 0.0294 and 0.0314, the MAE and RMSE of CS2_36 cell are 0.0510 and 0.0601, the MAE and RMSE of B0029 cell are 0.0057 and 0.0072, and the MAE and RMSE of B0030 cell are 0.0088 and 0.0102. This research innovatively combines the RFE method with the DBiLSTM model to improve the accuracy and robustness of SOH estimation.

Improved lithium battery state of health estimation and enhanced adaptive capacity of innovative kernel extreme learning machine optimized by multi-strategy dung beetle algorithm

Improved lithium battery state of health estimation and enhanced adaptive capacity of innovative kernel extreme learning machine optimized by multi-strategy dung beetle algorithm

State of Health Estimation for Lithium-Ion Batteries Based on Fusion Health Features and Adaboost-GWO-BP Model

To accurately predict the state of health (SOH) of lithium-ion batteries and improve the safety and reliability of battery management systems, a new SOH estimation method based on fusion health features (HFs) and adaptive boosting integrated grey wolf optimizer to optimize back propagation neural network (Adaboost-GWO-BP) is proposed. First, five kinds of multi-type HFs were extracted from the battery charging process, and the correlation between the proposed HFs and SOH was verified by Pearson and Spearman correlation coefficients. Then, the indirect health feature (IHF) was obtained by multidimensional scaling dimensionality reduction to reduce data redundancy and improve the correlation between HFs and SOH. The GWO-BP model was then used to establish the nonlinear mapping relationship between IHF and SOH. In order to overcome the problem of low accuracy of battery SOH estimation in a single model, the Adaboost algorithm in ensemble learning is introduced to enhance the accuracy of the model estimation. Finally, the proposed method is verified by NASA dataset, and compared with other models. In the comparative experiments, mean absolute error and root mean square error of the proposed method for SOH estimation is less than 0.81% and 1.26%, which has higher accuracy compared to other models.

Feature selection strategy optimization for lithium-ion battery state of health estimation under impedance uncertainties

Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles. Electrochemical impedance spectroscopy (EIS) offers valuable insights into battery degradation analysis and modeling. However, previous studies have not adequately addressed the impedance uncertainties, particularly during battery operating conditions, which can substantially impact the robustness and accuracy of state of health (SOH) estimation. Motivated by this, this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis. By utilizing metrics such as impedance residuals and correlation coefficients, the proposed method effectively filters out invalid and insignificant impedance data, thereby enhancing the reliability of the input features. Subsequently, the extreme gradient boosting (XGBoost) modeling framework is constructed for estimating the battery degradation trajectories. The XGBoost model incorporates a diverse range of hyperparameters, optimized by a genetic algorithm to improve its adaptability and generalization performance. Experimental validation confirms the effectiveness of the proposed feature optimization scheme, demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.

Energy storage battery state of health estimation based on singular value decomposition for noise reduction and improved LSTM neural network

Accurate estimation of the State of Health (SOH) of batteries is important for intelligent battery management in energy storage systems. To solve the problems of poor quality of data features as well as the difficulty of model parameter adjustment, this study proposes a method for estimating the SOH of lithium batteries based on denoising battery health features and an improved Long Short-Term Memory (LSTM) neural network. First, in this study, three health features related to SOH decrease were selected from the battery charge/discharge data, and the singular value decomposition technique was applied to the noise reduction of the features to improve their correlation with the SOH. Then, the whale optimization algorithm is improved using cubic chaotic mapping to enhance its global optimization-seeking capability. Then, the Improved Whale Optimization Algorithm (IWOA) is used to optimize the model parameters of LSTM, and the IWOA-LSTM model is applied to the battery SOH estimation. Finally, the model proposed in this research is validated against the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. The experimental results show that the prediction error of battery SOH by the method proposed in this study is less than 0.96%, and the prediction error is reduced by 49.42% compared to its baseline model. The method presented in the article achieves accurate estimation of the SOH, providing a reference for practical engineering applications.

Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve

Data-driven methods have gained extensive attention in estimating the state of health (SOH) of lithium-ion batteries. Accurate SOH estimation requires degradation-relevant features and alignment of statistical distributions between training and testing datasets. However, current research often overlooks these needs and relies on arbitrary voltage segment selection. To address these challenges, this paper introduces an innovative approach leveraging spatio-temporal degradation dynamics via graph convolutional networks (GCNs). Our method systematically selects discharge voltage segments using the Matrix Profile anomaly detection algorithm, eliminating the need for manual selection and preventing information loss. These selected segments form a fundamental structure integrated into the GCN-based SOH estimation model, capturing inter-cycle dynamics and mitigating statistical distribution incongruities between offline training and online testing data. Validation with a widely accepted open-source dataset demonstrates that our method achieves precise SOH estimation, with a root mean squared error of less than 1%.

Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy

The State of Health (SOH) of lithium-ion batteries is crucial for maintaining their safety and reliability. Electrochemical impedance spectroscopy (EIS) provides extensive information about the battery state, but the data across high-, medium-, and low-frequency ranges are not independent. Practical challenges in obtaining full-frequency EIS data include long measurement times, low accuracy due to noise, and high costs. This paper proposes an SOH estimation method based on offline EIS and domain-adversarial neural network (DaNN) transfer. Initially, we use a feature search strategy on experimental EIS data to identify the optimal impedance feature subset through the root mean square error (RMSE) and comprehensive indicators. These features are then input into the DaNN for SOH estimation using transfer algorithms. Finally, based on feature correlation analysis, weights are allocated using a Gaussian process regression model for a secondary prediction, optimizing the SOH results. Experiments on laboratory calendar aging and publicly available cycling aging datasets demonstrate the method's effectiveness, achieving an RMSE of <1 %. The proposed method significantly improves SOH estimation accuracy and efficiency, addressing the limitations of traditional methods and reducing dependency on extensive measurement data. This advancement enhances the safety, reliability, and cost-effectiveness of battery applications.

A label-free battery state of health estimation method based on adversarial multi-domain adaptation network and relaxation voltage

The state of health (SOH) estimation of lithium-ion batteries is crucial for the operational reliability and safety of electric vehicles. However, traditional data-driven methods face problems of label shortage and domain discrepancy caused by diverse battery types and operating conditions. This paper proposes a label-free SOH estimation method based on adversarial multi-domain adaptation technique and relaxation voltage (RV). Firstly, the raw RV and integral voltage are proposed to construct the two-dimensional input sequence to ensure high adaptability across various target domains. Secondly, a two-dimensional convolutional neural network integrated with fully connected layers is developed to establish the feature extractor and SOH estimator, which paves the way for effective knowledge transfer. Furthermore, an adversarial multi-domain adaptation method with a domain discriminator is developed to optimize the extraction of domain-invariant features and enable accurate SOH estimation without SOH labels. Datasets of 50 batteries with different temperatures and materials are used for the validation. The proposed method shows outstanding performance, achieving the average RMSE and MAE of 0.0274 and 0.0240 across various working temperatures, and 0.0177 and 0.0148 across different battery types. It suggests that the proposed method can achieve robust adaptive battery SOH estimation across multiple target domains without using SOH labels.

Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging

Accurately estimating the state-of-health (SOH) of lithium-ion batteries is crucial for efficient, reliable, and safe use. However, the degradation of these batteries involves complex and intricate failure mechanisms that cannot be fully captured by a single model. To tackle this challenge, we propose an SOH estimation method based on Bayesian Model Averaging (BMA), which effectively accounts for both parameter and model uncertainties by combining estimations from different model implementations. It provides both point-value estimates and the probability distribution estimates, delivering results in a fraction of a second. Evaluated on three open-source datasets, the maximum absolute error of SOH estimation is within 0.03, and the Continuous Ranked Probability Score is within 0.015. Compared to optimal individual models, the proposed BMA method reduces the prediction error of point estimates by half to two-thirds. Additionally, the prediction error for probability estimation decreases by an order of magnitude. Moreover, the comparison studies with respect to the Gaussian Process Regression model and the Quantile Regression Forests model demonstrate the applicability and superiority of proposed method. These results highlight the potential of the BMA method to advance battery SOH estimation and facilitate reliable battery management.

A novel sequential estimation framework for battery state of health and remaining useful life based on sparse and limited data

Battery State of Health (SOH) estimation and Remaining Useful Life (RUL) prediction are significant for battery management systems in both electric vehicles and energy storage systems. Various battery Health Indicators (HIs) and abundant health information are generally utilized for SOH and RUL estimation. However, HIs and health information may not be available for various reasons such as sensor installation limitations, computational burden and communication delay of the system. To overcome the issue of sparse and limited data acquisition in real applications, this study proposed a novel battery SOH and RUL sequential estimation framework only utilizing sparse segments of time series voltage data. The Extreme Learning Machine (ELM) and Long Short Term Memory (LSTM) network are applied for SOH estimation and RUL prediction respectively. Locally Weighted Scatterplot Smoothing (LOWESS) is used for both data smoothing and sparse data reconstruction. Three common battery datasets are fully investigated for validation. The experiment results show well estimation accuracies in both battery SOH and RUL, demonstrating the effectiveness of the framework in limited data prediction. Several popular SOH and RUL estimation algorithms and two state-of-the-art works are compared to further elaborate the superiority of the proposed framework.

Enhanced Lithium-Ion Battery SOH Estimation Using Bayesian-Optimized CNN Deep Learning Approach

The accurate health status evaluation of lithium-ion batteries is crucial for preemptive identification of potential battery failures and averting hazardous incidents, given its essential role in indicating the extent of battery degradation. The challenge in determining the State of Health (SOH) arises from the absence of a precise and standardized definition, as well as the difficulty in measuring essential input variables. Therefore, this paper utilizes current and voltage data during the charge and discharge process as direct inputs for SOH estimation and proposes a deep learning-based lithium-ion battery SOH estimation approach. Specifically, it leverages Bayesian optimized Convolutional Neural Network (CNN) within a data-driven framework. Experimental results demonstrate that the proposed deep learning method achieves a Mean Absolute Error (MAE) of 1% and a Maximum Error (MAX) below 4% in estimation accuracy, highlighting its enhanced precision and robustness.

Estimating the Health State of Lithium-Ion Batteries Using an Adaptive Gated Sequence Network and Hierarchical Feature Construction

State of health (SOH) estimation plays a vital role in ensuring the safe and stable operation of lithium-ion battery management systems (BMSs). Data-driven methods are widely used to estimate SOH; however, existing methods often suffer from fixed or excessively high feature dimensions, impacting the model’s adjustability and applicability. This study first proposed a layered knee point strategy based on the charging voltage curve, which reduced the complexity of feature extraction. Then, a new hybrid framework called the adaptive gated sequence network (AGSN) model was proposed. This model integrated independently recurrent neural network (IndRNN) layers, active state tracking long short-term memory (AST-LSTM) layers, and adaptive gating mechanism (AGM) layers. By integrating a multi-layered structure and an adaptive gating mechanism, the SOH prediction performance was significantly improved. Finally, batteries under different operating conditions were tested using the NASA battery dataset. The results show that the AGSN model demonstrated higher accuracy and robustness in battery SOH estimation, with estimation errors consistently within 1%.

Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability

Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are widely used for SOH estimation but still suffer from the difficulty of balancing speed, accuracy, and adaptability. Therefore, this study constructs the dung beetle optimization algorithm to optimize the kernel extreme learning machine model. This paper addresses the issues of long iteration time and mismatches in kernel function mapping in data-driven models. To improve the model's generality, an adaptive learning kernel function is designed to complement the polynomial kernel function and form a joint function. This joint function is then introduced into a single implicit-layer extreme learning machine, which achieves fast speed and strong adaptive capability. To enhance the algorithmic parameter search capability, the optimal Latin hypercube idea, and the Osprey algorithm's global exploration strategy are introduced, which effectively improves the algorithm's global search capability. Additionally, it successfully regulated the positional update through the design of the logarithmic weighting factor, which improved the local search and convergence capabilities of the algorithm. The experiment validates the effectiveness and rationality of the proposed model for advancing battery management system applications.

Battery state of health estimation based on voltage relaxation and an improved online sequential extreme learning machine

Battery state of health estimation based on voltage relaxation and an improved online sequential extreme learning machine

Industrial Battery State-of-Health Estimation with Incomplete Limited Data Towards Second-Life Applications

Battery State of Health (SOH) estimation is vital across applications ranging from portable electronics to electric vehicles, particularly in second-life applications where accurate prediction becomes complex due to varying degradation levels. This paper introduces a novel SOH estimation model to address the lack of labeled data, employing Domain Adversarial Neural Networks (DANN) combined with 1-dimensional Convolutional Neural Networks (CNN). The proposed method allows for effective transfer of knowledge between diverse battery conditions, enhancing adaptability and efficiency by utilizing both source and target datasets. Experimental results demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 1.68% and a Root Mean Squared Error (RMSE) of 2.50%, with minimal data. Specifically, the model requires only one cell of unlabeled data from the second-life target domain, utilizing only the dQ/dV curve for estimation. Proposed model sets a new standard in second-life battery health monitoring and management by effectively leveraging a minimal amount of data for training, this approach offers a robust solution for accurate SOH estimation, particularly in scenarios with limited access to labeled data. Conflict of Interest Statement The authors declare no conflicts of interest.

Retired battery state of health estimation based on multi-frequency decomposition of charging temperature and GRU–transformer integration model

Retired battery state of health estimation based on multi-frequency decomposition of charging temperature and GRU–transformer integration model

Lithium battery state of health estimation using real-world vehicle data and an interpretable hybrid framework

Accurate point estimation and uncertainty estimation of the state of health (SOH) of battery systems are crucial for alleviating user range anxiety and preventing battery safety incidents. On top of approximately 28 million real-world electric vehicle operation data samples, this paper embeds the categorical boosting algorithm as the base learner of the natural gradient boosting, proposing a novel interpretable N-CatBoost hybrid framework to achieve precise point estimation and uncertainty estimation of the battery SOH. Deep charging segments are selected to calculate the initial capacity, and the nonlinear decay trend of the capacity is derived through the Savitzky-Golay filter. Based on the influencing factors of capacity degradation, health features characterizing battery aging are extracted from electric vehicle data as model inputs. The model's hyperparameters are optimized using the particle swarm optimization algorithm, and it is compared with seven other popular machine learning algorithms. The results indicate that the proposed N-CatBoost model achieves the highest estimation accuracy, with mean absolute percentage error and root mean square error of 0.817 % and 1.156 Ah, respectively. In addition, the Shapley additive explanation method is utilized to make the model interpretable, providing full transparency of all predicted values. More importantly, the developed N-CatBoost model can achieve uncertainty estimation of its predictions, with 100 % of the actual capacity values successfully falling within the model's 99 % prediction interval. Therefore, the proposed N-CatBoost model is a reliable and effective method with great potential for deployment on the cloud side for the SOH estimation of batteries in large-scale vehicles.

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.

Robust Online Estimation of State of Health for Lithium-Ion Batteries Based on Capacities under Dynamical Operation Conditions

Lithium-ion batteries, as the main energy storage component of electric vehicles (EVs), play a crucial role in ensuring the safe and reliable operation of the battery systems through monitoring their state of health (SOH). However, temperature variations and battery aging have significant impacts on the internal parameters of lithium-ion batteries, and these changes directly correlate with the accuracy of battery SOH estimation. To address these issues, this paper proposes an estimation method for lithium-ion battery SOH that considers the impact of temperature. The method begins with reconstructing a second-order hybrid equivalent circuit model for lithium-ion batteries, through which an adaptive update rate for battery model parameters is designed. On this basis, a nonlinear observer for battery states is introduced by integrating filters to estimate SOH. The proposed method considers the impact of capacity in the design of the parameter adaptive update rate, enabling the capacity to be dynamically adjusted based on the actual state of the batteries. This reduces the cumulative error in the SOC observer and improves the modeling accuracy of battery models. Experimental results demonstrate that the method proposed in this paper exhibits exceptional performance in SOH estimation under different temperature conditions. The mean absolute error for SOH estimation does not exceed 0.5%, and the root mean square error does not exceed 0.2%. This method can significantly improve the estimation accuracy of SOH, providing a more efficient and accurate solution for battery management systems in EVs.