Battery State Of Health Estimation Research Articles

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

A hybrid kernel extreme learning machine modeling method based on improved dung beetle algorithm optimization for lithium-ion battery state of health estimation

A hybrid kernel extreme learning machine modeling method based on improved dung beetle algorithm optimization for lithium-ion battery state of health estimation

Novel Battery State of Health Estimation and Lifetime Prediction Method Based on the Catboost Model

Novel Battery State of Health Estimation and Lifetime Prediction Method Based on the Catboost Model

A knowledge distillation based cross-modal learning framework for the lithium-ion battery state of health estimation

AbstractThe accurate prediction of a lithium-ion battery’s State of Health is of critical importance for efficient battery health management. Existing data-driven estimation methodologies grapple with issues such as high model complexity and a dearth of guidance from prior knowledge, which impose constraints on their efficacy. This work introduces a novel cross-modal distillation network for battery State of Health estimation, structured around a TransformerEncoder as the teacher network and a Convolutional Neural Network as the student network. Initially, the teacher model is pre-trained offline using State of Health degradation data to learn the degradation patterns. The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network’s output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application.

Battery state of health estimation across electrochemistry and working conditions based on domain adaptation

Accurately assessing the health status of lithium-ion batteries is essential to ensure their safe and efficient application in electric vehicles and energy storage systems. Although various methods have been proposed to achieve battery state of health (SOH) estimation, most of them are only applicable to certain battery types or operating conditions. To address this issue, a novel method is proposed in this study, which leverages data-driven techniques and domain adaptation to cater to different battery electrochemistry and operating conditions. First, the evolution of battery aging is investigated and the incremental capacity sequence with ample aging information is extracted to indicate battery health. Then, the convolutional neural network and bidirectional long-short term memory network are combined to capture the nonlinear relationship between the input sequence and battery SOH. Next, a domain adaptation (DA) based on adversarial training is employed to enhance model adaptability by realizing domain-invariant features extraction. Furthermore, data augmentation is utilized to address data imbalance caused by significant lifespan disparity among different batteries. Finally, datasets with different battery types and aging conditions are used to verify the proposed method. The average estimation error of battery SOH can be controlled within 4.77 %, with over 30 % reduction contributed by the DA.

Lithium-Ion Battery State of Health Estimation by Matrix Profile Empowered Online Knee Onset Identification

Lithium-ion batteries (LiBs) degrade slightly until the knee onset, after which the deterioration accelerates to end of life (EOL). The knee onset, which marks the initiation of the accelerated degradation rate, is crucial in providing an early warning of the battery’s performance changes. However, there is only limited literature on online knee onset identification. Furthermore, it is good to perform such identification using easily collected measurements. To solve these challenges, an online knee onset identification method is developed by exploiting the temporal information within the discharge data. First, the temporal dynamics embedded in the discharge voltage cycles from the slight degradation stage are extracted by the dynamic time warping. Second, the anomaly is exposed by Matrix Profile during subsequence similarity search. The knee onset is detected when the temporal dynamics of the new cycle exceed the control limit and the profile index indicates a change in regime. Finally, the identified knee onset is utilized to categorize the battery into long-range or short-range categories by its strong correlation with the battery’s EOL cycles. With the support of the battery categorization and the training data acquired under the same statistic distribution, the proposed SOH estimation model achieves enhanced estimation results with a root mean squared error as low as 0.22%.

A hybrid intelligent model using the distribution of relaxation time analysis of electrochemical impedance spectroscopy for lithium-ion battery state of health estimation

The state of health (SOH) estimation of lithium-ion batteries is essential to ensure the safety of electric vehicles. Electrochemical impedance spectroscopy (EIS) measurement can provide valuable ageing information and pave the way for the battery SOH estimation. However, the semicircle's overlapping in the EIS during battery degradation introduces large uncertainty of the electrochemical process, causing difficulty in identifying battery ageing features. Therefore, this paper proposes a hybrid intelligent model for the extraction of highly influential health indicators (HIs) and more accurate lithium-ion battery SOH estimation. The distribution of relaxation time is first used to interpret the EIS and distinguish various electrochemical processes at multi-time scales. Second, various physical HIs in different ageing processes are further extracted based on the autoencoder and Spearman rank correlation analysis. Third, an improved cascade feedforward neural network using the genetic algorithm (GA-CF) is proposed to develop an accurate SOH estimation model using the extracted HIs. Finally, the developed model is validated at different operating temperatures of the lithium-ion battery, including 25°C, 35 °C, and 45 °C. The results reveal that the developed GA-CF model with the proposed physical HIs can achieve more accurate and robust SOH estimation, compared to raw features or other popular models.

End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data

Accurately monitoring the State of Health (SOH) of lithium-ion batteries is one of the key technologies critical for the safe and reliable operation of Electric Vehicles (EVs). However, the random charging–discharging behavior of EV users results in differentiated, incomplete, and limited health information in single charging–discharging data, leading to ongoing challenges in SOH estimation. Firstly, an efficient data preprocessing algorithm is designed to automatically handle data slicing, cleaning, alignment, and recombination. Secondly, recognizing the relatively slow change in SOH, multi-neighboring incomplete charging segments are aligned and used as inputs to the SOH estimation model, and then the average capacity of these neighboring charging data is computed as SOH labels. Furthermore, based on the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) structure, an end-to-end SOH estimation model is constructed. This model initially utilizes a multi-channel CNN to fuse health information from multi-neighboring incomplete charging segments and then employs LSTM for SOH prediction. Finally, on a dataset composed of 20 EVs, the proposed SOH estimation method is rigorously validated using K-fold cross-validation. The results demonstrated that the Mean Absolute Error (MAE) is within 2.13%, and the Root Mean Square Error (RMSE) is below 2.74%, highlighting the model’s high estimation accuracy.

Battery State of Health Estimation Using the Sliding Interacting Multiple Model Strategy

Due to their nonlinear behavior and the harsh environments to which batteries are subjected, they require a robust battery monitoring system (BMS) that accurately estimates their state of charge (SOC) and state of health (SOH) to ensure each battery’s safe operation. In this study, the interacting multiple model (IMM) algorithm is implemented in conjunction with an estimation strategy to accurately estimate the SOH and SOC of batteries under cycling conditions. The IMM allows for an adaptive mechanism to account for the decaying battery capacity while the battery is in use. The proposed strategy utilizes the sliding innovation filter (SIF) to estimate the SOC while the IMM serves as a process to update the parameter values of the battery model as the battery ages. The performance of the proposed strategy was tested using the well-known B005 battery dataset available at NASA’s Prognostic Data Repository. This strategy partitions the experimental dataset to build a database of different SOH models of the battery, allowing the IMM to select the most accurate representation of the battery’s current conditions while in operation, thus determining the current SOH of the battery. Future work in the area of battery retirement is also considered.

A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction

Battery life prediction is of great significance to the safe operation, and reduces the maintenance costs. This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery. By feature extraction, eight features are obtained to fed into the life prediction model. The hybrid framework combines variational mode decomposition, the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward, uneven distribution of high-dimensional feature space and the local escape ability, respectively. Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm. The algorithm can improve the local escape ability and convergence performance and find the global optimum. The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance. Compared with other algorithms, the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%. With the advance of the start point, the RUL prediction accuracy of the proposed algorithm does not change much.

A neural-driven stochastic degradation model for state-of-health estimation of lithium-ion battery

Due to usage for a long time, lithium-ion battery degradation issues may endanger security operations for service equipment. As batteries usually operate under uncertain usage profiles and experience random aging processes, accurately estimating the battery state of health (SOH) can be challenging. Most existing SOH estimation methods are either empirical knowledge-based with complicated parameters or serve as a black box neural network-based without stochastic nature. To address this limitation, a neural-driven stochastic degradation model for battery health estimation is proposed to ensure the security and reliability of the operation. Firstly, the parameters of a stochastic process are estimated by martingale methodology to describe battery degradation characteristics. Then the optimized sequence-to-sequence (Seq2Seq) neural network is utilized to develop a mapping model in order to update drift coefficient online. Concurrently, the martingale estimating function is used to obtain the diffusion coefficient function. Finally, the transition probability density function of SOH estimation is derived. Comparative experiments are carried out on different battery datasets to show the effectiveness of the proposed model against traditional Wiener process-based models and existing methods in SOH estimation. The model proposed provides an effective solution for battery SOH estimation of energy storage equipment.

Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

Accurately battery state of health (SOH) estimation in electric vehicles (EVs) is crucial for optimal performance and safety. This article presents an in‐depth investigation into the utilization of an optimized decision tree (DT) model for precise SOH estimation. Two aging features (AFs) are extracted through incremental capacity analysis from the partial charging process, and their efficacy is thoroughly validated on both battery cells and packs. To capture the relationship between AFs and capacity degradation, a robust DT model is established. Furthermore, Bayesian optimization is employed to optimize the model hyperparameters, enhancing the learning and generalization capabilities of the DT model. To validate the optimized DT model, a battery management system (BMS) is developed using a hybrid programming approach with LabVIEW and MATLAB, enabling online SOH estimation. The BMS undergoes rigorous evaluation using publicly available aging datasets of battery cells, as well as private datasets of battery packs. The results demonstrate that the optimized DT model outperforms traditional DT models, back propagation neural networks, and support vector machine approaches in accurately estimating battery SOH across diverse datasets. This research contributes to the advancement of BMS and holds profound implications for the efficient utilization of batteries in EVs.

A recurrent deep neural network for predicting the state of health of lithium‐ion batteries

AbstractBattery State of Health (SoH) estimation is a critical task in the field of battery management, as it provides information about remaining capacity and health of a battery. Various machine learning algorithms, including neural networks, decision trees, support vector machines, and random forests, have been utilized for battery SoH estimation. These models can be trained using different features, such as voltage, current, temperature, impedance, and their combinations. However, the diversity of data is a decisive factor that affects precision of battery SoH estimation using machine learning. In this research, the application of feedforward neural networks (FNNs) and recurrent neural networks (RNNs) is explored for the purpose of accurately estimating the SoH of batteries. These approaches are chosen due to the inherent benefits of FNNs and RNNs in capturing the long‐term dependencies present in sequential data. The battery SoH estimations are evaluated using the single and multichannel input: voltage, current, voltage‐current, voltage‐temperature, and voltage‐current‐temperature. The experimental findings reveal that the proposed RNN model, specifically the RNN with 20 neurons (RNN20) variant, exhibits an enhanced accuracy in predicting the SoH of batteries. For instance, when utilizing voltage and current as inputs, the RNN20 model demonstrated superior performance, achieving an mean absolute error (MAE) of 0.010157 for voltage and 0.010367 for current, outperforming the FNN with 10 neurons (FNN10) model, which yielded an MAE of 0.031635 and 0.065 for voltage and current, respectively. Furthermore, when employing diverse input combinations such as voltage‐current, voltage‐temperature, and voltage‐current‐temperature, the RNN20 model consistently outperformed its counterparts, exhibiting the lowest mean squared error and mean absolute percentage error across all metrics. These results underscore the RNN20 model's robustness and aptitude in accurately predicting battery SoH, affirming the merits of employing RNNs in battery management systems.

Battery state of health estimation using variable projection algorithm based on truncated variable order fractional gradient descent

Battery state of health estimation using variable projection algorithm based on truncated variable order fractional gradient descent

State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural network

Accurate estimation of State of Health (SOH) is crucial to ensure optimal performance and safe operation of lithium-ion battery. This paper proposes a Stacking ensemble learning paradigm for SOH estimation. The Stacking ensemble learning increases adaptability to different features by using base learners with different structures, reducing the risk of overfitting. The model utilizes random vector functional link (RVFL) and active state tracking long-short-term memory network (AST-LSTM) as base learners, where AST-LSTM actively tracks long-term information of lithium-ion battery, and RVFL acts as the meta-learner for stacking. The random vector functional link network helps to avoid the problem of gradient vanishing that is commonly encountered in neural networks due to the gradient descent principle. To further improve estimation accuracy, Singer initialization method and dimension learning method are employed to enhance the Heap-based optimization (HBO) algorithm. In this study, the IHBO algorithm is used to optimize the hyperparameters of the model. Comparing with other methods, the hybrid model proposed in this paper demonstrates superior estimation performance under different operating conditions: at a temperature of 24 °C with a discharge current of 1 A, at a temperature of 4 °C with a discharge current of 1 A, and at a temperature of 4 °C with a discharge current of 2 A. The highest RMSE of the proposed method for the three working conditions are 0.006, 0.01, and 0.017, respectively. Therefore, the proposed Stacking ensemble learning is feasible for SOH estimation of lithium-ion battery and can better adapt to lithium-ion battery data under different operating conditions.

Lithium-Ion Batteries SOH Estimation With Multimodal Multilinear Feature Fusion

As a key status for battery energy storage systems, state of health (SOH) can provide the fundamental information for lifespan management of the battery pack in electric vehicles (EV). Traditionally, features are extracted from various signals to establish a powerful data-driven model for the lithium-ion battery (Li-ion) SOH estimation. One issue left is how to utilize the existing features from diverse modalities of measurement signals for a superior battery aging information capture. The available options are selecting the features by analyzing their correlations with SOH. This paper aims to investigate the intercorrelations between various features through the multimodal multilinear fusion mechanism, which enables to utilize the multimodal multilinear features (MMF) and their interaction characteristics. A high-order polynomial module is designed to fuse the MMF from various sources. To improve the efficiency and performance of the SOH estimator, a 2D convolutional neural network (CNN) network is chosen to use the proposed MMF. The performance of the proposed method is validated on two independent datasets, which obtains the lowest mean absolute error (MAE) of 0.37% and the lowest root mean square error (RMSE) is 0.45%.

Battery State of Health estimation using GA-BP neural network on data feature mining

Battery State of Health estimation using GA-BP neural network on data feature mining

State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree

To ensure the stable, reliable, and safe operation of lithium-ion batteries, the state of health (SOH) serves as a crucial basis for battery safety monitoring and operational maintenance. However, the complex working environment of batteries leads to the presence of a significant amount of noise data, abnormal data, and data discontinuities, posing challenges for battery feature extraction and SOH estimation. To address this issue, this paper proposes a two-stage transformation-based feature extraction method combined with the gradient boosting decision tree (GBDT) algorithm. The two-stage transformation feature extraction method utilizes singular spectrum analysis (SSA) and fast Fourier transform (FFT) to extract time-domain and frequency-domain health features from the battery data, respectively. The GBDT ensemble learning algorithm is employed to estimate the SOH of lithium-ion batteries, leveraging the capability to effectively uncover and learn relevant features by combining multiple weak learners into a strong model. Furthermore, correlation analysis and ablation experiments are conducted to validate the effectiveness of the two-stage feature extraction method. Additionally, four sets of comparative experiments are designed to demonstrate the superior performance of the GBDT method compared to four other machine learning methods. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) of SOH estimation using the proposed method do not exceed 3 % on the NASA and CALCE datasets.

Li-Ion Battery State of Health Estimation Based on Short Random Charging Segment and Improved Long Short-Term Memory

Lithium-ion batteries have been used in a wide range of applications, including electrochemical energy storage and electrical transportation. In order to ensure safe and stable battery operation, the State of Health (SOH) needs to be accurately estimated. In recent years, model-based and data-driven methods have been widely used for SOH estimation, but due to the uncertainty of battery charging conditions in practice, it is difficult to obtain a fixed local segment. In this paper, the charging curve is first divided into several equal voltage difference segments based on charging segment voltage difference ΔV in order to solve the random charging segment problem. Time interval of equal charge voltage difference of the voltage curve, coefficient of variation and euclidean distance of the charging capacity difference curve are extracted as health features. The improved flow direction algorithmlong short term memory-based SOH assessment method is proposed and verified by the Oxford battery degradation dataset and experimental battery degradation dataset with a maximum error of 0.6%.

A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU

The state-of-health (SOH) of lithium-ion batteries has a significant impact on the safety and reliability of electric vehicles. However, existing research on battery SOH estimation mainly relies on laboratory battery data and does not take into account the multi-faceted nature of battery aging, which limits the comprehensive and effective evaluation and prediction of battery health in real-world applications. To address these limitations, this study utilizes real electric vehicle operational data to propose a comprehensive battery health evaluation indicator and a deep learning predictive model. In this study, the battery capacity, ohmic resistance, and maximum output power were initially extracted as individual health indicators from actual vehicle operation data. Subsequently, a methodology that combines the improved criteria importance through inter-criteria correlation (CRITIC) weighting method with the grey relational analysis (GRA) method is employed to construct the comprehensive battery health evaluation indicator. Finally, a prediction model based on the attention mechanism and the bidirectional gated recurrent unit (Att-BiGRU) is proposed to forecast the comprehensive evaluation indicator. Experimental results using real-world vehicle data demonstrate that the proposed comprehensive health indicator can provide a thorough representation of the battery health state. Furthermore, the Att-BiGRU prediction model outperforms traditional machine learning models in terms of prediction accuracy.