Battery Capacity Prediction Research Articles

Multi-scale self-attention feature decoupling transfer network-based cross-domain capacity prediction of lithium-ion batteries

Lithium-ion battery capacity prediction is paramount for improving the safety and reliability of energy storage devices. However, accurately predicting capacity continues to pose a challenge, stemming from the diverse range of operational conditions and the lack of capacity labels under unpredictable scenarios. To tackle this problem, a multi-scale self-attention feature decoupling transfer network is proposed for predicting the capacity of lithium-ion batteries across diverse operational conditions. More specifically, multi-scale multi-head self-attentive encoders are designed to adaptively extract multi-scale domain-invariant features by using a series of loss function constraints. Then, transfer learning integrated with domain-invariant features is applied to migrate the knowledge of the source domain to the target domain for cross-domain capacity prediction. Finally, the performance of our methods is authenticated through utilization of two battery dataset, each including three different charging and discharging scenarios. Experimental results indicate that the proposed method effectively extracts domain-invariant features from charging data characterized by varying distributions, thereby mitigating the effects of distributional differences. Compared with the state-of-the-art methods, the proposed method shows excellent accuracy and bustling ability in two datasets.

Personalized Federated Lithium-Ion Battery Capacity Prediction via Cluster and Fusion Modules

Personalized Federated Lithium-Ion Battery Capacity Prediction via Cluster and Fusion Modules

A hybrid battery degradation model combining arrhenius equation and neural network for capacity prediction under time-varying operating conditions

Capacity degradation modeling and remaining useful life (RUL) prediction play a pivotal role in enhancing the safety of battery energy storage systems. Lithium-ion batteries utilized in the systems are subjected to intricate and variable operating conditions, including temperature, charging/discharging rates and depth, which result in time-varying degradation rates. However, most existing models for battery capacity prediction have the limitation of ignoring the quantitative effects of time-varying operating conditions on degradation. Therefore, a hybrid battery capacity degradation model combining Arrhenius equation and lightweight Transformer is constructed. The effects of operating conditions on capacity degradation rates are introduced by an improved Arrhenius degradation equation. The coordinate ascent method embedded with Newton descent is used for estimating the model parameters. Then, the unknown mapping relationships between the degradation statistical features extracted from monitoring data and the degradation factors are captured by the light-weight Transformer. Finally, a novel framework consisting of future capacity and RUL predicting is constructed based on sequential importance resampling (SIR). For the purpose of verification, the cyclic charge-discharge experiments of eight battery cells under two distinct operating profiles are designed and conducted. Compared with other models, the proposed model achieves the best prognostics performance on the tested cells.

Initial Engineering Studies of Battery Capacity Prediction Power of "Electric Ship of FTK UNSADA" Container Concept from Jakarta to Ibu Kota Nusantara

Initial Engineering Studies of Battery Capacity Prediction Power of "Electric Ship of FTK UNSADA" Container Concept from Jakarta to Ibu Kota Nusantara

Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP

Over the last 20 years, lithium-ion batteries have become widely used in many fields due to their advantages such as ease of use and low cost. However, there are concerns about the lifetime and reliability of these batteries. These concerns can be addressed by obtaining accurate capacity and health information. This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during charge and discharge cycles. To enhance prediction accuracy, the Pearson correlation coefficient between these features and battery capacities was analyzed and eliminations were made for some batteries. Using a genetic algorithm (GA), the parameter optimization of Convolutional Neural Network (CNN), Backpropagation (BP), and Recurrent Neural Network (RNN) algorithms was performed. The parameters that provide the best performance were determined in a shorter time using GA, which includes natural selection and genetic processes instead of a trial-and-error method. The study employed five metrics—Mean Square Error (MSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), and Squared Correlation (R2)—to evaluate prediction accuracy. Predictions based on NASA experimental data were compared with the existing literature, demonstrating superior accuracy. Using 100 training data, 68 data predictions were made with a Root Mean Square Error (RMSE) of 0.1176%. This error rate represents an accuracy level 2.5 times higher than similarly accurate studies in the literature.

Correction: Chu et al. Temporal Attention Mechanism Based Indirect Battery Capacity Prediction Combined with Health Feature Extraction. Electronics 2023, 12, 4951

There was an error in the original publication [...]

TensorRT Powered Model for Ultra-Fast Li-Ion Battery Capacity Prediction on Embedded Devices

TensorRT Powered Model for Ultra-Fast Li-Ion Battery Capacity Prediction on Embedded Devices

Lithium-Ion Battery Capacity Prediction Method Based on Improved Extreme Learning Machine

Abstract Currently, research and applications in the field of capacity prediction mainly focus on the use and recycling of batteries, encompassing topics such as SOH estimation, RUL prediction, and echelon use. However, there is scant research and application based on capacity prediction in the battery manufacturing process. Measuring capacity in the grading process is an important step in battery production. The traditional capacity acquisition method consumes considerable time and energy. To address the above issues, this study establishes an improved extreme learning machine (ELM) model for predicting battery capacity in the manufacturing process, which can save approximately 45% of energy and time in the grading process. The study involves the extraction of features from the battery charge–discharge curve that can reflect battery capacity performance and subsequent calculation of the grey correlation between these features and capacity. The feature set comprises features with a high correlation with capacity, which are used as inputs for the ELM model. Kernel functions are used to adjust the ELM model, and Bayesian optimization methods are employed to automatically optimize the hyperparameters to improve the capacity prediction performance of the model. The study uses lithium-ion battery data from an actual manufacturing process to test the predictive effect of the model. The mean absolute percentage error of the capacity prediction results is less than 0.2%, and the root-mean-square error is less than 0.3 Ah.

Data-driven autoencoder neural network for onboard BMS Lithium-ion battery degradation prediction

An autoencoder based neural network architecture, CD-Net, is proposed to predict Lithium-ion battery capacity degradation as a function of operation time as part of a battery management system. CD-Net's generalization performance on various LIB cell chemistry is tested. The incorporation of cell chemistry in CD-Net leads to an improvement in the overall battery capacity prediction accuracy of >2 % for LiNiMnCoO2 cells, >5 % for LiNiCoAlO2 cells, and >12 % for LiFePO4 cells when compared to the similar ML models that do not incorporate cell chemistry information. A comparison of onboard battery health prediction using CD-Net against support vector regression, Bayesian regression, and Gaussian process regression-based approaches shows that CD-Net has higher computational efficiency with <2 % of relative remaining useful life (RUL) prediction error in a no-cell chemistry information setting. In summary, our work presents a chemistry-independent neural network model tailored specifically for onboard BMS applications, showcasing notable predictive capabilities in the context of Lithium-ion battery health assessment.

Capacity prediction of Lithium-ion batteries based on adaptive sliding window pooling extreme learning machine algorithm

Abstract Considering the safety and reliability, it is especially important to accurately predict the capacity decline trend of lithium-ion batteries. In this paper, a simple and easy-to-operate singular value decomposition technique is used to extract the health indicators (HIs) that are correlated with the capacity from the measurable parameters of battery, and then the HIs that have a high Pearson correlation coefficient with the capacity are selected for predicting the battery capacity. Aiming at the problems of low prediction accuracy and random dispersion of traditional extreme learning machine (ELM), this paper proposes an adaptive sliding window pooling extreme learning machine (ASW-PELM) algorithm. The algorithm first adaptively adjusts the window length according to the fluctuation of local data, and then dynamically traverses the data with the sliding window for data enhancement, and this adaptive sliding window mechanism provides effective data for the model prediction stage. Then it combines the pooling operation and the ELM to replace the random factor between the input layer and the hidden layer, which effectively solves the problem of random dispersion in the original learning model. The results of lithium battery capacity prediction under two sets of different experimental conditions show that the method has the highest prediction accuracy compared with other generalized algorithms.

A Two-State-Based Hybrid Model for Degradation and Capacity Prediction of Lithium-Ion Batteries with Capacity Recovery

The accurate prediction of Li-ion battery capacity is important because it ensures mission and personnel safety during operations. However, the phenomenon of capacity recovery (CR) may impede the progress of improving battery capacity prediction performance. Therefore, in this study, we focus on the phenomenon of capacity recovery during battery degradation and propose a hybrid lithium-ion battery capacity prediction framework based on two states. First, to improve the density of capacity-related information, the simultaneous Markov blanket discovery algorithm (STMB) is used to screen the causal features of capacity from the initial feature set. Then, the life-long cycle sequence of batteries is partitioned into global degradation regions and recovery regions, as part of the proposed prediction framework. The prediction branch for the global degradation region is implemented through a long short-term memory network (LSTM) and the other prediction branch for the recovery region is implemented through Gaussian process regression (GPR). A support vector machine (SVM) model is applied to identify recovery points to switch the branch of the prediction framework. The prediction results are integrated to obtain the final prediction results. Experimental studies based on NASA’s lithium battery aging data highlight the trustworthy capacity prediction ability of the proposed method considering the capacity recovery phenomenon. In contrast to the comparative methods, the mean absolute error and the root mean square error are reduced by up to 0.0013 Ah and 0.0043 Ah, which confirms the validity of the proposed method.

Temporal Attention Mechanism Based Indirect Battery Capacity Prediction Combined with Health Feature Extraction

The burgeoning utilization of lithium-ion batteries within electric vehicles and renewable energy storage systems has catapulted the capacity prediction of such batteries to a pivotal research frontier in the energy storage domain. Precise capacity prognostication is instrumental not merely in safeguarding battery operation but also in prolonging its operational lifespan. The indirect battery capacity prediction model presented in this study is based on a time-attention mechanism and aims to reveal hidden patterns in battery data and improve the accuracy of battery capacity prediction, thereby facilitating the development of a robust time series prediction model. Initially, pivotal health indicators are distilled from an extensive corpus of battery data. Subsequently, this study proposes an indirect battery capacity prediction model intertwined with health feature extraction, hinged on the time-attention mechanism. The efficacy of the proposed model is assayed through a spectrum of assessment metrics and juxtaposed against other well-entrenched deep learning models. The model’s efficacy is validated across various battery datasets, with the Test Mean Absolute Error (MAE) and Test Root Mean Squared Error (RMSE) values consistently falling below 0.74% and 1.63%, respectively, showcasing the model’s commendable predictive prowess and reliability in the lithium-ion battery capacity prediction arena.

Random Forest-based Training Dataset Supplementation and Prediction of Lithium-ion Battery Capacity

Random Forest-based Training Dataset Supplementation and Prediction of Lithium-ion Battery Capacity

Battery state of health prediction based on voltage intervals, BP neural network and genetic algorithm

ABSTRACT Accurate prediction of lithium ion (li-ion) battery capacity is of great significance to battery health status management. In this paper, the different discharge time corresponding to the equal voltage interval is taken as the health factor. Three highly correlated health factors are extracted from the battery discharge curve, and the Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) is used to estimate the battery capacity accurately and quickly. Firstly, health factors related highly to battery capacity from the battery cycle life test are extracted, and the selected health factors are analyzed using the Spearman correlation coefficient and Pearson correlation coefficient. Secondly, this paper analyzes the prediction effect of different combinations of selected health factors using BPNN optimized by a genetic algorithm. Then, to verify the superiority of the proposed optimization algorithm, different optimization algorithms are used to adjust and optimize the parameters of BPNN automatically, and the experimental data are used for comparative analysis. Finally, the GA-BP is compared with other common battery capacity prediction methods. The results show that the GA-BP neural network prediction model can accurately and effectively predict the capacity of li-ion battery when using selected health factors.

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance Via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically.This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance.The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

Experimental Study on Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Three Regression Models for Electric Vehicle Application

This paper presents three regression models that predict the lithium-ion battery life for electric cars based on a supervised machine learning regression algorithm. The linear regression, bagging regressor, and random forest regressor models will be compared for the capacity prediction of lithium-ion batteries based on voltage-dependent per-cell modeling. When sufficient test data are available, three linear regression learning algorithms will train this model to give a promising battery capacity prediction result. The effectiveness of the three linear regression models will be demonstrated experimentally. The experiment table system is built with an NVIDIA Jetson Nano 4 GB Developer Kit B01, a battery, an Arduino, and a voltage sensor. The random forest regressor model has evaluated the model’s accuracy based on the average of the square of the difference between the initial value and the predicted value in the data set (MSE (mean square error)) and RMSE (root mean squared error), which is smaller than the linear regression model and bagging regressor model (MSE is 516.332762; RMSE is 22.722957). The linear regression model with MSE and RMSE is the biggest (MSE is 22060.500669; RMSE is 148.527777). This result allows the random forest regressor model to remain helpful in predicting the life of lithium-ion batteries. Moreover, this result allows rapid identification of battery manufacturing processes and will enable users to decide to replace defective batteries when deterioration in battery performance and lifespan is identified.

A Novel Fine-Tuning Model Based on Transfer Learning for Future Capacity Prediction of Lithium-Ion Batteries

Future capacity prediction of lithium-ion batteries is a highly researched topic in the field of battery management systems, owing to the gradual degradation of battery capacity over time due to various factors such as chemical changes within the battery, usage patterns, and operating conditions. The accurate prediction of battery capacity can aid in optimizing its usage, extending its lifespan, and mitigating the risk of unforeseen failures. In this paper, we proposed a novel fine-tuning model based on a deep learning model with a transfer learning approach comprising of two key components: offline training and online prediction. Model weights and prediction parameters were transferred from offline training using source data to the online prediction stage. The transferred Bi-directional Long Short-Term Memory with an Attention Mechanism model weights and prediction parameters were utilized to fine-tune the model by partial target data in the online prediction phase. Three battery batches with different charging policy were used to evaluate the proposed approach’s robustness, reliability, usability, and accuracy for the three charging policy batteries’ real-world data. The experiment results show that the proposed method’s efficacy improved, with an increase in the cycle number of the starting point, exhibiting a linear relationship with the starting point. The proposed method yields relative error values of 8.70%, 6.38%, 9.52%, 7.58%, 1.94%, and 2.29%, respectively, for the six target batteries in online prediction. Thus, the proposed method is effective in predicting the future capacity of lithium-ion batteries and holds potential for use in predictive maintenance applications.

A Lithium-Ion Battery Capacity and RUL Prediction Fusion Method Based on Decomposition Strategy and GRU

To safeguard the security and dependability of battery management systems (BMS), it is essential to provide reliable forecasts of battery capacity and remaining useful life (RUL). However, most of the current prediction methods use the measurement data directly to carry out prediction work, which ignores the objective measurement noise and capacity increase during the aging process of batteries. In this study, an integrated prediction method is introduced to highlight the prediction of lithium-ion battery capacity and RUL. This approach incorporates several techniques, including variational modal decomposition (VMD) with entropy detection, a double Gaussian model, and a gated recurrent unit neural network (GRU NN). Specifically, the PE−VMD algorithm is first utilized to perform a noise reduction process on the capacity data obtained from the measurements, and this results in a global degradation trend sequence and local fluctuation sequences. Afterward, the global degradation prediction model is established by employing the double Gaussian aging model proposed in this paper, and the local prediction models are built for each local fluctuation sequence by GRU NN. Lastly, the proposed hybrid prediction methodology is validated through battery capacity and RUL prediction studies on experimental data from three sources, and its accuracy is also compared with prediction algorithms from the recent related literature. Experimental results demonstrate that the proposed hybrid prediction method exhibits high precision in the predicting future capacity and RUL of lithium-ion batteries, along with strong robustness and predictive stability.

A novel lithium-ion battery capacity prediction framework based on SVMD-AO-DELM

Accurate and efficient lithium-ion battery capacity prediction plays an important role in improving performance and ensuring safe operation. In this study, a novel lithium-ion battery capacity prediction model combining successive variational mode decomposition (SVMD) and aquila optimized deep extreme learning machine (AO-DELM) is proposed. Firstly, SVMD is used to divide capacity signal and it improves short-term trend prediction, especially for capacity growth that occurs during the degradation process. Secondly, the DELM network outperforms other networks in efficiently extracting time-dependent features, and it is more accurate than other standard ELM-based methods. The AO algorithm is used to find the parameters of the DELM training process for the problem of sensitivity to initial weights. Finally, experiments are conducted to validate the predictive performance of the proposed model based on NASA and CALCE lithium-ion batteries sequences. The MAE (0.0066Ah, 0.0044Ah), RMSE (0.0113Ah, 0.0078Ah), MAPE (0.44%, 0.82%) are effectively reduced, and the R2 (98.94%, 99.87%) is better than the prediction performance of other hybrid models.

An interpretable BRB model with interval optimization strategy for lithium battery capacity prediction

AbstractAccurately predicting the capacity of lithium battery is conducive to improving its safety. Affected by complex internal electrochemical reaction and external use conditions, the prediction accuracy is difficult to guarantee; at the same time, the existing prediction methods are unexplainable, resulting in the inability to trace the prediction process. Therefore, a capacity prediction model based on belief rule base with interpretability and interval optimization strategy is proposed in this paper. First, the reasoning process is designed according to interpretability modeling criteria. Second, to achieve a balance of accuracy and interpretability, based on the whale optimization algorithm, a model parameter optimization method using interval optimization strategy is proposed. Finally, through a case study, the model's effectiveness is verified. Comparison with other models shows that the proposed model has certain advantages in accuracy and interpretability.