Lithium-ion Battery State Research Articles

Deep transfer learning enables battery state of charge and state of health estimation

In the realm of lithium-ion battery state estimation, traditional data driven approaches face challenges in accurately estimating state of charge and state of health throughout the battery's life cycle under dynamic working condition, and there is still a lack of research on models that can fulfill these requirements simultaneously. To address these issues, this study proposes an adaptive convolutional gated recurrent unit with Kalman filter for state of charge estimation throughtout battery's full life cycle, leveraging transfer learning and deep learning techniques. Additionally, an adaptive convolutional gated recurrent unit with average post-processor is developed to estimate the battery state of health under dynamic working conditions, using voltage, current, temperature, state of charge, and accumulated discharge capacity as input features. Furthermore, a joint adaptive deep transfer learning model is proposed for simultaneously state of charge and state of health estimation through battery's full life cycle under dynamic working conditions. Experimental results validate the feasibility, accuracy, and robustness of the proposed models.

Lithium-ion battery state of health estimation method based on variational quantum algorithm optimized stacking strategy

Accurate state-of-health (SOH) estimation is critical for the performance and safety of lithium-ion batteries. An innovative method for SOH estimation is proposed by employing a variational quantum algorithm to optimize a stacking integrated learning strategy. The strategy effectively combines multiple model advantages, enhancing the estimation accuracy and generalizability. Using this method, eight sets of health factors are extracted, focusing on the relationship between battery capacity degradation and electrothermal parameters. A stacking integrated learning framework is developed by utilizing diverse primary learners to effectively capture the dynamic changes in health factors. A ridge regression meta-learner is incorporated to address overfitting problems found in primary learners. A significant innovation is the integration of a variational quantum circuit module as the primary learner. This module plays a crucial role in optimizing the hyperparameters for the analysis of complex and high-dimensional battery data. The effectiveness of the method is validated using four different types of batteries, showing a 77.4% improvement in prediction accuracy compared with traditional methods, with the SOH estimation error maintained within a tight margin of 0.67%. The mean absolute error, mean absolute percentage error, and root mean square error with maximum reduction rates are 76.7%, 77.4%, and 62.7%, respectively. The maximum increase in the R-squared coefficient is 5.3%. This study demonstrates the potential of variational quantum algorithms in enhancing the SOH estimation accuracy and opens new possibilities for the advanced health status management of lithium-ion batteries.

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.

ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

Accuracy and speed of Lithium-ion battery state of charge (SOC) estimation determine the reliability and stability of electric vehicle (EV), as well as promoting the development of smart home energy management system. Existing SOC estimation approaches embedded in commercial EVs still suffer from limitations of low precision, effectiveness, and relatively low robustness. To address these issues, we propose an internal cascaded neuromorphic computing system (ICNCS) via memristor circuits for EV SOC estimation. Specifically, we use three circuit modules to facilitate the design of the proposed ICNCS. Firstly, a bidirectional gated recurrent unit (Bi-GRU) circuit module is designed, enabling adequate feature extraction from the time-contextual battery information. Secondly, an attention circuit module is proposed to distinguish the useful and unimportant information related to SOC estimation. Thirdly, the Kalman filter module is constructed to eliminate the random noise caused by data transmission and processing. Finally, a series of experiments and analysis demonstrate that the proposed ICNCS has good performances in terms of accuracy (the lowest achievable RMSE and MAE are 0.853 and 0.711, respectively), time efficiency (approximately 16 20 times), and robustness (anti-noise capacity), indicating an advancement in consumer electronics applications.

State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine

Lithium-ion battery State of Health (SOH) estimation is an essential issue in battery management systems. In order to better estimate battery SOH, Extreme Learning Machine (ELM) is used to establish a model to estimate lithium-ion battery SOH. The Swarm Optimization algorithm (PSO) is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy. Firstly, collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve. Use Grey Relation Analysis (GRA) method to analyze the correlation between battery capacity and five characteristic quantities. Then, an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics, and a PSO is introduced to optimize the parameters of the capacity estimation model. The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions. The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation, and the average absolute percentage error is less than 1%.

Online Lithium-ion Battery Modeling and State of Charge Estimation via Concurrent State and Parameter Estimation

This paper develops a novel approach for online Lithium-ion (Li-ion) battery model identification and state of charge (SOC) estimation. To account for the SOC-dependent battery dynamics and the static nonlinearity between the open-circuit voltage (OCV) and SOC, we formulate a grey box nonlinear state-space model, in which elements depend on SOC in a polynomial way. For model identification, we propose an online concurrent state and parameter estimation by alternating the recursive least squares algorithm and particle filter; the SOC is computed via Coulomb counting during the modeling. The identified grey box model is then applied for SOC estimation using the particle filter. Simulation with real-world battery measurements demonstrates the effectiveness of the model structure and the estimation approach, which is reflected in accurate terminal voltage estimation and nonlinear OCV-SOC relation, and superior performance regarding SOC estimation compared to state-of-the-art approaches.

Transfer Learning Techniques for the Lithium-Ion Battery State of Charge Estimation

Transfer Learning Techniques for the Lithium-Ion Battery State of Charge Estimation

Data-driven Prediction Methods for Lithium-ion Battery State of Health Based on Elbow Rule

Data-driven Prediction Methods for Lithium-ion Battery State of Health Based on Elbow Rule

Lithium-Ion Battery State of Health Estimation Method Based on Frequency-Domain-Assisted Data Decoupling and Time–Frequency Features Compensation Learning Network

Lithium-Ion Battery State of Health Estimation Method Based on Frequency-Domain-Assisted Data Decoupling and Time–Frequency Features Compensation Learning Network

Decentralized Deep Learning Approach for Lithium-Ion Batteries State of Health Forecasting Using Federated Learning

Decentralized Deep Learning Approach for Lithium-Ion Batteries State of Health Forecasting Using Federated Learning

Bayesian Approach to Estimate Distribution of Relaxation Times for Lithium-Ion Batteries

Electrochemical impedance spectroscopy (EIS) has emerged as a powerful tool for analyzing the state of lithium-ion batteries due to its ability to provide rich electrochemical information. Traditionally, equivalent circuit and physical models have been utilized for interpreting EIS data. However, these methods possess inherent drawbacks: equivalent circuit models are non-unique, while physical models can be challenging to implement. As an alternative, the distribution of relaxation times (DRT) has garnered increasing attention in recent years. The DRT method enables the analysis of EIS data without predefining a model's shape, allowing for the decomposition of internal characteristics in complex systems, such as lithium-ion batteries. Despite its advantages, DRT estimation is an ill-posed inverse problem that often requires regularization or fine-tuning based on empirical choices, which can lead to the misinterpretation of EIS data.To address these challenges, we propose a Bayesian approach that enables the estimation of DRT while considering uncertainties, even in the presence of observational errors in EIS data. Moreover, the proposed method can assist in estimating the state of health (SOH) of the battery, which provides valuable insights into the battery's overall performance, longevity, and safety. To validate the efficacy of our proposed method, we apply it to synthetic EIS data and compare its performance with existing DRT estimation methods. The results show that the proposed Bayesian approach offers a more robust and accurate estimation of DRT. We also demonstrate the practical applicability of the proposed method by applying it to real lithium-ion battery EIS experimental data, where it successfully extracts meaningful information about the battery's internal state and SOH. In summary, the proposed DRT-based Bayesian method has the potential to enable a robust diagnosis of the internal state and SOH of lithium-ion batteries through EIS measurements. By incorporating this approach, researchers and industry practitioners can gain a deeper understanding of battery performance, facilitating the development of improved diagnostic techniques and battery management strategies to enhance the performance and reliability of lithium-ion batteries. This, in turn, could contribute to the broader adoption of sustainable energy solutions, such as electric vehicles and renewable energy storage systems.This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grants funded by the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20214910100070). Figure 1

Lithium-Ion Battery State of Charge Estimation With Adaptability to Changing Conditions

Accurate state of charge (SOC) estimation provides critical information to ensure the safe operation of lithium-ion batteries (LIBs). With the evolution of artificial intelligence, the model-free deep learning-based SOC estimation has made extraordinary progress. However, collecting sufficient data to train an accurate SOC estimator of batteries' whole lifecycle is costly, and generalizing the estimator to different operating conditions is challenging. To this end, aiming to enhance the generalization ability of data-driven SOC estimators, this paper proposes a novel SOC estimation framework based on adversarial transfer learning (TL), which transfers a well-trained SOC estimator with fewer data demands to fit different conditions. Specifically, a source SOC estimator is trained under a specific condition to learn the basic characteristics of the battery, which is defined as the source domain. Target domains correspond to measured data of the battery under different conditions. An adversarial TL-based training framework is developed to extract the domain invariants of the source and target domains, which is guided by minimizing the distribution discrepancies. The effectiveness of the proposed method is demonstrated through the use of two LIBs datasets. The successful generalization to multiple operating conditions and aging health states demonstrates the estimation accuracy, robustness, and good generalization.

An electrochemical-thermal-aging effects coupled model for lithium-ion batteries performance simulation and state of health estimation

Numerous interrelated coupled aging mechanisms contribute to the degradation of lithium-ion batteries (LIBs), making it challenging to confirm the influence of aging effects on the performance and state of health (SOH). Inspired by electrochemical impedance spectroscopy (EIS) analysis and observed morphological changes, this paper considers double layer resistance increase, solid electrolyte interface (SEI) film growth, and cathode aggregate crack propagation as the main aging effects, and the influence of temperature is considered, leading to development of an electrochemical-thermal-aging coupled model. The model achieves high-precision simulation for different temperature and aging conditions by updating the model parameters based on the aging effects and internal temperature. Compared with experimental data, the relative error of voltage simulation is within 1%. Additionally, the aging effects parameters exhibit a consistent trend with data analysis and mechanism inference, and this strong correlation is beneficial for estimating SOH. The estimation error of SOH based on four linear regression methods is less than 0.5%. This novel electrochemical-thermal-aging effects coupling model addresses two major challenges in battery management system (BMS): performance simulation and SOH estimation under real-world operating conditions.

Data-Driven Discovery of Lithium-Ion Battery State of Charge Dynamics

Abstract We present a physics-inspired input/output predictor of lithium-ion batteries (LiBs) for online state-of-charge (SOC) prediction. The complex electrochemical behavior of batteries results in nonlinear and high-dimensional dynamics. Accurate SOC prediction is paramount for increased performance, improved operational safety, and extended longevity of LiBs. The battery's internal parameters are cell-dependent and change with operating conditions and battery health variations. We present a data-driven solution to discover governing equations pertaining to SOC dynamics from battery operando measurements. Our approach relaxes the need for detailed knowledge of the battery's composition while maintaining prediction fidelity. The predictor consists of a library of candidate terms and a set of coefficients found via a sparsity-promoting algorithm. The library was enhanced with explicit physics-inspired terms to improve the predictor's interpretability and generalizability. Further, we developed a Monte Carlo search of additional nonlinear terms to efficiently explore the high-dimensional search space and improve the characterization of highly nonlinear behaviors. Also, we developed a hyperparameter autotuning approach for identifying optimal coefficients that balance accuracy and complexity. The resulting SOC predictor achieved high predictive performance scores (RMSE) of 2.2×10−6 and 4.8×10−4, respectively, for training and validation on experimental results corresponding to a stochastic drive cycle. Furthermore, the predictor achieved an RMSE of 8.5×10−4 on unseen battery measurements corresponding to the standard US06 drive cycle, further showcasing the adaptability of the predictor and the enhanced modeling approach to new conditions.

Application of Nondestructive Testing Technology in Device‐Scale for Lithium‐Ion Batteries

Lithium‐ion batteries (LIBs), due to their high energy density and long cycling life have been widely applied in a variety of industries, including electric vehicles, small‐ and medium‐sized electronic devices, and intelligent medical care. Nevertheless, the security and real‐time state of LIBs is difficult to obtain accurately, improving the battery's service life and ensuring battery safety have become the focus of research. Nondestructive testing (NDT) technology has developed quickly to reach this purpose, requiring a thorough investigation of how batteries’ internal structures have evolved. The principles, contributing factors, and applications of various widely used NDT techniques are summarized and discussed in this review. These inspection techniques can be used to evaluate the battery condition, observe the internal structure of the battery, analyze the failure phenomenon and electrochemical performance of the battery operation, etc. Finally, a summary and outlook are given regarding the characteristics and prospects of NDT methods. This overview will show new light on the application of NDT technology for LIBs and will promote the development of next‐generation LIBs with high security.

CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics

Lithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However, acquiring thermal imaging samples of lithium-ion battery faults is challenging due to factors such as high experimental costs and associated risks. To address this, our study proposes the utilization of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty and Residual Network (CWGAN-GP with Residual Network) to augment the dataset of thermal images depicting lithium-ion battery faults. We employ various evaluation metrics to quantitatively analyze and compare the generated thermal images of lithium-ion batteries. Subsequently, the expanded dataset, comprising four types of thermal images depicting lithium-ion battery faults, is input into a Mask Region-based Convolutional Neural Network for training. The results demonstrate that the proposed model surpasses both traditional Generative Adversarial Network and Wasserstein Generative Adversarial Network in terms of the quality of generated thermal images of lithium-ion batteries. Moreover, the augmentation of the dataset leads to an improvement in the fault diagnosis accuracy of the Mask Region-based Convolutional Neural Network.

A novel unsupervised domain adaptation-based method for lithium-ion batteries state of health prognostic

Precise state-of-health prognostic is crucial for the safe and reliable operation of Lithium-ion batteries in energy storage systems. Nevertheless, most data-driven state-of-health prognostic methods are only effective when the distribution of laboratory training data and test data is consistent. This paper proposes an unsupervised domain adaptation method for individualized SOH estimation. More specifically, a convolutional neural network is used to automatically extract battery aging features from raw charging voltage curves. Besides, we define a geometrical distance between different domain feature subspaces based on the principal angles and learn deep transferable features by minimizing it. In this way, we can narrow the domain gap by using orthogonal bases of the feature spaces, while avoiding changes in feature scales. To maintain the geometrical properties of feature subspaces to the maximum extent possible, we further apply the bases mismatch penalization to penalize the matching of orthogonal bases of different importance. Our method is evaluated on three different datasets with different chemistries, profiles, and ambient temperatures to validate the effectiveness and generalizability. Our method outperforms several other domain adaptation approaches significantly. The results show that the proposed transfer learning-based method is widely applicable and can greatly shorten the duration of battery aging experiments.

A Novel Temperature Compensation Method for Surface Strain of Cylindrical Lithium-ion Batteries

In order to ensure the safe operation of lithium-ion batteries, real-time monitoring of battery status is necessary. The surface strain signal of lithium-ion batteries has the potential to evaluate the battery’s state, but it is significantly affected by temperature. Generally, measuring the battery surface temperature and the thermal expansion coefficient can be performed to quantify and eliminate the influence of temperature on strain, but this increases the cost and complexity of strain measurement. This article proposes a method that eliminates the need to measure the battery temperature and material parameters. By simultaneously measuring the circumferential and axial strains on the battery surface and calculating their difference, the influence of temperature on strain can be minimized. Furthermore, the effectiveness of the proposed method is experimentally tested. Results demonstrate that after applying temperature compensation to commercial lithium-ion batteries, the influence of temperature on strain can be reduced from 16.4 ppm/°C to 1.7 ppm/°C. The strain no longer exhibits sensitivity to current, making it more suitable for evaluating the state of lithium-ion batteries.

Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures

In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The SOC represents the current state of charge of the lithium-ion battery. This paper proposes a joint estimation algorithm based on genetic algorithm (GA) simulating biogenetic properties and support vector regression (SVR) to improve the prediction accuracy of lithium-ion battery SOC. Genetic algorithm support vector regression (GASVR) is proposed to address the limitations of traditional SVR, which lacks guidance on parameter selection. The model attains notable accuracy. GASVR constructs a set of solution spaces, generating initial populations that adhere to a normal distribution using a stochastic approach. A fitness function calculates the fitness value for each individual. Based on their fitness, the roulette wheel method is employed to generate the next-generation population through selection, crossover, and mutation. After several iterations, individuals with the highest fitness values are identified. These top individuals acquire parameter information, culminating in the training of the final SVR model. The model leverages advanced mathematical techniques to address SOC prediction challenges in the Hilbert space, providing theoretical justification for handling intricate nonlinear problems. Rigorous testing of the model at temperatures ranging from −20 ∘C to 25 ∘C under three different working conditions demonstrates its superior accuracy and robustness compared to extreme gradient boosting (XGBoost), random forest regression (RFR), linear kernel function SVR, and the original radial basis kernel function SVR. The model proposed in this paper lays the groundwork and offers a scheme for predicting the SOC within the battery management system of new energy vehicles.

Lithium-ion battery state of health estimation using a hybrid model based on a convolutional neural network and bidirectional gated recurrent unit

This paper proposes a real-time state of health (SOH) estimation model based on a deep learning (DL) framework. The proposed model is a combination of two different architectures; a one-dimensional convolutional neural network (1D-CNN) and a bidirectional gated recurrent unit (BiGRU). The hybrid CNN-BiGRU uses the 1D CNN layers to extract pertinent features from input data and then relies on the BiGRU layers for sequence learning in both directions. To account for all SOH indicators, the proposed approach uses the current, voltage, and temperature measurements, which are readily obtainable from the electric vehicle's battery management system (BMS). This prevents the complex and time-consuming feature extraction used in most related papers. Since the hyperparameters have a significant impact on the performance of neural network models, a Bayesian Optimization (BO) technique based on the Gaussian Process (GP) was considered to tune the CNN-BiGRU model hyperparameters. Accordingly, the objective function was able to converge to a low Mean Squared Error (MSE) of 1.2×10−5 in just 19 iterations. Afterward, to verify the accuracy of the optimized model, a Lithium-ion battery dataset with several discharge profiles provided by the National Aeronautics and Space Administration (NASA) was used. The obtained results demonstrated the accuracy and robustness of the proposed method compared to other commonly used models. The CNN-BiGRU model yielded a Mean Absolute Error (MAE) of 2.080% and a root-mean-square error (RMSE) of 2.516% in the case of the battery set #C, referring to a set with 70 cycles already used at 24 °C. Additionally, the End of life (EOL) indicator error of zero cycles for the same data.