Lithium-ion Battery State Research Articles

Passive Equilibrium Circuit for Lithium-Ion Battery State of Charge Balance

Abstract Lithium-ion batteries serve as pivotal energy storage solutions in electric vehicles and portable electronics. Precise State of Charge (SOC) estimation is crucial for optimizing battery performance and ensuring longevity. This paper introduces an innovative approach utilizing passive equilibrium circuits for enhanced SOC estimation. The passive balancing circuits actively equalize voltage levels among individual battery cells during both charging and discharging, thereby improving SOC estimation accuracy. The proposed method is implemented and simulated using Matlab Simulink, demonstrating superior performance compared to traditional methods. Simulation results reveal the effectiveness of the passive equilibrium circuit in diverse operating conditions, offering a promising solution for accurate SOC estimation. This research contributes to the advancement of battery management systems, with potential applications in renewable energy storage.

An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures

With the development of intelligentization and network connectivity of new energy vehicles, the estimation of power lithium-ion battery state of charge (SOC) using artificial intelligence methods is becoming a research hotspot. This paper proposes an optimized multi-segment long short-term memory (MSLSTM) network strategy for SOC estimation of powered lithium-ion batteries' adaptive wide temperatures. First, the multi-timescale electrochemical processes during the charging and discharging of power lithium-ion batteries are efficiently analyzed, and the analytically measurable external parameters are classified into subsets based on the analysis. Secondly, the idea of segment long short-term memory (SLSTM) estimation is proposed to enhance the data linkage between the SOC and the nonlinearly varying parameters and to improve the prediction accuracy. Finally, an optimized MSLSTM neural network is proposed for nonlinear regression prediction of SOC in subset intervals through a combination of segmented estimation idea and SLSTM neural network. The proposed algorithm is validated under a variety of temperatures and operating conditions, and the accuracy of the SOC estimation is improved by at least 66.770 % or more. It provides a solution idea for intelligent estimation of power lithium-ion battery SOC.

Imprints of Graphene Nanocomposites Towards Energy Storage Potential of Lithium Ion Batteries—State of the Art and Perspectives

Imprints of Graphene Nanocomposites Towards Energy Storage Potential of Lithium Ion Batteries—State of the Art and Perspectives

Lithium-ion battery state of health and failure analysis with mixture weibull and equivalent circuit model

Lithium-ion battery state of health and failure analysis with mixture weibull and equivalent circuit model

A deep learning framework for the joint prediction of the SOH and RUL of lithium-ion batteries based on bimodal images

Accurately predicting the state of health (SOH) and remaining useful life (RUL) is significant for battery-powered electric devices. Since the images generated from raw cyclic data of the batteries can reveal more inherent battery degradation characteristics than raw cyclic data studied in most of previous studies, the bimodal images generated by two methods, such as images of curves and Gramian angular field (GAF), are effectively integrated into a unified deep learning framework to predict the health states of lithium-ion batteries. To this end, a bimodal fusion regression network (BFRN) is elaborately designed to jointly predict SOH and RUL of the battery, in which the features of bimodal images for the battery are fully extracted, interactively fused and aggregated by specific-designed network modules. Experiments on the public MIT/Stanford dataset indicate that the proposed method can achieve accurate joint state prediction for lithium-ion batteries, with the coefficient of determination (R2) for the SOH and RUL tasks are 0.98 and 0.93, respectively, which is superior to existing deep learning methods. Experiments on another public battery dataset demonstrate that it can be practical and generalized for the batteries with other chemistries.

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

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

The 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.

Modeling and health feature extraction method for lithium-ion batteries state of health estimation by distribution of relaxation times

Accurate state of health (SOH) estimation of lithium batteries (LIBs) depends on the accuracy of LIBs modeling and the correlation of extracted health features (HFs). Aiming at the problems of a priori assumptions for existing battery modeling and poor correlation of HFs, this paper proposes a SOH estimation method for LIBs based on distribution of relaxation times (DRT). Firstly, we deduce and establish the developed DRT-based impedance model from battery electrochemical impedance spectroscopy (EIS), and the model parameters are calculated by EIS split-frequency domain method based on the principle of parsimony. Then, we analyze the variation rules of the peak and valley information in the distribution function of relaxation times curve with the capacity decay, and we extract multiple features strongly correlated with SOH as HFs. The extracted HFs are subjected to Weighted Principal Component Analysis (WPCA) to obtain indirect health features (IHFs), which achieve the estimation of SOH for LIBs. Finally, the effectiveness of the proposed modeling and health feature extraction method is validated by SOH estimation using the public dataset from Nature subseries. The results indicated that the EIS fitting accuracy R2 of the developed model is the best and the RMSE of SOH estimation is within 0.873 %.

State of Health Estimation for Lithium-Ion Battery Based on Sample Transfer Learning under Current Pulse Test

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods.

Accelerated proximal gradient algorithm for lithium-ion battery state of charge estimation with outliers

Accelerated proximal gradient algorithm for lithium-ion battery state of charge estimation with outliers

Multi-interest adaptive unscented Kalman filter based on improved matrix decomposition methods for lithium-ion battery state of charge estimation

Accurately predicting the state of charge (SOC) is crucial to improving Li-ion battery performance. However, available model-based estimation approaches still face challenges in handling model uncertainty and measurement noise effects on parameter identification. Besides, the widely used unscented Kalman filter (UKF) algorithm has limitations in electric vehicles as it requires the error covariance matrix to maintain positive definiteness, limiting its applicability under certain conditions. This study introduces the bias-compensated forgetting factor recursive least squares (BCFFRLS) method for parameter estimation within the second-order RC equivalent circuit model specific to the INR18650-20R battery. Furthermore, we propose a novel algorithm named the optimization multi-interest adaptive unscented Kalman filter (O-MIAUKF). This algorithm is designed to address stability and robustness issues with traditional UKF encounters in dynamic environments. Experimental validation demonstrates that the O-MIAUKF algorithm excels in maintaining strong stability and robustness in various working conditions, accurately estimating SOC even with a non-positive covariance matrix. The SOC estimation error remains stable at 0.8 %, which is lower than that of the current Extended Kalman Filter (EKF), UKF, and Dual Extended Kalman Filter (DEKF).

A Novel Feature Engineering-Based SOH Estimation Method for Lithium-Ion Battery with Downgraded Laboratory Data

Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery’s aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%.

Binary multi-frequency signal for accurate and rapid electrochemical impedance spectroscopy acquisition in lithium-ion batteries

Electrochemical Impedance Spectroscopy (EIS) plays a crucial role in characterizing the internal electrochemical states of lithium-ion batteries and proves to be effective for estimating battery states. Traditional EIS measurement, however, requires expensive electrochemical workstations with time-consuming signal injection, especially in low-frequency regions, thus limiting its practical applications. Here we show that applying our proposed pulse-like Binary Multi-Frequency Signals (BMFS) as the excitation signal in the EIS measurement, which simultaneously possesses numerous frequency components and maintains high energy at each frequency component, will significantly improve test speed while retaining accuracy. The applicability of the BMFS under various cathode material types, including nickel cobalt manganese (NCM), lithium cobalt oxide (LCO), and lithium iron phosphate (LFP) is demonstrated. The robustness of the signal is experimentally verified through varying C-rates and measurement window lengths. The BMFS, requiring only 30 s per test, can achieve test results with an amplitude error of 1% and a phase error of 1° as compared with those obtained from traditional EIS tests. Moreover, BMFS can also be applied in online EIS measurement scenarios, favorable for real-world applications. This work enables accurate and rapid acquisition of EIS results, which is currently expensive and time-consuming to obtain, ensuring a faster and more nuanced characterization of the internal states of many battery systems in an affordable and accessible manner, especially in data-driven and machine-learning approaches.

Lithium-ion battery state of health prognostication employing multi-model fusion approach based on image coding of charging voltage and temperature data

The employment of readily obtainable parameters, including voltage and temperature, derived from the battery management system facilitates the real-time evaluation of lithium-ion batteries health status. However, the complex aging mechanisms and noise interference inherent in lithium-ion batteries present substantial challenges to accurate estimation. Hence, this research proposes a multi-model fusion approach utilizing Gramian Angular Field encoding to accurately predict the health status of lithium-ion batteries. Battery voltage and temperature data are acquired through system aging experiments. The Gramian Angular Field-based image encoding transforms these data into images, accentuating the subtle characteristics of the data and thereby enhancing its recognizability by neural networks. Subsequently, two-dimensional Convolutional Neural Networks are employed to extract features from the images, followed by one-dimensional Convolutional Neural Networks to reduce the dimensions of the feature matrix. The battery health status is then predicted utilizing Long Short-term Memory Networks. The culmination is the evaluation of the model performance through the application of quantitative error metrics. The study results show that the method can pinpoint the prediction error to within 2%, significantly improving accuracy over traditional prediction methods. It proves the great potential of direct data, such as voltage and temperature, in predicting battery health.

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the three main steps involved in various machine-learning-based SOC estimation methods. It delves into the aspects of data collection and preparation, model selection and training, as well as model evaluation and optimization, offering a thorough analysis, synthesis, and summary. The aim is to lower the research barrier for professionals in the field and contribute to the advancement of intelligent SOC estimation in the battery domain.

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%.

Assessing spatial non-uniformities in lithium-ion battery state of charge using ultrasound immersion testing

Enhancing the performance, safety and reliability of battery management systems is crucial for advancing the state of the art in battery electric vehicles. Current research explores the potential of ultrasound to monitor state of charge (SoC) changes in individual cells. Understanding spatial variations in SoC is essential, as non-uniformities could lead to sub-optimal performance, premature ageing, and possible safety risks. This study uses ultrasound immersion C-scans to map wave speed and attenuation at different SoC levels during battery cycling. Results indicate non-uniform wave speed and attenuation suggestive of SoC spatial variations within single cells, emphasising the importance of addressing this issue. Acoustic measurements under various C-rates and relaxation periods are discussed, providing insights into lithium-ion rearrangement in graphite particles. Potential causes of structure and manufacturing variations of the cell are discussed, highlighting the need to address these issues to prevent overcharging or overdischarging in specific battery areas.

A hybrid deep learning model for lithium-ion batteries state of charge estimation based on quantile regression and attention

Accurate estimation of the state of charge (SOC) is essential to ensure the safe and efficient utilization of lithium-ion batteries. However, external factors can affect the SOC, making it challenging to achieve precise estimations. To address this issue, we propose a deep learning model that integrates the parallel computing capabilities of temporal convolutional networks with the robust learning abilities of gated recurrent units. This model incorporates an attention mechanism to dynamically assign weights and focus on salient features based on correlations in historical information. Furthermore, we employ the quantile regression loss function during network training, thereby equipping the neural network model with the capability to directly generate interval estimates. The results show that the MAE and RMSE of the lithium iron phosphate battery, lithium cobalt oxide battery and ternary lithium battery dataset are below 1.45 and 1.98, 1.12 and 1.25, 0.61 and 0.75 respectively. The comparison analysis with other SOC estimation methods demonstrates its exceptional accuracy in point estimation and high reliability in confidence interval estimation. Furthermore, it showcases remarkable generalization ability across diverse temperatures, operating conditions, battery lifetimes, and battery types.

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.