Estimation Of Lithium-ion Batteries Research Articles

Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination (R2) exceeding 97%, significantly improving prediction accuracy and efficiency.

State-of-charge estimation based on improved back-propagation neural network for lithium-ion batteries in energy storage power stations

This paper develops a novel approach for the state of charge (SOC) estimation of Lithium-ion batteries in energy storage power stations, leveraging an improved back-propagation (BP) neural network optimized by an immune genetic algorithm (IGA). Addressing the paramount importance of accurate SOC estimation for enhancing battery management systems, this work proposes a methodological enhancement aimed at refining estimation precision and operational efficiency. First, the mechanisms of temperature, current, and voltage impacts on SOC are revealed, which serve as the inputs of the neural network. Second, the improved BP neural network’s structure and optimization through an IGA are designed, emphasizing the mitigation of traditional BP neural networks’ limitations including slow convergence speed and complex parameterization. Through an extensive experimental setup, the proposed model is validated against standard BP neural networks across various discharge experiments at different temperatures and discharge currents. Results prove that the estimation accuracy of the proposed method reaches as high as 98.15% and faster converges compared to the traditional BP network, thereby being valuable practically.

BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator

BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator

Enhanced Accuracy in State-of-Charge Estimation for Lithium-Ion Batteries in Electric Vehicles Using Augmented Adaptive Extended Kalman Filter

Accurate assessment of state of charge (SoC) is crucial for the safety and efficiency of lithium-ion batteries used in electric vehicles (EVs). For precise SoC estimation in EV BMSs, an Augmented Adaptive Extended Kalman Filter (AAEKF) has been proposed in this research. To improve the precision of SoC estimation, AAEKF approach combines Kalman filtering methods with adaptive robust control concepts. The effectiveness of the AAEKF algorithm is shown by simulation results and confirmed by comparison with data from LiFePO4 battery research. The proposed AAEKF SoC estimation matches measurement results with errors of less than 3%. Adaptive Extended Kalman Filter (AEKF) is quite sensitive to the accuracy of the battery model, as errors in this region can lead to large estimate errors. Faster convergence to actual SoC, improved real-time responsiveness, accuracy, and resilience to disturbances and parameter fluctuations are all results of AAEKF's efficient handling of nonlinearities in battery systems, adaptation to system changes, errors (Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and overall performance. The maximum estimate error stays under 5% at various external temperatures.

Exploration of Imbalanced Regression in state-of-health estimation of Lithium-ion batteries

The state of health (SOH) estimation for lithium-ion batteries based on deep learning (DL) has made great progress. However, due to different electrochemical compositions of lithium-ion batteries, different ways of conducting experiments and other factors, the degradation process of some batteries shows longer early degradation time and shorter later degradation time, resulting in a long-tailed distribution of degradation data. This leads to the problem of data imbalance in SOH estimation tasks, which affects the accuracy of SOH estimation. This article explores the long-tailed distribution phenomenon in the field of batteries and the corresponding imbalanced regression problem it brings to the estimation of battery SOH. In addition, a method for improving model performance is proposed. Specifically, we use a quadratic interpolation and standardization method to analyze the battery data to ensure the consistency of data features. By discretized analysis of continuous problems, the label distribution smoothing (LDS) method is applied to deep neural networks to analyze and solve this imbalanced regression problem. By convolution processing with the kernel function and label distribution, the weights corresponding to different labels are calculated, which improves the estimation accuracy. We conducted battery aging experiments and verified that the degradation data follows a long-tailed distribution. The effectiveness of the final method was validated on our experimental data and a publicly available dataset.

Fast estimation method for lithium battery state of health based on incremental capacity curve peak characteristics

State of Health (SOH) estimation for lithium-ion batteries is a complex task, often plagued by issues such as slow estimation speed and high practical application difficulty in existing methods. In this paper, capacity is established as the criterion for defining State of Health. We conducted an analysis of Incremental Capacity (IC) curves, examining the characteristic features of the incremental capacity curves throughout the entire lifecycle of lithium-ion batteries at different temperatures and charge-discharge rates. The investigation revealed that the peaks in the incremental capacity curves correspond to nearly constant State of Charge (SOC) values. Building upon this discovery, a rapid SOH estimation method based on IC curves is proposed. The method was validated using data from the complete lifecycle of 18,650 cells, yielding a maximum SOH estimation error of 3%. Furthermore, the proposed method was applied to 240 cells in an energy storage battery pack at a specific station, resulting in a maximum SOH estimation error of 3.9%. This demonstrates the feasibility of the method under practical conditions, highlighting its high precision in SOH estimation. The proposed approach holds significant promise for real-time and rapid SOH estimation and detection, offering valuable insights for battery health monitoring.

Gramian angular field-based state-of-health estimation of lithium-ion batteries using two-dimensional convolutional neural network and bidirectional long short-term memory

Accurate assessment of the state of health (SOH) of Lithium-ion batteries (LIBs) is crucial for ensuring the stability and reliability of associated equipment. However, in practical applications, current feature extraction techniques face challenges due to process complexity and the difficulty of models in capturing the dynamic evolution of time series data. This study introduces an advanced method for predicting LIBs SOH by integrating two-dimensional (2D) convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) with the Gramian angular field (GAF) technique. Health indicators (HIs) are derived from the incremental capacity and charging voltage curves, which are transformed into two-dimensional images using GAF. Bayesian optimization determines the optimal hyperparameters for the model, which uses image data of two health factors as inputs. The results demonstrate that the proposed dual-channel(CH2) image data input model, combined with voltage data during the charging phase, achieves superior performance, with lower errors and higher accuracy, evidenced by an average root mean square error (RMSE) of 0.0112 and an average mean absolute error (MAE) of 0.0087. The model's generalization capability and comparative analysis with existing methodologies affirm its practical significance.

Enhancing the state-of-charge estimation of lithium-ion batteries using a CNN-BiGRU and AUKF fusion model

Accurate estimation of lithium-ion batteries’ state of charge (SOC) is crucial for the safety of energy storage devices, preventing issues such as overcharging or discharging. This paper introduces a fusion model combining convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and adaptive unscented Kalman filter (AUKF). This method's advantage lies in integrating Kalman filtering with the benefits of neural networks, eliminating the need for complex hyperparameter tuning and battery model construction. Initially, CNN-BiGRU maps variables like voltage, current, and temperature to SOC for initial estimation. The AUKF algorithm then filters these SOC outputs, minimizing fluctuations and enhancing accuracy. This approach ultimately leads to precise and stable SOC estimates. To validate the model's effectiveness, lithium-ion battery datasets across various temperatures were extensively trained and tested. The CNN-BiGRU-AUKF model demonstrated consistent performance under these conditions, with a maximum MSE of 0.00011, MAE under 0.0092, Max AE around 0.02, and a maximum RMSE of 0.017. Compared to similar methods, the proposed approach demonstrates superior SOC estimation accuracy and computational efficiency. Additionally, the model's scalability was validated using training and testing data for SOC estimates from a different type of lithium battery.

Adaptive optimal observer for real-time state of charge estimation of lithium-ion batteries in robotic systems

PurposeThis study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.Design/methodology/approachThe authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.FindingsThe proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.Originality/valueThis study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

State-of-health estimation of lithium-ion batteries using multiple correlation analysis-based feature screening and optimizing echo state networks with the weighted mean of vectors

Lithium-ion batteries (LIBs) are widely employed in electric vehicles (EVs) and energy storage systems owing to their high energy density, low self-discharge rate, and superior longevity. As a vital evaluation index for the degradation state of LIBs, the state of health (SOH) must be accurately estimated. This study adopts an electrochemical impedance spectroscopy (EIS)-based equivalent circuit model (ECM) to estimate battery SOH. The internal dynamic electrical behaviors of LIBs are decoupled by the distribution of relaxation times (DRT) method, whereby the critical degradation features are extracted from the DRT curves and the fitted ECM parameters. Subsequently, referring to the ensemble learning approach (ELA), the multiple correlation analysis (MCA) is utilized to identify the most pertinent degradation features. The battery data are subjected to a comprehensive analysis with five types of correlations to ascertain the optimal number and combination of health indicators (HIs). Furthermore, the weighted mean of vectors (INFO) algorithm is employed to optimize the hyperparameters in the echo state network (ESN) model for SOH estimation. The echo state networks model optimized with the weighted mean of vectors algorithm (INFO-ESN) is verified with eight types of LIBs under different operating conditions. Experimental results of the proposed method manifest that the root mean square error (RMSE) and mean absolute error (MAE) of the battery SOH range from 0.32 % to 1.06 %, thereby verifying the accuracy and robustness of the proposed method.

Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM

Accurate state-of-charge (SOC) estimation is a crucial part of the battery management system (BMS). However, conventional estimation methods are unable to capture the extremely complex dynamic characteristics of lithium-ion batteries. Besides, manually setting the optimal hyperparameters of models has many drawbacks. To address the problems, an (improved whale optimization algorithm) IWOA- (long short-term memory) LSTM model is proposed in this work. Utilizing the whale optimization algorithm (WOA) improved with four enhancement strategies (Gaussian chaotic mapping initialization, Nonlinear weight update, Lévy flight mechanism, and Elite opposition-based learning) to optimize the number of hidden layer nodes, the learning rate, and the number of iterations of LSTM model. It not only overcomes the shortcomings of artificially setting LSTM hyperparameters but also further boosts the learning ability of the IWOA-LSTM model, making the model more suitable for SOC estimation under different scenarios. The evaluation results show that the MAE of the proposed model for SOC estimation results is lower than 0.8 % under different temperatures and dynamic conditions. Compared with SOTA models, all MAE, RMSE, and MAPE of the proposed model substantially decline. Furthermore, the R2 of the estimation results using the LG dataset in Experiment Ⅲ is 98.65 %, suggesting the applicability of the proposed model to Li-ion batteries from various manufacturers. The experimental results demonstrate the proposed IWOA-LSTM model is suitable for accurate SOC estimation.

State-of-health estimation for lithium-ion batteries using relaxation voltage under dynamic conditions

The data-driven approach accurately estimates the state-of-health of lithium-ion batteries using online data, aiding consumers in operational and maintenance decisions. However, the stochastic charging and discharging behavior in realistic scenarios leads to continuous transient processes that render conventional features undetectable or exacerbate fluctuations. Here, we use domain knowledge and equivalent circuit modeling to investigate the extraction of physical features of aging through a relatively stable relaxation process under dynamic conditions. Our study uses 16-cell data from the National Aeronautics and Space Administration randomized dataset and compares four basic data-driven models for validation. The results show that incorporating a limited set of previous discharge step information significantly enhances model robustness and accuracy. The best-performing model, auto-relevance determination gaussian process regression, achieves a low root mean square error of 1.94 %. Physically interpretable features do not rely on historical data, require a smaller sample size, and exhibit greater generalizability across different current scenarios. This method does not depend on a specific charging method, making it practical and adaptable. Therefore, the data-driven approach utilizing relaxation voltages and correlation features offers a viable solution for accurately estimating the health state of lithium-ion batteries under dynamic conditions.

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

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

Combined Meta-Learning With CNN-LSTM Algorithms for State-of-Health Estimation of Lithium-Ion Battery

Combined Meta-Learning With CNN-LSTM Algorithms for State-of-Health Estimation of Lithium-Ion Battery

Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms

Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management.

A novel data-driven IBA-ELM model for SOH/SOC estimation of lithium-ion batteries

Lithium-ion batteries (Libs) have become the best power source for electric vehicles due to their high energy density and long cycle life. The state estimation is directly related to the safe and efficient use of Libs. In our previous research, a Bat Algorithm-Extreme Learning Machine (BA-ELM) model was constructed and promising results were achieved for state of health (SOH) estimation. To increase the engineering application value of BA-ELM for SOH/state of charge (SOC) estimation, optimization mechanism of improving the global exploration and local exploitation capabilities of the model was studied. First, three improved methods including a conversion mechanism of Artificial Bee Colony algorithm employing bees and scout bees, a mechanism for adaptive adjustment of the search frequency of bats according to the Euclidean distance difference, and an adaptive inertia weight coefficient method were proposed to construct a novel Improved Bat Algorithm (IBA)-ELM model. Second, a battery life degradation test platform was built to obtain the degradation dataset for Libs. The variation rules of nine battery variables and cycles were revealed. Four high-quality SOH-related features were extracted. Thus, the SOH estimation performance of six models was evaluated. Third, a Libs charge–discharge dataset under driving conditions was collected and processed. Four models were trained based on 72,048 samples from four conditions. Further, SOC estimation tests were conducted on 35,071 samples from completely different conditions. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of SOH estimation by IBA-ELM decreased by 21.93 % and 23.79 %, respectively, compared to those by BA-ELM. The RMSE and MAE of SOC estimation decreased by more than 10 %. Error, radar, and violin plots demonstrate that the IBA-ELM could accurately estimate SOH/SOC in larger scale datasets, simultaneously exhibiting better generalization performance.

Comparison of techniques based on frequency response analysis for state of health estimation in lithium-ion batteries

Frequency response analysis (FRA) methods are commonly used in the field of State of Health (SOH) estimation for Lithium-ion batteries (Libs). However, identifying their appropriate application scenarios can be challenging. This paper presents four FRA techniques, including electrochemical impedance spectra (EIS), mid-frequency and low-frequency domain equivalent circuit model (MLECM), distribution of relaxation time (DRT) and non-linear FRA (NFRA) technique. This paper proposes two estimation frameworks, machine learning and curve fitting, to be applied to each of the four techniques. Eight SOH estimation models are developed by linking the extracted feature parameters to the battery capacity variations. The paper compares the accuracy of estimation, estimation range, and other properties of the eight models. Application scenarios are identified for the techniques by using three classification methods: different estimation frameworks, frequency response linearity, and impedance technique. The results demonstrate that MLF is recommended for scenarios with a large amount of battery data, while CFF is recommended for scenarios with a small amount of data. NFRA could be applied to electric vehicle power batteries, while LFRA is recommended to be used for retired batteries. EIS method is recommended for complex and dynamic scenarios, while non-EIS method is recommended for scenarios that require high accuracy.

A novel approach for state-of-charge estimation of lithium-ion batteries by quasi-static component generation of ultrasonic waves

Lithium-ion batteries content complex internal components, such as porous media and electrolytes, which result in strong scattering and high attenuation of ultrasonic waves in these batteries. The low attenuative feature of the quasi-static components (QSCs) of ultrasonic waves offers great potential for nondestructive assessment of highly attenuating and porous materials. This paper presents an innovative approach for estimating the state-of-charge (SOC) of lithium-ion batteries using QSC of ultrasonic waves. Experimental results demonstrate a clear and repeatable linear relationship between the amplitudes of the generated QSC and the SOC of lithium-ion batteries. In addition, the relationships between different SOCs of the battery and the conventional linear ultrasonic parameters, second harmonic generation (SHG), and the QSC were compared to verify the improved sensitivity of the proposed approach. Notably, compared to linear ultrasonic features and the SHG, the generated QSC shows much higher sensitivity to the variations of SOC. We employ the phase-reversal method to further enhance the signal-to-noise ratio of measured QSC signals. The experimental results demonstrate that the proposed method exhibits a heightened sensitivity to changes in the SOC of batteries, resulting in significantly enhanced detection accuracy and resolution. This method effectively addresses the deficiencies observed in the current detection methods such as limited accuracy and sluggish response times. This method provides a new solution to overcome this challenge. Meanwhile, it also confirms that nonlinear ultrasound promises an alternative method for SOC assessment, providing a foundation for efficient and safe battery management practices.

An Equivalent Siding Mode Observer for Electric Vehicle Lithium Battery SOC Estimation

Accurate state of charge (SOC) estimation for lithium-ion batteries is essential to guarantee long-term stable operation of electric vehicles. In this study, an equivalent sliding mode observer (ESMO) is proposed to estimate the battery SOC. First, a sliding mode observer (SMO) was designed with Walcott-Zak structure to increase the sliding region. Next, a controlled equivalent function was used to replace sign function in the SMO, which can lessen chattering issue and increase system robustness. Furthermore, this study performs online parameter identification of a second-order resistance capacitor equivalent circuit model using the forgetting factor recursive least squares approach. Lastly, the experiments under dynamic current conditions were conducted to verify the proposed ESMO. The results show that the mean square error of the ESMO is decreased to 0.5%, which implies that the proposed ESMO can estimate the SOC with higher accuracy compared to the traditional SMO.

Fast State-of-Charge Estimation for Lithium-Ion Batteries Using a Simplified Electrochemical Model Without Initial State Restrictions

Fast State-of-Charge Estimation for Lithium-Ion Batteries Using a Simplified Electrochemical Model Without Initial State Restrictions