Estimation Of Lithium-ion Batteries Research Articles

A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries

In the quest for clean and efficient energy solutions, lithium-ion batteries have emerged at the forefront of technological innovation. Accurate state-of-charge (SOC) estimation across a broad temperature range is essential for extending battery longevity, and enduring effective management of overcharge and over-discharge conditions. However, prevailing challenges persist in achieving precise SOC estimates and generalizing across a wide temperature range, particularly at lower temperatures. Our comparative analysis reveals that, while a single-layer bidirectional LSTM model with a self-attention mechanism achieves remarkable SOC estimation accuracy at room temperature, the intricacies of SOC estimation at lower temperatures necessitate the incorporation of more hidden layers and more complex network architecture to capture intricate features influencing battery dynamics. Hence, we propose a deep learning model, based on convolutional neural networks integrating bidirectional long short-term memory and self-attention mechanism (CNN-Bi-LSTM-AM), specifically designed to tackle the challenges of achieving accurate SOC estimations across a wide temperature range. The proposed model demonstrates proficiency in capturing both spatial and temporal dependencies critical for lithium-ion battery SOC estimation. Furthermore, the integration of a self-attention mechanism enhances the model’s adeptness to discern pertinent features and patterns within the dataset, thereby improving its overall performance and robustness, even in sub-room temperature environments.

Support vector regression-based state of charge estimation for batteries: cloud vs non-cloud

Embracing the potential of cloud technology in the field of electric vehicle advancements, this paper explores the application of support vector regression (SVR) for accurate state of charge (SOC) estimation of lithium-ion batteries in various computational landscapes. This study aims to scrutinize and compare the performance of SOC estimation, with a specific focus on precision, computational efficiency, and execution speed. The investigation is conducted across diverse environments, including a traditional non-cloud setup and two cloud-based platforms-a standard cloud environment employing Amazon web services (AWS) EC2 servers and an enhanced configuration utilizing the MATLAB production server. The investigation not only emphasizes the effectiveness of cloud integration but also provides valuable insights into the strengths and weaknesses of the proposed methodology. The experimental results contribute to a nuanced understanding of the methodology’s performance, shedding light on its potential implications for advancing electric vehicle technologies. This study thus extends its significance beyond technical considerations, providing a broader perspective on its relevance to global electrification initiatives.

Study on the estimation of the state of charge of lithium-ion battery

Lithium-ion battery (LIB) occupies a major position in rechargeable batteries. However, if the state of charge (SOC) estimation of LIB is not accurate, it will lead to the decline of battery life. SOC is often estimated by the extended Kalman filter (EKF) algorithm, but this method depends on the accuracy of model parameters. The sampling point interval time can affect the accuracy of SOC estimation. In this paper, the first-order RC model parameters are identified by two methods and the SOC is estimated by the EKF algorithm. Using this method, the battery parameters are received more accurate and the SOC estimation error is smaller. When the sampling interval is 0.03 s, the SOC estimation error is the smallest. And the second-order RC model can receive the same result. When the sampling interval is 0.03 s, the SOC estimation error of the 1RC-1 method is less than 1.4 %, the SOC estimation error of the 1RC-2 method is less than 0.017, and the root mean square error (RMSE) is 0.0087 and 0.01084. The conclusion is also used in the second-order RC model and contributes to the improvement of the SOC estimation accuracy. An effective method to estimate the battery SOC range is also proposed. This paper provides an effective method for obtaining more accurate SOC.

State of charge estimation of electrical vehicle battery by Using Box Particle Filter Signal Tracking Method

ABSTRACT This paper concentrates on the effective study of the online state of charge (SoC) estimation of lithium-ion batteries used in electric vehicles (EVs). Box Particle filters (BPF) are commonly used in the estimation of multivariate systems under noisy environments. In this study, a Joint-estimator is proposed which uses the forgetting factor recursive least square (FF-RLS) method for the 1-RC battery model online parameter estimation and the lithium-ion battery SoC is estimated using battery terminal voltage signal tracking with (BPF). The implemented method is compared with a Sequential Importance Resampling Particle Filter (SIR-PF) which shows that the proposed BPF has reduced the number of particles and computational time and maintained the efficiency comparable to the SIR-PF. The BPF performance is further evaluated at assorted voltage signal tracking accuracy, the number of boxes and the initial dimension of the boxes. The BPF method requires less number of boxes as compared to the SIR-PF with compatible efficiency. Therefore, BPF can be used as an alternative to the complex algorithms used in the SoC estimation in battery management systems (BMS) of electric vehicles.

The Estimation of Battery State of Charge using Corny Network

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various conventional computation and intelligent computation. Consequently, some existing methods performed battery SOC estimation using conventional and intelligent computation have not very accurate to predict the SOC battery characteristics. There some drawbacks in employment deep learning to estimate SOC battery, such as complicated algorithm or network, over fitting and so on. The proposed method, the Corny architecture has narrow layers design. This design has low cost computation and prevent over fitting. The result shows the accuracy of method is very high. The predicted and targeted values are almost merged in a single line. The RMSE and MAX error indexes are very low. That the accuracy of the model is acceptable. The electric vehicle battery can estimate to life longer and more reliable to perform mobility task. Finally, this method also show the accuracy of estimation SOC battery of electric vehicle can be solved by narrow learning layers.

A Bayesian optimized machine learning approach for accurate state of charge estimation of lithium ion batteries used for electric vehicle application

In recent times, battery technology used in electric vehicles has drawn numerous researchers' attention. Monitoring the battery condition, particularly the State of charge, is required to ensure the battery's safe and reliable performance. Despite the fact that many SOC estimation methods have been proposed, further research is necessary to identify an approach that adapts versatile lithium-ion battery chemistries. In the recent study it has been demonstrated that Machine Learning approaches have better prediction accuracy compared to conventional methods. To maximize the performance of Machine learning models, it is imperative to select the optimal hyperparameters and employ appropriate input parameters. At present, researchers employ, established heuristics methods to select hyperparameters, which may involve manual tuning or exhaustive search techniques such as random search and grid search. These techniques make the models less accurate and inefficient. In this paper, a systematic, automated process for selecting hyperparameters with a Bayesian optimization algorithm is proposed. In addition, along with the battery parameters (voltage, current and temperature), vehicle velocity, road condition, motor characteristics and environmental conditions are used as the input parameters for accurate SOC prediction. The highly correlated input features are selected through the MRMR algorithm. The performance of six ML algorithms, namely SVM, ANN, GPR, Ensembler, linear regression and Decision Tree, is tested and validated with and without hyperparameter tuning for different data sets. The experimental results demonstrate that the hyperparameter-tuned model outperforms the standard model in estimating the State of charge (SOC). In addition, the model's estimation error is <1 % across a range of vehicle velocity, road condition, motor torque, battery voltage, current, and temperature.

State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is crucial for the operation and maintenance of new energy electric vehicles. To address this current problem, an improved hybrid neural network model for SOH prediction based on a sparrow search algorithm (SSA) optimized convolutional bi-directional long short-term memory neural network (CNN-Bi-LSTM) is proposed. Firstly, by analyzing the battery aging data, several feature indicators with highly correlated battery life degradation are constructed. Secondly, the CNN-Bi-LSTM model is used to extract the battery aging data features and the latent timing laws. Finally, the SSA optimizes the parameters to improve the model accuracy. Experimental results based on the NASA-Pcoe battery dataset show that the SSA-CNN-Bi-LSTM model outperforms other models, and the root-mean-square errors of the SOH prediction results are all less than 0.6 %. It indicates that the proposed SSA-CNN-Bi-LSTM model is capable of predicting SOH accurately and with high precision.

Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-Ion Battery

Abstract Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality requirements and poor model generalizability. This paper proposes a comparison-transfer learning approach with cyclic synchronization to estimate the state of health of lithium-ion batteries. First, a cyclic synchronization method based on the Bezier curve fitting algorithm is introduced to synchronize the data obtained at different charge–discharge cycles of the lithium-ion battery, facilitating input to the model. Second, a comparison-transfer network using the Pearson correlation coefficient is proposed to transfer knowledge from the source dataset to predict the target dataset under different environmental temperatures. By training a pre-trained model on the source dataset and utilizing the correlation coefficient to analyze the similarity between the source and target datasets, the accumulated knowledge in the source dataset can be effectively utilized to enhance prediction performance on the target dataset. In the experiments, the proposed method is validated using the lithium-ion battery aging public datasets. The experimental results demonstrate that the proposed approach achieves superior prediction performance in the case of small-sample sizes, exhibiting higher accuracy and stability compared to traditional deep learning methods.

Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments

During the driving process of electric vehicles, the ambient temperature exhibits diverse variations with regional characteristics. To achieve robust state of charge (SOC) estimation for lithium-ion batteries under various varying temperature environments, this paper proposes an enhanced model-based closed-loop SOC estimation approach. First, beginning with a mechanistic analysis of batteries, the traditional second-order equivalent circuit model is enhanced by incorporating critical solid-phase diffusion effects during battery operation. Furthermore, utilizing data collected from multiple constant temperature environments, the complete enhanced battery model that accounts for the influence of current rates across a wide temperature range is constructed. Subsequently, under environments of different varying temperature settings, we design a series of complex operation experiments to verify the accuracy and generalizability of the established battery model. Meanwhile, a high-performance adaptive diagonalization of matrix cubature Kalman filter is introduced to address the challenge of fluctuating sampling noises in battery operation. Finally, the robustness and generalization of the proposed SOC estimation method are verified in multiple complex operating experiments under varying temperatures with non-Gaussian noise interferences and with non-full charging schemes. Remarkably, the proposed approach consistently delivers high-precision SOC estimation results across all scenarios, maintaining root mean square error and mean absolute error below 1.5%.

A Bayesian Optimized Deep Learning Approach for Accurate State of Charge Estimation of Lithium Ion Batteries Used for Electric Vehicle Application

A Bayesian Optimized Deep Learning Approach for Accurate State of Charge Estimation of Lithium Ion Batteries Used for Electric Vehicle Application

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

Dual-task Learning for Joint State-of-Charge and State-of-Energy Estimation of Lithium-ion Battery in Electric Vehicle

Dual-task Learning for Joint State-of-Charge and State-of-Energy Estimation of Lithium-ion Battery in Electric Vehicle

Online State-of-Charge Estimation of Lithium-Ion Battery using a Fault Tolerant and Noise Immune Threefold Modified Adaptive Extended Kalman Filter

Online State-of-Charge Estimation of Lithium-Ion Battery using a Fault Tolerant and Noise Immune Threefold Modified Adaptive Extended Kalman Filter

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

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

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

Towards fast embedded moving horizon state-of-charge estimation for lithium-ion batteries

For state-of-charge (SOC) estimation with high precision, moving horizon estimation (MHE) has recently emerged as a competitive alternative to various Kalman filters or observers due to its significantly improved accuracy and robustness. However, MHE demands considerably higher computational complexity, preventing its deployment on a low-cost embedded microcontroller. To address this challenge, we propose a fast moving horizon SOC estimation algorithm to retain the benefits of MHE at a vastly reduced computational cost. In particular, we consider joint SOC and parameter estimation for an SOC-dependent equivalent circuit model subject to model mismatch. The proposed fast joint MHE (jMHE) algorithm performs a fixed number of Gauss–Newton (GN) iterations to approximate the fully converged solution. At each iteration, the GN Hessian matrix is factorized by exploiting its block tridiagonal structure to construct computationally efficient forward–backward recursions. To further speed up computations, another fast jMHE algorithm wtih an Event-Triggered Relinearization strategy (jMHE-ETR) is proposed to avoid refactorizing the GN Hessian matrix at each iteration. Using experimental datasets under different operating conditions, it is verified on both laptop and Raspberry Pi microcontroller that compared to the conventional optimal MHE solved with an off-the-shelf solver, the proposed fast jMHE and jMHE-ETR algorithms both have a slight loss of performance (still significantly better than extended Kalman filtering) while reducing its computational cost by an order of magnitude.

Open access dataset, code library and benchmarking deep learning approaches for state-of-health estimation of lithium-ion batteries

Great progress has been made in deep learning (DL) based state-of-health (SOH) estimation of lithium-ion batteries, which helps to provide recommendations for predictive maintenance and replacement of lithium-ion batteries. However, despite the abundance of articles, few open-source codes are publicly available. While there are several public datasets, they tend to be more oriented toward simulating laboratory environments rather than real-world usage scenarios. Moreover, they solely provide raw data without any corresponding preprocessing codes, resulting in inconsistencies in preprocessing methods across different papers. These reasons lead to unfair comparisons and ineffective improvements. In response to these problems, this paper publishes a large-scale lithium-ion battery run-to-failure dataset, consisting of 55 batteries, and provides a unified data preprocessing method. Besides, we comprehensively evaluate 5 well-known DL-based models to provide benchmark research. To be specific, first, the existing DL-based SOH estimation methods are reviewed in detail. Second, we provide a comprehensive evaluation of DL-based models on 2 large-scale datasets, including 100 batteries, with 3 input types and 3 normalization methods. Third, we make the complete evaluation codes and dataset publicly available for better comparison and model improvement. Fourth, we discuss future DL-based SOH estimation, including unsupervised learning, transfer learning, interpretability, and physics-informed machine learning. We emphasize the importance of open-source code, provide baseline estimation errors (error upper bounds), and discuss existing issues in this field. The code library is available at: https://github.com/wang-fujin/SOHbenchmark.

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

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

Differential current in constant-voltage charging mode: A novel tool for state-of-health and state-of-charge estimation of lithium-ion batteries

Compared with extensive discussions on constant current (CC) charging for lithium-ion batteries, the probe into constant voltage (CV) charging is currently insufficient. Analogous to the differential analysis of the voltage curve in CC charging, this paper introduces the differential current curve (dQ/dI curve) in CV charging and uses it as the feature to identify battery states. The differential curve is qualitatively interpreted with an equivalent circuit model, and the relationship between the dQ/dI value and the battery states is fitted with the Gaussian process regression (GPR) model. With a total of 4836 sets of CV charging data, an excellent state of health (SOH) estimation with a mean absolute error (MAE) of 0.18 % can be achieved by using the first 190 data points of the dQ/dI curve as the model input. For state of charge (SOC) estimation, a brand-new experimental work is introduced by adjusting the cutoff voltage of the previous CC charging to generate a benchmark database illustrating the relation between the SOC value at the initial CV charging and the subsequent current curve. Through GPR, the SOC prediction MAE is reduced to about 0.88 %, which confirms that the dQ/dI curve is a promising candidate for battery state estimation.

Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

Accurate estimation of the State of Charge (SoC) plays a vital role in ensuring the efficient and safe operation of lithium iron phosphate (LFP) batteries. However, the flat open circuit voltage (OCV) curve of the LFP battery implies a low sensitivity to SoC, which results in large SoC estimation errors in the presence of noisy terminal voltage measurements. To address this challenge, an SoC estimation methodology utilizing an ultrasonic reflection response model is proposed, which is the first methodology regarding highly accurate and robust ultrasonic model-based SoC estimation under dynamic load profiles. Since ultrasound waves enable non-destructive acquisition of battery internal physical property changes directly associated with SoC, the ultrasonic battery near-surface reflection feature is extracted and demonstrated to exhibit a highly linear correlation with and higher sensitivity to SoC. We pioneeringly construct an empirical differential ultrasonic model to describe how the ultrasonic feature depends on the SoC and dynamic current. The advantage of such an ultrasonic model is demonstrated by theoretical and experimental results of an Adaptive Extend Kalman Filter (AEKF) and an Adaptive H-infinity Filter (AHIF) under various dynamic load profiles and temperatures. The Root Mean Square Error (RMSE) for ultrasonic model-based SoC estimation remains at approximately 1% across all tests, reducing by 36.7% compared to the voltage model, which shows its great potential in accurate SoC estimation.