State Of Charge Estimation Performance Research Articles

State of charge estimation for lithium-ion batteries with pre-set convergence time based on a comprehensive unobservable model

State of charge (SOC) is a critical metric for assessing the remaining capacity of lithium-ion batteries, while convergence time is an essential indicator for evaluating the performance of SOC estimation. To reduce the convergence time of SOC estimation, a novel strategy has been proposed. Initially, an accurate and comprehensive model is employed for lithium-ion batteries, which includes the nonlinear behavior of the open circuit voltage (OCV) with respect to SOC. However, the linear component of this model is unobservable. Then, the nonlinear relationship between OCV and SOC is approximated using a first-order piecewise fitting function. This approach ensures accuracy while reducing computational complexity compared to non-linearization methods. Next, the unobservable model is decomposed into observable and unobservable subsystems, addressing the challenge of designing observers for unobservable models. Furthermore, a finite time observer is designed based on the observable subsystem, enabling SOC estimation to be achieved within any pre-set convergence time. Finally, theoretical analysis and extensive experimental results validate the feasibility and reliability of the proposed estimation strategy, demonstrating its superiority over traditional methods.

Estimating state of charge of cylindrical lithium-ion cells using multiple random convolutional kernel transform and low-frequency stress waves

Lithium-ion batteries (LIBs) represent an energy storage technology increasingly utilized across various fields. To maintain the safety and reliability of a LIB, monitoring the LIB state of charge (SoC) is essential. Currently, there are limited studies on SoC estimation of cylindrical LIBs using the low-frequency stress wave technique due to its limited sensitivity to internal changes of LIBs during the charging/discharging process. Here, we present the first demonstration of SoC estimation of cylindrical LIBs using low-frequency stress wave (5-50kHz). We adopt Pearson correlation coefficients (PCCs) to select sampling points relevant (>0.5) to the SoC from the response stress wave signals and then apply the multiple random convolutional kernel transform (Multi-Rocket) model to extract four types of features for SoC estimation. The SoC estimation performance of the developed method achieves similar accuracy in both original and down-sampled (low-resolution) signals, with estimation root mean square error of about 0.7%, 1.4%, 3.5%, and 5.3%, respectively in four scenarios. This new method based on low-frequency stress waves enables a drastic cost reduction compared to expensive high-frequency ultrasound methods, paving the way for low-cost, real-time SoC monitoring with excellent accuracy.

Research on precise lithium battery state of charge estimation method based on CALSE-LSTM model and pelican algorithm

This paper presents an innovative fusion model called “CALSE-LSTM,” which integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), self-attention mechanisms, and squeeze-and-excitation attention mechanisms to optimize the estimation accuracy of the State of Charge (SoC). The model incorporates battery historical data as input and employs a dual-attention mechanism based on CNN-LSTM to extract diverse features from the input data, thereby enhancing the model's ability to learn hidden information. To further improve model performance, we fine-tune the model parameters using the Pelican algorithm. Experiments conducted under Urban Dynamometer Driving Schedule (UDDS) conditions show that the CALSE-LSTM model achieves a Root Mean Squared Error (RMSE) of only 1.73 % in lithium battery SoC estimation, significantly better than GRU, LSTM, and CNN-LSTM models, reducing errors by 31.9 %, 31.3 %, and 15 %, respectively. Ablation experiments further confirm the effectiveness of the dual-attention mechanism and its potential to improve SoC estimation performance. Additionally, we validate the learning efficiency of CALSE-LSTM by comparing model training time with the number of iterations. Finally, in the comparative experiment with the Kalman filtering method, the model in this paper significantly improved its performance by incorporating power consumption as an additional feature input. This further verifies the accuracy of CALSE-LSTM in estimating the State of Charge (SoC) of lithium batteries.

A comparative study on state-of-charge estimation for lithium-rich manganese-based battery based on Bayesian filtering and machine learning methods

In this paper, a comparative study on the state of charge (SOC) estimation of the lithium-rich manganese-based battery (LRMB) has been conducted by systematically considering the equivalent circuit model (ECM), aging state and algorithms. The results show that the first-order RC model combined with the Extended Kalman filter (EKF) is more suitable for the SOC estimation of the LRMB when the battery decays less severely. The maximum value of root mean square error of SOC estimation error no exceeds 1.6 % under various dynamic operating conditions, which can well meet the practical application. Besides, the first-order model based EKF algorithm occupies least computing resource and simultaneously possesses the best convergence velocity against the initial SOC value deviation. When the LRMB suffers a severe aging, the SOC estimation performance decreases significantly with the max error exceeding 6 %, to overcome this, a new algorithm combined deep learning and Extend Kalman filter (EKF) is proposed, which exhibit high SOC estimation accuracy with the RMSE less than 0.16 %. In summary, a systematic study about the SOC estimation of LRMB has been carried out, which can provide great guidance for the future engineering application of LRMB.

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.

Research on state-of-charge estimation of lithium-ion batteries based on an improved gas-liquid dynamics model

Improving the accuracy of state of charge (SOC) is beneficial for the safety and service life of batteries. The battery analysis model has become one of the research hotspots due to its clear physical significance and visibility of parameters. Based on analytical modeling methods, an improved gas-liquid dynamics (GLD) model of the lithium-ion battery (LIB) is proposed, which excels in simulating lithium-ion diffusion behavior and terminal voltage rebound characteristics under the open circuit state of LIBs. The parameter mapping relationships between the LIB and GLD model are improved and estimation equations of SOC, terminal voltage, and open circuit voltage are deduced according to GLD state equations. Based on these estimation equations and dual extended Kalman filter, an efficient real-time SOC estimation method for LIBs is developed. The performance of SOC estimation method is verified by LIBs data sampled by onboard BMS under five working conditions. Results show that the proposed method has strong robustness of the initial error of SOC = ±20 % which is eliminated within 15 s, and has a reliable performance of SOC estimation against the current and terminal voltage noises.

Open circuit voltage - state of charge curve calibration by redefining max–min bounds for lithium-ion batteries

The relationship between open circuit voltage (OCV) and state of charge (SoC) is essential for SoC estimation of lithium-ion batteries, which can be secured by either low-current OCV test or incremental OCV test, with incremental OCV test demonstrating better results. Nevertheless, low current always leads to a discharge capacity that is inconsistent with maximum available capacity, which is a key parameter in SoC estimation. Therefore, the resultant OCV-SoC curve fails to provide a consistent SoC range and thus affects SoC estimation. In this paper, OCV-SoC curves obtained from low-current OCV tests are calibrated by redefining max–min bounds to improve SoC estimation accuracy. Max–min bounds of SoC are redefined by measuring, calculating and resetting upper and lower cut-off voltages of the OCV-SoC curve. Based on second-order RC model, model parameters are identified online and adaptive square-root unscented Kalman filter (ASRUKF) algorithm is introduced to perform SoC estimation, which is proved to be precise and adaptive. The calibrated OCV-SoC curve achieves an overall better SoC estimation performance than that obtained from the low-current OCV test and incremental OCV test, which is validated by experiments of LiNiMnCoO2 (NMC) batteries at 0°C and 25°C.

Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost

Lithium batteries have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe utilization of these batteries necessitates accurate state-of-charge (SOC) estimation. In this study, we propose a hybrid model combining temporal convolutional network (TCN) and eXtreme gradient boosting (XGBoost) to investigate the nonlinear and evolving characteristics of batteries. The primary goal is to enhance SOC estimation performance by leveraging TCN’s long-effective memory capabilities and XGBoost’s robust generalization abilities. We conducted experiments using datasets from NASA, Oxford, and a vehicle simulator to validate the model’s performance. Additionally, we compared the performance of our model with that of a multilayer neural network, long short-term memory, gated recurrent unit, XGBoost, and TCN. The experimental results confirm that our proposed TCN–XGBoost hybrid model outperforms the other models in SOC estimation across all datasets.

Research on a High-Precision State-of-Charge Estimation Method Based on Forgetting Factor Recursive Least Squares and Adaptive Extended Kalman Filter Applied to LiFePO4 Battery

The state-of-charge (SOC) estimation accuracy is closely associated with the estimation method and the battery parameter identification performance. The battery parameter identification method based on forgetting factor recursive least squares (FFRLS) has the advantages of high parameter identification accuracy and fast dynamic response speed. On this basis, the performance of two SOC estimation methods, the extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) are compared and studied. The results show that AEKF has better steady-state and dynamic SOC estimation performance, but the estimation accuracy and dynamic response performance are still not objective. To further improve the performance of SOC estimation, a joint SOC estimation method based on FFRLS-AEKF is proposed, and the SOC estimation experimental results with FFRLS-AEKF and AEKF are conducted. The experimental results show that the proposed joint SOC estimation method based on FFRLS-AEKF has a better steady-state and dynamic performance of SOC estimation. The maximum absolute error of the proposed algorithm is 4.97%. As the battery working time increases, the SOC estimation accuracy continues to converge to the true value, and the average absolute error is reduced to 2.5%. The proposed method and theoretical analysis are proven to be correct and feasible.

Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction

Due to the possible interference of non-Gaussian noise in Li-ion battery management systems, there is no guarantee of reliable accuracy when using the extended Kalman filter (EKF) algorithm for battery state of charge (SOC) estimation. A novel EKF algorithm based on the adaptive maximum correntropy criterion (AMCCEKF) is proposed to enhance the robustness of SOC estimation in the paper. The Gaussian kernel function is chosen as the cost function to reconstruct the state error variance and the measurement noise variance. And a kernel width adaptive update strategy is designed to address the constraints of fixed kernel width on SOC estimation performance. In addition, an open circuit voltage (OCV) correction strategy based on terminal voltage innovation and OCV-SOC curve gradient is designed to reduce the impact of OCV error caused by non-Gaussian noise on SOC estimation. Incorporating the AMCCEKF method and the OCV correction strategy, a novel estimation method based on the dOCV-AMCCEKF is offered to perform the SOC estimation. Simulation results under different operating conditions and temperatures show that the maximum absolute error of the dOCV-AMCCEKF method is close to 0.5%, which verifies it can reduce the SOC estimation error in a non-Gaussian noisy interference environment compared with the EKF based method.

State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries

To ensure the secure and healthy usage of lithium-ion batteries, it is necessary to accurately estimate the state of charge (SOC) in battery management systems. The development of deep learning (DL) provides a new solution for battery SOC estimation. However, the directly measured physical quantities contain less useful information and have low estimation accuracy. This paper proposes a method of integrating the mechanism knowledge of the battery domain into the DL framework. Firstly, the simplified electrochemical model is utilized to obtain the mechanism-related physical variables to expand the input of the DL model. Secondly, the long short-term memory (LSTM) network is used with the Bayesian optimization, and the variables with high correlation are identified. The best SOC estimation performance can be obtained by adding all the selected highly-correlated variables to the input for training together. The results show that the proposed method can improve the SOC estimation performance with only a slight increase in computation cost. Finally, other DL models are utilized to further validate the effectiveness, to reveal the universality. These results show that the performance of the DL model can be effectively improved by using the knowledge of the battery domain.

Prediction of charge-discharge behavior and state of charge estimation for tri-electrode rechargeable zinc-air flow batteries

An effective state of charge (SOC) estimation is essential for the development of a battery management system. At present, the provision of a SOC estimation for zinc-air flow batteries (ZAFBs) is still at its early stage of development. This work sets out to develop the SOC estimator for ZAFBs. The estimator is based-on a linear parameter varying (LPV) model integrated with an extended Kalman filter (EKF). The LPV model is constructed from multiple linear time-invariant (LTI) models with battery current and SOC as scheduling parameters. It is observed that the response data for ZAFBs have an exceptionally flat profile related to SOC change; dynamic differentiation only occurs when SOC is almost depleted. For this reason, the estimation of SOC converges to the true value when SOC is near depletion. In this work, it is shown that by appropriate tuning, the SOC estimation performance of the LPV model combined with EKF performs well as the absolute errors of soc. estimation lie under 2 % after true SOC convergence. The LPV model with the EKF algorithm is also compared with the Luenberger observer (LO). The proposed estimator can surpass the LO estimator. Overall, this SOC estimator provides a systematic way to fulfill the requirements of a battery management system.

State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles under Extreme Operating Temperatures Based on an Adaptive Temporal Convolutional Network

The accurate estimation of state of charge (SOC) under various conditions is critical to the research and application of batteries, especially at extreme temperatures. However, few studies have examined the SOC estimation performance of estimation algorithms for several types of batteries under such conditions. In this study, a new method was derived for SOC estimation and a series of experiments were conducted covering five types of lithium-ion batteries with three kinds of cathode materials (i.e., LiFePO4, Li(Ni0.5Co0.2Mn0.3)O2, and LiCoO2), three test temperatures, and four real driving cycles to verify the proposed method. The test temperatures for battery operation ranges from −20 to 60 °C. Then, an adaptive machine learning (ML) framework based on the deep temporal convolutional network (TCN) and Coulomb counting method was proposed, and the structure of the estimation model was designed through the Taguchi method. The accuracy and generalizability of the proposed method were evaluated by calculating the estimation errors and their standard deviations (SDs), its average errors showed a decline of at least 49.66%, and its SDs showed a decline of at least 45.88% when compared to four popular ML methods. These traditional ML methods performed poor accuracy and stability at extreme temperatures (−20 and 60 °C) when compared to 25 °C, while the proposed adaptive method exhibited stable and high performances at different temperatures.

SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output

The state of charge (SOC) estimation of lithium-ion battery (LIB) based on recurrent neural network (RNN) has been a popular research due to its suitability for time series data prediction. However, there are significant output fluctuations in solo network, which lead to unstable SOC estimation performance. To solve this problem, this paper proposes a novel long short-term memory recurrent neural network (LSTM-RNN) with extended input (EI) and constrained output (CO) for battery SOC estimation, named EI-LSTM-CO. For the network input, an additional slow time-varying information sliding window average voltage is introduced to enhance the ability of network to map the nonlinear characteristics of the battery and reduce the output SOC fluctuations. In terms of the network output, a state flow strategy based on the Ampere-hour integration (AhI) is designed to constrain the variation between adjacent output SOCs of the network to smooth the network output and further improve the SOC estimation performance. In the experiments, the LiFePO4 battery datasets at various temperatures are used to validate the SOC estimation performance and generalization ability. In particular, the root mean square error (RMSE) and the maximum error (MAXE) of the proposed method on unknown data are less than 1.3% and 3.2% respectively.

State of charge estimation for lithium-ion batteries using gated recurrent unit recurrent neural network and adaptive Kalman filter

The state of charge (SOC) is one of the most important monitoring states for the battery management system. It is still a challenge to estimate the battery SOC accurately and stably. The conventional model-based filtering methods may cause inaccurate SOC estimation in application due to the high dependence on accurate battery model. And the emerging methods based on machine learning often have the problem of estimated SOC fluctuation when the current fluctuates greatly. To solve these problems, this paper proposes a robust and efficient combined SOC estimation method, GRU-AKF, which combines the gated recurrent unit recurrent neural network (GRU-RNN) and the adaptive Kalman filter (AKF). Firstly, the GRU-RNN is used to establish a mapping model between the battery measured variables and SOC in the full temperature range, and to achieve the SOC pre-estimation. Then, an AKF is employed to filter the output SOC of the GRU-RNN for reducing the fluctuation in pre-estimated SOC. Finally, the accurate and stable estimated SOC is obtained. In the experiments, the LiFePO4 battery datasets at various temperatures are used to validate the SOC estimation performance and generalization ability. Specifically, the root mean square error is less than 1.3% and 5.8%, and the maximum error is less than 2.2% and 7.7% for the unknown data at positive and negative temperatures, respectively. By comparing with other methods of the same type, the proposed method is demonstrated to be superior in SOC estimation performance and computation efficiency, especially it has excellent performance in initial SOC convergence ability.

Elaborated EECM parameter identification reflecting adaptive weight for achieving a high‐accuracy SOC estimator

AbstractIn this paper, a combined method is proposed to improve the accuracy of an electrical equivalent circuit model (EECM) and state‐of‐charge (SOC) estimation in situations where a lithium‐ion battery is frequently charged and discharged. To calculate the terminal voltage in the dynamic region, where charging and discharging are frequently repeated at 0.1 s intervals, a specific region is determined and historical weight is selected, based on the amount of charge in that region, to reflect the model parameters. The historical weights are applied to the charging and discharging parameters to improve the estimated performance of the EECM in estimating the terminal voltage of the battery. Also, the improved EECM is applied to the extended Kalman filter to improve the performance of SOC estimation. A 21,700 cylindrical lithium nickel manganese cobalt oxide‐type battery was used for verification, and the parameters required for modeling were extracted through experiments that were conducted to determine electrical characteristics. This paper compared two models not considering the history of charging or discharging characteristics with a model considering history to verify the accuracy of voltage and SOC estimation results. The result shows that the voltage and SOC estimation errors of the proposed combined method are the lowest in the three cases.

A Comparative Study on Different Online State of Charge Estimation Algorithms for Lithium-Ion Batteries

With an accurate state of charge (SOC) estimation, lithium-ion batteries (LIBs) can be protected from overcharge, deep discharge, and thermal runaway. However, selecting appropriate algorithms to maintain the trade-off between accuracy and computational efficiency is challenging, especially under dynamic load profiles such as electric vehicles. In this study, seven different widely utilized online SOC estimation algorithms were considered with the following goals: (a) to compare the accuracy of the different algorithms; (b) to compare the computational time in the simulation. Since the 2-RC battery model is highly accurate and not very computationally complex, it was selected for implementing the considered algorithms for the model-based SOC estimation. The considered online SOC estimation performance was evaluated using measurement data obtained from experimental tests on commercial lithium manganese cobalt oxide batteries. The experimental analysis consisted of a dynamic current profile comprising a worldwide harmonized light vehicle test procedure (WLTP) cycle and constant current discharging pulses. In addition, the performance of the considered different algorithms was compared in terms of estimation error and computational time to understand the challenges of each algorithm. The results indicated that the extended Kalman filter (EKF) and sliding mode observer (SMO) were the best choices because of their estimation accuracy and computation time. However, achieving the SOC estimation accuracy depended on the battery modeling. On the other hand, the estimated SOC root means square error (RMSE) using a backpropagation neural network (BPNN) was less than that using a Luenberger observer (LO). Moreover, with the advantages of BPNNs, such as no need for battery modeling, the estimation error could be further reduced using a large size dataset.

State of charge estimation of lithium-ion battery under time-varying noise based on Variational Bayesian Estimation Methods

Non-linear filters such as UKF, CKF are often used to estimate the State of Charge (SoC) of lithium batteries, but the premise is that the noise is Gaussian noise, but the actual noise cannot be Gaussian noise, such as the process noise or measurement noise changed with time, which will affect the estimated effect. In this paper, a second-order battery model is established, and the Kalman filter is used to identify the model parameters, and then three methods are used to obtain the SOC-OCV mapping curve. Next, to improve the performance of SoC estimation under the influence of time-varying measurement noise or process noise, the idea of Variable Bayesian iteration is introduced into the nonlinear filter to obtain the Variable Bayesian unscented Kalman filter and the Variable Bayesian square-root cubature filter. Then the two methods are used to estimate the SoC under four working conditions, two temperatures, and two kinds of covariance time-varying noise. Experimental results show that the proposed method can effectively improve the estimation performance of SoC, and the SOC-OCV mapping curve used in this paper is more stable.

Improved wild horse optimizer with deep learning enabled battery management system for internet of things based hybrid electric vehicles

Internet of Things (IoT) become an emergent platform in wireless technologies in design of electric vehicles (EVs) and hybrid electric vehicles (HEVs). Dynamic energy storage systems, batteries can be damaged due to overcharging/discharging and their mass penetration deeply affects the grid. For circumventing the likelihood of damage, the EVs and HEVs require an accurate state of charge (SOC) estimation approach for improving the lifetime and safety. An efficient battery management system (BMS) remains a challenging problem in HEVs, commonly utilized to indicate the battery state-of-charge (SOC). As over-charge and over-discharge outcome from predictable damage to the batteries, precise SOC estimation model is needed for HEVs. In this aspect, this study presents an improved wild horse optimizer with deep learning enabled battery management system (IWHODL-BMS) for IoT based HEVs. The presented IWHODL-BMS employs attention based bidirectional long short-term memory (ABiGRU) approach to accurately estimate SOC in HEVs. For enhancing SOC estimation performance of the ABiGRU technique, the IWHO algorithm is utilized as hyperparameter optimizer. The application of the ABiGRU model results in simpler and accurate representation of the input. A comprehensive simulation result portrayed the enhanced outcomes of the IWHODL-BMS model over the other methods under varying measures.

State of charge estimation of Power lithium-ion battery based on an Affine Iterative Adaptive Extended Kalman Filter

State of charge (SOC) is a very important parameter for power lithium-ion battery in battery operation, but it cannot be measured directly, so it needs to be accurately estimationed. Considering the time-varying characteristics of the model parameters of the power lithium-ion battery, an online identification algorithm called Adaptive Forgetting Factor Recursive Augmented Least Squares (AFFRALS) is proposed. It can obtain a more accurate model compared with the offline method (fixed model parameters) by considering that the noise of power lithium-ion battery system is commonly non-Gaussian white noise in practice, which is different from the existing equivalent circuit models. Then, an Affine Iterative Adaptive Extended Kalman Filter (AIAEKF) method is proposed to deal with the non-Gaussian white noise and accelerate the convergence rate of the estimated results when the initial SOC value is wrong. Experiments demonstrate the effective of the online identification method as well as the SOC estimation method. This method shows a faster convergence rate and better SOC estimation performance than the traditional Extend Kalman Filter (EKF) method. The RMSE of the SOC estimation results is less than 0.023 except for 0 °C, which rarely occurs in practice.