Accurate State Of Charge Estimation Research Articles

Evaluation of Various Offline and Online ECM Parameter Identification Methods of Lithium-Ion Batteries in Underwater Vehicles.

For underwater vehicles, the state of charge (SOC) of battery is often used to guide the optimal allocation of energy. An accurate SOC estimation can improve work efficiency and reliability of underwater vehicles. Model-based SOC estimation methods are still mainstream routes used in practical applications. Hence, accurate battery models are highly desirable, which depends not only on the circuit structure but also on the circuit parameters. Four-parameter identification algorithms, offline mechanism-based and least squared (LS) methods, as well as online recursive least-squares with forget factor (FFRLS) and extended Kalman filter (EKF) methods were analyzed in terms of SOC estimation under three different conditions. The results revealed that in the case without any disturbance, the predicted SOCs based on four-parameter identification circuits fitted well with the reference. Moreover, it is remarkable that the LS offline methods work better than the FFRLS online routes. In addition, the robustness has also been accessed through the other two conditions, i.e., measurement data with disturbance and initial SOC value with deviation. The results showed that maximum errors of SOC estimation based on the EKF approach are significantly lower than those of the other methods, and the values are 0.51% and 0.20%, respectively. Thus, the circuit model based on the EKF parameter identification approach possessed a stronger anti-interference performance during the SOC estimation process. This research can provide corresponding theoretical support on ECM parameter identification for lithium-ion batteries in underwater vehicles.

An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries

Accurate state of charge (SOC) estimation of lithium-ion batteries by the battery management system (BMS) plays a prominent role in ensuring their reliability, safe operation, and acceptable durability in smart devices, electric vehicles, etc. In this paper, the effect of the training and testing working conditions on the accuracy of the SOC using a long short-term memory (LSTM) network is studied through transfer learning. Then, a relevant attention mechanism is introduced as a data optimizer for faster training of the LSTM network to establish a relevant LSTM (RLSTM). Finally, the SOCs estimated by the RLSTM are independently input with the working current to an extended Kalman filter (EKF) and a proposed squared gain EKF (SGEKF) method to iteratively denoise and optimize the accuracy of the final SOC under the three complex working conditions. The results show that the SOC estimation accuracy is influenced by the training and testing working conditions using the LSTM network, which provides a technique for accurate SOC estimation. Also, the established RLSTM network is computationally efficient for accurate SOC estimation. Moreover, the proposed hybrid RLSTM-SGEKF model has an overall maximum mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error values of 0.35299%, 0.0017448%, 0.41765%, and 2.34403%, respectively, under the three complex working conditions. The proposed hybrid RLSTM-SGEKF model is optimal, robust, and computationally efficient for accurate SOC estimation of lithium-ion batteries for real-time BMS applications.

A Hybrid Data-Driven Method for State-of-Charge Estimation of Lithium-Ion Batteries

As a portable energy storage system, lithium-ion batteries (LIBs) are widely used in wireless sensor networks, electric vehicles and other fields. To ensure the continuity of power supply, it is necessary to monitor the state of charge (SOC) of LIBs. However, due to the nonlinearity of battery operation, accurate SOC estimation has become a challenging task. In this paper, a SOC estimation method based on long-term short-term memory (LSTM) network and improved particle filter (IPF) is proposed, which maps the easily observed voltage, current and temperature to the target SOC. Firstly, through a layer of the LSTM network, the timing characteristics of the data are fully utilized to obtain the SOC variation trend of LIBs. Then, the noise variance adaptive algorithm and particle distribution optimization algorithm are introduced to improve the standard particle filter (PF). On this basis, the estimation results of the LSTM network are optimized by IPF. In addition, the performance of the proposed LSTM-IPF method is compared with other methods. The results show that the estimation performance of the proposed model is excellent, and the root mean squared error (RMSE) and maximum error (MAX) are controlled below 1% and 2% respectively, which meets the requirements of SOC estimation and verifies the effectiveness of the proposed method.

A Multi-Scale Time Method for the State of Charge and Parameter Estimation of Lithium-Ion Batteries Using MIUKF-EKF

Accurate state estimation is essential for the safe and reliable operation of lithium-ion batteries. However, the accuracy of the battery state estimation depends on the accuracy of the battery parameters. Because the state of charge (SOC) cannot be directly measured, estimation methods based on the Kalman filter are widely used. However, it is difficult to estimate SOC online and get high accuracy results. This article proposes a method for parameter identification and SOC estimation for lithium-ion batteries. Because the lithium-ion battery has slow-varying parameters (such as internal resistance, and polarization resistance), and the SOC has fast-varying characteristics, so a multi-scale multi-innovation unscented Kalman filter and extended Kalman filter (MIUKF-EKF) are used to perform online measurement of battery parameters and SOC estimation in this method. The battery parameters are estimated with a macro-scale, and the SOC is estimated with a micro-scale. This method can improve the estimation accuracy of the SOC in real-time. Results of experiments indicate that the algorithm has higher accuracy in online parameter identification and SOC estimation than in the dual extended Kalman filter (DEKF) algorithm.

A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF

Lithium-ion batteries have become the fastest-growing energy storage equipment available for extrinsic and intrinsic reasons. State of Charge (SOC) is one of the lithium-ion batteries' most critical performance indicators, reflecting the remaining capacity. An accurate and stable estimate of SOC is critical for any lithium-ion battery. This paper proposes a hybrid method to achieve stable and real-time battery SOC estimation at different temperatures, composed of an Improved Bidirectional Gated Recurrent Unit (IBGRU) network and Unscented Kalman filtering (UKF). The proposed method is experimentally validated using data from UDDS and US06 driving cycles. The verification results show that the method can adapt to various working conditions and obtain good estimation accuracy and robustness, with MAE and RMSE less than 0.83% and 1.12%, respectively. After transfer learning, the method can also be applied to new lithium-ion batteries and achieve good estimation performance at new temperature conditions. The maximum errors are 4.98% and 5.76% at 25 °C and −10 °C, respectively. Therefore, the IBGRU-UKF method can achieve a more accurate and stable SOC estimation with good expansion performance for different lithium-ion batteries.

State-of-charge estimation for lithium-ion battery during constant current charging process based on model parameters updated periodically

With online established battery model, model-based estimation method can track battery state-of-charge (SOC) precisely under dynamic conditions. Nevertheless, both recursive least square-based and filter-based methods cannot distinguish whether the voltage difference comes from SOC difference or internal resistance difference during constant current (CC) conditions, further leading to erroneously identified model parameters and inaccurate SOC estimation. To address this issue, a novel SOC estimation method during CC charging process by fusion of global optimization algorithm and Kalman filter family algorithm is developed in this paper. Firstly, some key parameters that are helpful for initialization and lower/upper bounds setting for global optimization method are extracted from electric vehicles’ driving process. Secondly, considering the shortcomings in traditional global optimization methods, including possible premature convergence, slow search speed in the late stage and relatively large computational cost, an improved particle swarm optimization is designed to periodically update model parameters during CC charging process. With obtained model parameters, SOC is further tracked via extended Kalman filter (EKF). The verification results based on experimental data demonstrates that the developed method can significantly weaken the strong cross-interference between model parameters and SOC, further achieving much more accurate SOC estimation than existing dual/joint EKF during CC charging process.

State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter

Accurate estimation of the state of charge plays a very important role in ensuring the safe and effective operation of battery lithium-ion batteries and is one of the most important state parameters. However, the estimation method of state of charge has various limitations, so it is of great significance to improve the accuracy and calculation speed of the method. In this article, we propose an improved recurrent neural network model to estimate lithium-ion battery state of charge. Simple recurrent units are used to replace the traditional recurrent neural network basic unit or long short-term memory unit, and the computation speed is improved by implementing parallel processing. Finally, the prediction results of the model are fed into an unscented Kalman filter module to remove the interference of noise on the prediction. This article studies the prediction accuracy and speed of Samsung INR 18650-20R and INR 18650-25R under various ambient temperatures, initial state of charge values, and electric vehicle drive cycles. The results show that the proposed method can obtain accurate state of charge estimation results in the INR 18650-20R data set. For different temperatures and initial SOC, the root mean square error is less than 0.015 and 0.016, and the prediction speed is about 30% higher than that of long short-term memory. In the INR 18650-25R data set, for three different driving cycles, the root mean square error is less than 0.034, and the average test speed is about 2.7s, which proves the effectiveness of this method in estimating accuracy and speed.

Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network

State of charge (SOC) is the most important parameter in battery management systems (BMSs), but since the SOC is not a directly measurable state quantity, it is particularly important to use advanced strategies for accurate SOC estimation. In this paper, we first propose a bidirectional long short-term memory (BiLSTM) neural network, which enhances the comprehensiveness of information by acquiring both forward and reverse battery information compared to the general one-way recurrent neural network (RNN). Then, the parameters of this network are optimized by introducing a Bayesian optimization algorithm to match the data characteristics of lithium batteries with the network topology. Finally, two sets of lithium battery public data sets are used to carry out experiments under different constant temperature and variable temperature environments. The experimental results show that the proposed model can effectively fit the actual measurement curve. Compared with traditional long short-term memory network (LSTM) and BiLSTM models, the prediction accuracy of the Bayes-BiLSTM model is the best, with a root mean square error (RMSE) within 1%, achieving a better ability for capturing long-term dependencies. Overall, the model exhibits high accuracy, adaptability, and generalization for the SOC estimation of batteries with different chemical compositions.

Adaptive robust unscented Kalman filter-based state-of-charge estimation for lithium-ion batteries with multi-parameter updating

State-of-charge (SOC) estimation is one of the key technologies for the development and application of battery management system (BMS). To achieve high accuracy in SOC estimation, which can adapt to any conditions, an adaptive robust unscented Kalman filter (ARUKF) based on multi-parameter update is proposed herein. First, the DP battery model is applied to replicate the dynamic behavior of lithium-ion batteries, and the model parameters are identified online by the improved forgetting factor recursive least square (IFFRLS). Then, the Institute of Geodesy and Geophysics (IGGIII) weight function is introduced into unscented Kalman filter (UKF) as the form of a robust factor to adjust the weights of observation residuals, and receding horizon based adaptive filter tuning is employed to obtain the time-varying noise covariance. Subsequently, joint estimation of battery model parameters and SOC with capacity updating is implemented which can suppress the system disturbance caused by outliers, mistuning, unknown initial value, and aging. Finally, the superior performance of accuracy and convergence is verified by cycle and aging tests. The maximum absolute error of SOC estimation under the proposed method is kept within 2%. The convergence speed of SOC estimation utilizing ARUKF is nearly 80 s faster than that of robust UKF (RUKF) and UKF under a 20% SOC initial error.

SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture

State-of-charge (SOC) is critical to the safe operation and energy management of electric vehicles. Data-driven SOC estimation algorithms all require a period of data to ensure convergence of the estimation results and cannot accurately estimate the SOC values near the starting point. We propose a SOC estimation method based on the U-Net architecture that can handle variable-length input data and output equal-length SOC estimation results, including accurate SOC of the starting point. Symmetric padding convolutional layer was proposed to address the boundary effect of Convolutional Neural Networks (CNN) and improve the accuracy of SOC estimation at the edges. We also propose a total variation loss function, which improves the stability of the estimation only by optimizing the loss function without increasing the model complexity, and significantly reduces the maximum error. The model was trained using dynamic drive cycle data at five constant temperatures, and the model has high accuracy at both constant and variable temperature conditions. The proposed method can estimate the SOC at constant temperatures with mean absolute error (MAE) within 1.1% and root-mean-square error (RMSE) within 1.4%. This method also can estimate SOC at varying temperatures with MAE within 1.5% and RMSE within 1.8% under different driving conditions.

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.

Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion

Open circuit voltage (OCV) is crucial for battery degradation analysis. However, high-precision OCV is usually obtained offline. To this end, this paper proposes a novel self-evaluation criterion based on the capacity difference of State of Charge (SoC) unit interval. The criterion is integrated into extended Kalman filter (EKF) for joint estimations of OCV and SoC. The proposed method is evaluated in a typical application scenario, energy storage system (ESS), using a LiFePO4 (LFP) battery. Extensive experimental results show that a more accurate OCV and incremental capacity and differential voltage (IC-DV) can be achieved online with the proposed method. Our method also greatly improves the accuracy of SoC estimation at each SoC point where the maximum estimation error of SoC is less than 0.3%.

State estimation of lithium-ion batteries based on strain parameter monitored by fiber Bragg grating sensors

Multisensory and artificial intelligence approaches are key tools to achieve intelligent management of future battery systems. Strain monitoring using optical fiber sensors is an important role of multi-sensing in batteries. In this paper, the strain of batteries is monitored by fiber Bragg grating sensors, and the strain data are used to estimate the state of charge (SoC) and state of health (SoH) of batteries. A Kalman filtering (KF) model is proposed for SoC estimation based on strain signal of cells. Moreover, this work employs an artificial neural network (NN) for SoC estimation based on the strain data. The experimental data are acquired from commercial lithium-ion cells under two operating conditions. The KF model is established based on multiple regression between strain and SoC, which shows good performance in estimation for the static cycles. For NN estimators, input variables with strain parameter can enhance the accuracy of SoC estimation. A KF model based on the peak strain is developed to estimate the capacity degradation of battery, and the results show that strain can be used as an indicator to estimate SoH. The results present an encouraging outcome that SoC estimation can be achieved using non-electrical parameters solely, and the strain signal can also be used as an auxiliary parameter to improve the accuracy of SoC estimation. This new exploration provides a basis for multi-parameter cooperative estimation of battery state in the future battery system with a multisensory approach.

A novel adaptive H‐infinity filtering method for the accurate SOC estimation of lithium‐ion batteries based on optimal forgetting factor selection

AbstractAccurate estimation of the state of charge (SOC) of lithium‐ion batteries is quite crucial to battery safety monitoring and efficient use of energy; to improve the accuracy of lithium‐ion battery SOC estimation under complicated working conditions, the research object of this study is the ternary lithium‐ion battery; the forgetting factor recursive least square (FFRLS) method optimized by particle swarm optimization (PSO) and adaptive H‐infinity filter (HIF) algorithm are adopted to estimate battery SOC. The PSO algorithm is improved with dynamic inertia weight to optimize the forgetting factor to solve the contradiction between FFRLS convergence speed and anti‐noise ability. The noise covariance matrixes of the HIF are improved to realize adaptive correction function and improve the accuracy of SOC estimation. To verify the rationality of the joint algorithm, a second‐order Thevenin model is established to estimate the SOC under three complex operating conditions. The experimental results show that the absolute value of the maximum estimation error of the improved algorithm under the three working conditions is 0.0192, 0.0131, and 0.0111, respectively, which proves that the improved algorithm has high accuracy and offers a theoretical basis for the safe and efficient operation of the battery management system.

Study on the Relationship Between Open-Circuit Voltage, Time Constant And Polarization Resistance of Lithium-Ion Batteries

Battery management system (BMS) is an important role in battery applications. In BMS, the accurate estimation of the state of charge (SOC) of lithium-ion batteries is most important. Open circuit voltage (OCV) is very important for the accurate estimation of SOC. In order to obtain accurate SOC, the relationship between OCV and SOC requires real-time and accuracy. Due to the difference in lithium-ion concentration and battery internal resistance in the lithium-ion battery, OCV has the characteristics of relaxation. It is necessary to study the relaxation behavior of battery OCV. In this paper, the OCV behavior is studied and focuses on the relationship of the time constant and polarization resistance with SOC during relaxation. The results show that, when the SOC is 30%–100%, the time constant and polarization resistance of lithium-ion batteries are the smallest, the performance is the most stable, and the SOC estimation accuracy is the highest. When the battery is overcharged or SOC is low, the performance of the battery is obviously unstable. Finally, it is proposed that the SOC range of the battery can be roughly estimated by calculating the time constant, and the current aging degree of the battery can be judged.

Adaptive filter algorithms for state of charge estimation methods: A comprehensive review

Battery <span>management system is compulsory for long life and effective utilization of lithium ion battery. State of charge (SOC) is key parameter of battery management system. SOC estimation isn’t an easy job. Effective estimation of SOC involves complex algorithms where. Conventional methods of SOC estimation does not take continuously varying battery parameters into account thus large noise in both voltage and current signal are observed resulting in inaccurate estimation of SOC. Therefore, in order to improve the accuracy and precision in SOC estimation, improved adaptive algorithms with better filtering are employed and discussed in this paper. These adaptive algorithms calculate time varying battery parameters and SOC estimation are performed while bringing both time scales into account. These time scales may be slow-varying characteristics or fast-varying characteristics of battery. Some experimentations papers have proved that these adaptive filter algorithms protect battery from severe degradation and accurately calculate battery SOC. This paper reviews all previously known adaptive filter algorithms, which is the future of the electrical vehicles. At the end, a comparison is built based upon recent papers which talked on SOC at their differences in control strategies, efficiency, effectiveness, reliability, computational time and cost</span>.

A Cell-to-Pack State Estimation Extension Method Based on a Multilayer Difference Model for Series-Connected Battery Packs

Battery degradation and cell inconsistency challenge the existing state estimation methods, which often ignore the influence of cell inconsistency or suffer from heavy computation complexity. To address the difficulty of state estimation by cell inconsistency and realize joint estimation of the state-of-charge (SOC) and capacity for series-connected battery packs, a novel cell-to-pack state estimation extension method based on a multilayer difference model (MDM) is investigated. The proposed extension method can efficiently realize accurate SOC and capacity estimation for a battery pack based on the existing estimation algorithms for a single cell. Two state-difference estimators for SOC and capacity differences are constructed and realized separately through adaptive extended Kalman filter and recursive least squares algorithm. Considering that time-varying characteristics of states and state differences are different, the MDM estimator runs in multiple timescales. Based on battery pack cycling experiments, the cell-to-cell consistency evolution during aging is revealed. The proposed MDM’s accuracy, efficiency, and adaptability are verified through the experiments of a series-connected battery pack under different dynamic conditions. The proposed method also shows adaptability to various battery temperatures and different cell inconsistencies. The maximum SOC root-mean-squared error and capacity average error for battery pack during aging are 2.35% and 3.02%, respectively.

Kalman filter and classical Preisach hysteresis model applied to the state of charge battery estimation

The goal of this work is first to include a hysteresis model in the classical equivalent circuit model (ECM) for a battery system and then to improve the estimation of the state of charge (SoC) by applying the Extended Kalman Filter (EKF). The hysteretic behavior of the open circuit voltage (OCV) is modelled with the classical Preisach model used for magnetic materials. The construction of the Preisach operator is made by means of the Everett function identified from experimental data which only involve the charging curves of the battery. Thus, a significant reduction in the time necessary to obtain the measurements is achieved. The model is assessed with some laboratory experiments performed on a lithium-ion battery and the results show that with this procedure hysteresis is very well reproduced, even when interior loops are present. In addition, the use of the EKF allows us to eliminate the measurements noise and ensure the accuracy of SoC estimation. The high computational efficiency and precision of the method, joined to the limited computational resources needed for the numerical implementation, make it particularly suitable for real-time embedded battery management system (BMS) applications. In addition, the proposed methodology is well-adapted to any battery type, independently of the SoC-OCV profile of the hysteresis cycle.

Estimation of battery open-circuit voltage and state of charge based on dynamic matrix control - extended Kalman filter algorithm

State of Charge (SOC) estimation is one of the most important functions of the battery management system for new energy vehicles. Extended Kalman Filter (EKF) algorithm has been widely used in SOC estimation of lithium-ion batteries (LiBs). However, the model parameters in the SOC estimation algorithm change with the aging of the battery, which makes EKF unable to obtain accurate estimation results. Because of these defects, a battery model is built based on the second-order RC equivalent circuit model. The parameters of the LiB model are identified by an off-line test and the battery simulation model is calibrated. Then, based on the simulation model, the influence of Open Circuit Voltage (OCV) deviation on SOC estimation accuracy of the EKF algorithm is quantitatively analyzed. The results show that the deviation of OCV will affect the accuracy of SOC estimation by the EKF algorithm. When the OCV deviation reaches 15 mV, the SOC estimation error will reach 5%. Subsequently, a method of updating model parameters based on the Dynamic Matrix Control (DMC) algorithm is proposed. And the DMC-EKF algorithm is used to estimate OCV and SOC. The results show that after the DMC algorithm is used to linearize the RC network, the identified OCV parameter deviation is <10 mV, and the SOC deviation estimated by the DMC- EKF algorithm is <5%, which can meet the application requirements. The EKF algorithm can estimate the SOC more accurately after updating the OCV by the DMC algorithm. Compared with EKF algorithm and UKF algorithm without online OCV updating, DMC-EKF algorithm reduces the maximum deviation of SOC estimation by at least 2%. The proposed DMC-EKF algorithm has good accuracy and robustness.

State-of-charge estimation using data fusion for vanadium redox flow battery

Accurate estimation of the state of charge (SOC) is important for preventing overcharge and overdischarge of vanadium redox flow batteries (VRFBs). In this paper, we propose a data-fusion (DF) method to improve the accuracy of SOC estimation for VRFBs by combining two different SOC estimation results. First, the extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) were used to estimate the SOC according to the equivalent circuit model (ECM) of the VRFB. Thereafter, the SOC estimation results of the EKF and AEKF were utilized separately as the basic estimation of the DF method to obtain two different DF results, which can be explicitly divided into DF-EKF and DF-AEKF. The ECM parameters were extracted using the recursive least squares (RLS) algorithm in real time. Subsequently, an experiment involving a hybrid pulse discharge was conducted to validate the proposed method. Here, the root-mean-square error (RMSE) and mean absolute error (MAE) are used as statistical methods for outcome evaluation. Compared with the RMSE and MAE (0.0012, 0.0010, and 0.0010 and 0.0009) of the EKF and AEKF, the RMSE and MAE of the DF-EKF decreased by 33.3%, 20%, and 36.3% and 22.2% to 0.0008 and 0.0007, respectively. The same downward trend also occurs in DF-AEKF. The RMSE of the results of DF-AEKF decreased by 33.3% and 20% to 0.0008, and the MAE decreased by 45.5% and 33.3% to 0.0006, respectively. The results show that the proposed DF method exhibited a high fidelity and accuracy in estimating the SOC of the VRFB.