Lithium-ion Battery SOC Research Articles

Digital twin model for lithium-ion battery SOC estimation in battery swapping station

Digital twin model for lithium-ion battery SOC estimation in battery swapping station

Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU

If the charging state of the lithium-ion battery can be accurately predicted, overcharge and overdischarge of the battery can be avoided, and the service life of the battery can be improved. In order to improve the prediction accuracy of SOC, a prediction method combined with convolutional layer, multi-head attention mechanism and gated cycle unit is proposed to extract data feature information from different dimensions of space and time. Using the data set of the University of Maryland, we simulated the battery in real vehicle operating conditions at different temperatures (0 °C, 25 °C, 45 °C). The test results showed that the mean absolute error, root mean square error and maximum prediction error of the model were 0.53%, 0.67% and 0.4% respectively. The results show that the model can predict SOC accurately. At the same time, the comparison with other prediction models shows that the prediction accuracy of this model is the highest.

Collaborative evaluation of SoC, SoP and SoH of lithium-ion battery in an electric bus through improved remora optimization algorithm and dual adaptive Kalman filtering algorithm

Many countries are switched to electric vehicles (EVs) for public transportation, which increases the adoption of electric buses. Batteries are the key component in battery-operated electric vehicles and must be monitored for the system to operate efficiently. This work has proposed a joint estimation method to examine the battery states. The lithium-ion battery (LiB) is initially designed through a first-order RC equivalent (FO-RC) circuit. To optimize the modelling parameters of a battery, an improved remora optimization algorithm (IROA) is proposed in this work. After detecting the optimum values for the parameters, the SoC is evaluated by a dual adaptive Kalman filtering algorithm (DAKF). Then the SoH is estimated based on the predicted SoC of a battery, whereas the SoP is evaluated by considering current and voltage constraints during battery operation. After that, the battery's remaining useful life (RUL) is examined based on the estimated SoC. The proposed work is implemented on the MATLAB platform, and the results will be validated under varying operating conditions. The comparative analysis shows that the IROA provides optimum parameters near the actual parameters' actual values, thereby improving the prediction accuracy of battery states.

State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment

The extended Kalman filter (EKF) has low estimation accuracy for lithium-ion battery SOC under non-Gaussian noise interference. To solve this problem, an adaptive EKF based on maximum correlation-entropy criterion (MCC-AEKF) was proposed. In this algorithm, the maximum correlation entropy criterion is used to obtain accurate estimation results by replacing the overall solution with the local optimal solution. Meanwhile, the adaptive covariance matrix is established to update the process noise variance to reduce the SOC estimation error. MCC-AEKF were used to estimate the SOC based on two kinds of battery data. The experiments show that, under non-Gaussian noise interference and at operating temperature of 25 °C, compared with EKF and MCC-EKF, the estimation accuracy of MCC-AEKF improved by 80.3% and 24.2% separately for battery 1. For battery 2, its estimation accuracy improved by 72.6% and 22.0% separately, and are also the highest at 10 °C and 40 °C. Given wrong initial SOC values, MCC-AEKF can converges to real value within 30s. All results present the good accuracy and robustness of the proposed method under different cases.

MACHINE LEARNING AND IOT-BASED LI-ION BATTERY CLOUD MONITORING SYSTEM FOR 5G BASE STATIONS

With the accelerated construction of 5G and IoT, more and more 5G base stations are erected. However, with the increase of 5G base stations, the power management of 5G base stations becomes progressively a bottleneck. In this paper, we solve the problem of 5G base station power management by designing a 5G base station lithium battery cloud monitoring system. In this paper, first, the lithium battery acquisition hardware is designed. Second, a new communication protocol is established based on Modbus. Third, the windows desktop upper computer software and the cloud-based monitoring system are designed. Finally, this paper designs the improved ResLSTM algorithm which is fused with ResNet algorithm based on Stacked LSTM. The algorithm designed in this paper is tested in comparison with SVM and LSTM. The performance of the algorithm designed in this paper is better than SVM and LSTM. Furthermore, the communication test, as well as the training and testing of the ResLSTM algorithm are outstanding. The 5G base station lithium-ion battery cloud monitoring system designed in this paper can meet the requirements. It has great significance for engineering promotion. More importantly, the ResLSTM algorithm designed in this paper can better guide the method of lithium-ion battery SOC estimation.

Joint Estimation of SOC and Available Capacity of Power Lithium-Ion Battery

Temperature has an important effect on the battery model. A dual-polarization equivalent circuit model considering temperature is established to quantify the effect of temperature, and the initial parameters of the model are identified through experiments. To solve the defect of preset noise, the H-infinity filter algorithm is used to replace the traditional extended Kalman filter algorithm, without assuming that the process noise and measurement noise obey Gaussian distribution. To eliminate the influence of battery aging on SOC estimation, and considering the different time-varying characteristics of the battery states and parameters, the dual time scale double H-infinity filter is used to jointly estimate the revised SOC and available capacity. The simulation results at two temperatures show that, compared with the single time scale, the double time scale double H-infinity filter reduces the simulation time by nearly 90% under the premise that the accuracy is almost unchanged, which proves that the proposed joint estimation algorithm has the dual advantages of high precision and high efficiency.

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium-ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF-AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞-CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium-ion battery SOC. FF-AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD-CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF-AGLS and H∞-SVD-CKF was able to accurately track model parameters and SOC with a strong degree of robustness.

A Novel Joint Support Vector Machine - Cubature Kalman Filtering Method for Adaptive State of Charge Prediction of Lithium-Ion Batteries

Accurate estimation of SOC of lithium-ion batteries has always been an important work in the battery management system. However, it is often very difficult to accurately estimate the SOC of lithium-ion batteries. Therefore, a novel joint support vector machine - cubature Kalman filtering (SVM-CKF) method is proposed in this paper. SVM is used to train the output data of the CKF algorithm to obtain the model. Meanwhile, the output data of the model is used to compensate the original SOC, to obtain a more accurate estimate of SOC. After the SVM-CKF algorithm is introduced, the amount of data needed for prediction is reduced. By using Beijing Bus Dynamic Stress Test (BBDST) and the Dynamic Stress Test (DST) condition to verify the training model, the results show that the SVM-CKF algorithm can significantly improve the estimation accuracy of Lithium-ion battery SOC, and the maximum error of SOC prediction for BBDST condition is 0.800%, which is reduced by 0.500% compared with CKF algorithm. The maximum error of SOC prediction under DST condition is about 0.450%, which is 1.350% less than that of the CKF algorithm. The overall algorithm has a great improvement in generalization ability, which lays a foundation for subsequent research on SOC prediction.

Least squares support vector machine for state of charge estimation of lithium-ion battery using gray wolf optimizer

An accurate SOC estimation can ensure the effective and normal operation of the battery. To this end, a method for estimating the SOC of lithium-ion battery based on least squares support vector machine (LSSVM) is proposed, and the gray wolf optimizer (GWO) algorithm is used to further optimize the model parameters to overcome the non-linearity and unevenness of the actual working condition. Influence of interference factors, and then obtain accurate SOC estimation results. The optimized LSSVM is verified by collected data at different temperatures, the results shows the proposed optimization model is greatly improved compared to before optimization, the model accuracy is improved by about 35%.

A new method of unscented particle filter for high-fidelity lithium-ion battery SOC estimation

A new method of unscented particle filter for high-fidelity lithium-ion battery SOC estimation

An estimation method for lithium-ion battery SOC of special robots based on Thevenin model and improved extended Kalman

An estimation method for lithium-ion battery SOC of special robots based on Thevenin model and improved extended Kalman

Estimation of lithium-ion battery SOC based on GWO-optimized extreme learning machine

Estimation of lithium-ion battery SOC based on GWO-optimized extreme learning machine

An Artificial Potential Field-Based Lithium-ion Battery SOC Equilibrium Method in Electric Vehicles

A novel cell equilibrium algorithm which is used for battery state of charge equilibrium of battery pack is proposed in this paper. Cell equilibrium algorithm is a key technology for lithium-ion battery pack in the energy storage systems. In order to eliminate the imbalance of battery state of charge, it’s generally controlled by adjusting the charging current. The artificial potential field can construct a virtual force function, which provides the mapping between state of charge deviation and charging current. Therefore, an artificial potential field-based equilibrium method to balance battery state of charge was proposed in this paper. By reasonably distributing the charging current of the battery, the equilibrium of the battery state of charge can be achieved. A laboratory testbed has been built to verify the effectiveness of the proposed method compared with conventional method. Experiment results analyzed the energy consumption and the convergence speed of state of charge deviation under artificial potential field control with different parameters.

Lithium-ion Battery SoC Estimation of an Order-reduced Electrochemical Model Using SMO-URL in EVs

Lithium-ion Battery SoC Estimation of an Order-reduced Electrochemical Model Using SMO-URL in EVs

A nonlinear observer approach of SOC estimation based on hysteresis model for lithium-ion battery

In this paper, a state of charge U+0028 SOC U+0029 estimation approach for lithium-ion battery based on equivalent circuit model and the input-to-state stability U+0028 ISS U+0029 theory has been proposed. According to the electrochemical performance of lithiumion battery, the equivalent circuit model with two RC networks is established, which includes hysteresis characteristic in inner electrochemical response process. The nonlinear relation between open circuit voltage U+0028 OCV U+0029 and SOC is obtained from a rapid test. Exponential fitting method is used to identify the parameters of the model. A novel state observer based on ISS theory is designed for lithium-ion battery SOC estimation. The designed observer is tested on AMESim and Simulink co-simulation. The simulation results show that the proposed method has a high SOC estimation accuracy with an error of about 2 percent.

Estimating Method for Lithium Ion Battery State of Charge based on Twin Support Vector Regression

SOC is an important parameter of battery, which occupies an important position in BMS. This paper presents a modified method-Twin Support Vector Regression,based on SVR.To pick up current,voltage,ampere-hour, overfitting SOC of lithium ion battery (LIB), and making comparasions with traditional SVR. The results of simulation and experient prove that T-SVR has a higher modeling accuracy, a higher prediction accuracy, and it has a faster computing speed than SVR. TSVR is fit for prediction of SOC of LIB.

A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation

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