Battery SOC Estimation Research Articles

Battery SOC estimation based on UHIF algorithm for third-order circuit models

Battery SOC estimation based on UHIF algorithm for third-order circuit models

ANN approach to SOC estimation of Lithium-Ion Battery

ANN approach to SOC estimation of Lithium-Ion Battery

SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

SOC Estimation of Li-Ion Power Battery Based on Strong Tracking UKF with Multiple Suboptimal Fading Factors

SOC Estimation of Li-Ion Power Battery Based on Strong Tracking UKF with Multiple Suboptimal Fading Factors

An Electrochemical-Thermal Coupling Model Based on Two-Factor Parameter Modification for Lithium-Ion Battery

Abstract The accurate establishment of battery model can improve the design reliability and reduce the design risk, which provides an important basis for the research of battery. First, the key parameters of the Li-ion battery model are identified by the least square method based on the full-battery equivalent circuit model of the single-particle impedance spectrum, and the diffusion coefficient and exchange current density under different temperatures and SOC conditions are calculated. At the same time, the one-dimensional thermal rate model is used as the heat source of the three-dimensional model, and the mean temperature T of the three-dimensional model is calculated by using Fourier's law, and T is fed back to the one-dimensional model as the key parameter to modify the conductivity, diffusion coefficient, and exchange current density, and a semi-empirical electrochemical-thermal coupling model with two-factor parameter modification is established. Finally, the model is verified by the temperature field distribution and discharge voltage curve at different discharge rates. The maximum temperature difference is less than 3.1 °C, and the maximum voltage difference error is less than 0.131 V. The results show that the improved model can accurately reflect the influence of temperature on the model parameters, and has high accuracy in the estimation of battery terminal voltage and SOC.

Real-Time Online Estimation Technology and Implementation of State of Charge State of Uncrewed Aerial Vehicle Lithium Battery

The SOC estimation of UAV lithium batteries plays a crucial role in the mission planning and safe flight of UAVs. Aiming at existing UAV lithium battery SOC estimation problems, such as low estimation accuracy and poor real-time performance, a real-time online estimation scheme for UAV lithium battery SOC is proposed. A model-based approach is adopted to establish an SOC estimation model on the basis of the Thevenin equivalent circuit model, and a UAV lithium battery online monitoring device is developed to monitor the current and voltage of the UAV lithium battery in real time and import the data into the SOC estimation model in real time for calculation. Using the developed online monitoring device, the current and voltage of the lithium UAV battery are monitored in real time, and the data are imported into the SOC estimation model in real time for calculation, realizing the real-time online estimation of the SOC of the lithium UAV battery. The experimental results show that the program can realize the real-time online estimation of UAV lithium battery SOC, and the estimation error is less than 3%, which meets the requirements of online estimation accuracy of the UAV lithium battery SOC.

A switching gain adaptive sliding mode observer for SoC estimation of lithium-ion battery

A new state of charge (SoC) estimation method for lithium-ion battery that uses a Switching Gain Adaptive Sliding Mode Observer (SGASMO) is proposed. The purpose of SGASMO is to reduce the chattering of estimated results from the sliding mode observers (SMOs) and improve the estimation accuracy. First, the Dual Polarization (DP) equivalent circuit model is selected and its parameters are identified to provide a basis for the design of the new SMO. Second, based on the DP model, the nonlinear terminal sliding surface and continuous control law were introduced. And an improved switching gain equation was designed, which is adaptively adjusted according to the sliding mode surface equation. Thus, the SGASMO was realized, and the convergence of the proposed observer was proved by the Lyapunov stability theory. Finally, based on the test data of the self-built experimental platform, it is verified that the proposed SGASMO has less jitter in the estimated results and better estimation accuracy and robustness compared with the conventional SMOs and other types of mainstream improved SMOs.

Design and Analysis of Battery Management System for Electric Vehicles

Abstract: This article presents a design for both the hardware and software components of the BMS, enabling battery monitoring and management. The Battery Management System (BMS) is a fundamental component of electric vehicles, primarily utilized to ensure battery safety and enhance battery lifespan. The hardware component encompasses the design of voltage acquisition circuitry, second-order filtering circuitry, sampling and holding circuitry, CAN bus communication circuitry, and other relevant features. The software section comprises subroutines for battery information collection, equalization circuitry, SOC estimation, and other relevant features. The BMS developed in this study successfully collects voltage, temperature, current, and other relevant information, and accurately estimates SOC and other crucial parameters. Testing confirmed that the battery management system precisely collects battery voltage, current, and temperature information, while the SOC estimation achieves a relatively high degree of accuracy.

Online lithium battery SOC estimation based on adversarial domain adaptation under a small sample dilemma

Online lithium battery SOC estimation based on adversarial domain adaptation under a small sample dilemma

Time–Frequency Domain Deep Convolutional Neural Network for Li-Ion Battery SoC Estimation

Time–Frequency Domain Deep Convolutional Neural Network for Li-Ion Battery SoC Estimation

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

Design of Electric Vehicle Battery Management System

The Battery Management System (BMS) is a fundamental component of electric vehicles, primarily utilized to ensure battery safety and enhance battery lifespan. This article presents a design for both the hardware and software components of the BMS, enabling battery monitoring and management. The hardware component encompasses the design of voltage acquisition circuitry, second-order filtering circuitry, sampling and holding circuitry, CAN bus communication circuitry, and other relevant features. The software section comprises subroutines for battery information collection, equalization circuitry, SOC estimation, and other relevant features. The BMS developed in this study successfully collects voltage, temperature, current, and other relevant information, and accurately estimates SOC and other crucial parameters. Testing confirmed that the battery management system precisely collects battery voltage, current, and temperature information, while the SOC estimation achieves a relatively high degree of accuracy.

Performance comparison of lithium polymer battery SOC estimation using GWO-BiLSTM and cutting-edge deep learning methods

Performance comparison of lithium polymer battery SOC estimation using GWO-BiLSTM and cutting-edge deep learning methods

A Novel Method for SoC Estimation of Lithium-Ion Batteries Based on Kalman Filter in Electric Vehicle

In recent years, the energy crisis has become more and more serious. Li-ion batteries are used in grids because of their benefits such as contributing to the intermittent generation of renewable energy sources and stabilizing the grid. In addition, li-ion batteries are widely used in electric vehicles due to their long cycle life and high energy density. Li-ion battery state of charge (SoC) is an important indicator for safety. Therefore, the SoC estimation of li-ion batteries is important. Today, there are different methods to determine the state of the SoC in many applications. The traditional estimation method, the ampere-hour integration method and the coulomb counting method, has a cumulative error and cannot achieve good results in a working environment with Gaussian noise. For this purpose, in this study, firstly, the Thevenin equivalent model was created for battery SOC estimation, and then the Kalman filter algorithm was applied. Thus, the estimation error caused by Gaussian noise is eliminated. SoC estimation was simulated for the battery model created in the MATLAB/Simulink program using this method. Using these simulation results, the charge/discharge characteristics of the battery were obtained. However, the SoC estimation has been made for the charging and discharging processes of the battery. In the simulation, the charge value was recorded for 6 hours. The data recorded every 10 minutes gave results very close to the true value.

Online parameters identification and state of charge estimation for lithium-ion batteries based on improved central difference particle filter

Accurate SOC is of great significance to alleviate the endurance anxiety of electric vehicle drivers. To improve the accuracy of SOC estimation, a method of combining FFRLS and GPR for parameter identification and an improved particle filter for lithium battery SOC estimation are proposed. The proposed collaborative parameter identification method uses the forgetting factor recursive least squares (FFRLS) to identify the parameters in the middle and high SOC region. In the low SOC region, the parameters identified by the FFRLS are used as the training data of Gaussian process regression (GPR) for model parameter prediction to improve the parameter identification accuracy in the low SOC region; in the SOC estimation stage, the central difference Kalman filter is used to generate the importance density function to overcome the particle degradation, and the step size h of the central difference is calculated suboptimal to further improve the prediction accuracy. Finally, the proposed method is compared with other methods under different working conditions and different temperatures, and the effectiveness and adaptability of the proposed method are verified.

An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery

ABSTRACT For real-time monitoring and safe control of electrical vehicles, it is important to accurately estimate the state of charge of lithium-ion batteries. A combined data-driven modeling approach based on Least squares support vector machine based on particle swarm optimization and unscented Kalman filter is proposed to obtain a better state of charge estimation accuracy. In this article, least squares support vector machine is used to establish the nonlinear connection between current, voltage, and SOC, and the parameters of least squares support vector machine are optimized by particle swarm optimization to improve the accuracy of voltage estimation, and the state and measurement equations are established by Least squares support vector machine in unscented Kalman filter for SOC estimation. The experimental results show that the maximum voltage error for the voltage prediction made with the PSO optimized model is 0.5 V. The maximum SOC error under various working situations is similarly kept to 0.5%, which is a significant improvement compared to the traditional algorithm. The above data show that the PSO considerably increases the precision of the Least squares support vector machine, as well as the estimation accuracy of the voltage and SOC, demonstrating the effectiveness of the model.

An Adaptive Cutoff Frequency Design for Butterworth Low-Pass Filter Pursuing Robust Parameter Identification for Battery Energy Storage Systems

Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R0, R1, C1, and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively.

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.

Research on Estimation of State of Charge of Li-ion Battery based on Cubature Kalman Filter

Li-ion batteries are widely used in the new energy vehicle industry. To make the battery pack work safely and reliably, it is necessary to estimate the state of charge of battery (SOC) accurately. In this paper, the second-order Thevenin is used as the equivalent circuit model, and the parameters of the model are identified based on the experimental data of Hybrid Pulse Power Characterization (HPPC). In view of the shortcomings of the Extended Kalman Filter (EKF) algorithm and the Unscented Kalman Filter (UKF) algorithm, the Cubature Kalman Filter (CKF) algorithm is chosen to estimate the SOC in this paper. Three algorithms are estimated and verified under different operating conditions. This paper also compare the estimated results with the simulation data. The results showed that the accuracy of SOC-estimation is significantly improved by using the optimized CKF algorithm method. The maximum error of estimated SOC is kept within 4% and the average error is kept within 1%. Compared with other algorithms, it is greatly improved and satisfies the accuracy and real-time requirements of Li-ion battery SOC estimation, which provides a reference value for practical applications.

Joint state estimation of power battery based on UKPF

Accurate estimation of power battery state is an important function in battery management system. In order to accurately estimate power battery SOC and SOH and improve the performance of long-term estimation of battery SOC, a joint estimation method of power battery state based on UKPF was proposed in this paper. The particle filter algorithm was added on the basis of the unscented Kalman filter algorithm, and the particle filter algorithm was optimized by the unscented Kalman filter algorithm, which improved the particle degradation problem and improved the accuracy of battery state estimation. Based on the time scale transformation, the battery state estimation was completed, and the SOC and SOH were estimated at short and long time scales, respectively. The SOH estimation results were updated to the model parameters for SOC estimation. The results show that the joint estimation method can accurately estimate battery SOC and SOH with an error of less than 3%.