SOC Estimation Of Lithium-ion Battery Research Articles

ANN approach to SOC estimation of Lithium-Ion Battery

ANN approach to SOC estimation of Lithium-Ion Battery

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.

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

A grey wolf algorithm improved MSTUKF for lithium-ion battery SOC estimation

A grey wolf algorithm improved MSTUKF for lithium-ion battery SOC estimation

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.

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.

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

基于EKF与UKF的锂离子电池SOC估计

基于EKF与UKF的锂离子电池SOC估计

SOC Estimation of Lithium-Ion Battery by Extended Kalman Fixed Lag Smoother

SOC Estimation of Lithium-Ion Battery by Extended Kalman Fixed Lag Smoother

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

Letter to the editor re "Fractional modeling and SOC estimation of lithium-ion battery"

Letter to the editor re "Fractional modeling and SOC estimation of lithium-ion battery"

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.

SOC Estimation of Lithium-Ion Battery Packs Based on Thevenin Model

Due to the immeasurability of SOC in battery and inevitability of error in current collection, SOC estimation of Lithium-ion battery has become a focus of EV research. With Thevenin equivalent circuit model, this paper employs EKF algorithm to estimate SOC, which takes into consideration both precision requirement of the estimation and amount of computation involved in online estimation. Based on above-mentioned objectives and principles, a test platform composed of Digatron battery test system and thermostat was built. Experimental result has confirmed that the combination of EKF algorithm with Thevenin model can improve precision and reduce amount of computation.