Abstract
To accurately estimate the state of charge (SOC) of lithium-ion power batteries in the event of errors in the battery model or unknown external noise, an SOC estimation method based on the H-infinity filter (HIF) algorithm is proposed in this paper. Firstly, a fractional-order battery model based on a dual polarization equivalent circuit model is established. Then, the parameters of the fractional-order battery model are identified by the hybrid particle swarm optimization (HPSO) algorithm, based on a genetic crossover factor. Finally, the accuracy of the SOC estimation results of the lithium-ion batteries, using the HIF algorithm and extended Kalman filter (EKF) algorithm, are verified and compared under three conditions: uncertain measurement accuracy, uncertain SOC initial value, and uncertain application conditions. The simulation results show that the SOC estimation method based on HIF can ensure that the SOC estimation error value fluctuates within ±0.02 in any case, and is slightly affected by environmental and other factors. It provides a way to improve the accuracy of SOC estimation in a battery management system.
Highlights
The state of charge (SOC) estimation is the most basic function of battery status monitoring.SOC represents the remaining power of the battery, and accurate SOC estimations are of great significance in improving the battery efficiency and safety performance [1,2]
Domestic and foreign scholars have been researching different SOC estimation methods for lithium-ion batteries, which can be divided into four major categories [3]: estimation methods based on characteristic parameters of lithium-ion batteries, estimation methods based on ampere-hour integration, data-driven estimation methods, and model-based estimation methods
The introduction of genetic crossover factors enriches the diversity of population particles, solves the problem that the classical particle swarm optimization (PSO) algorithm is prone to fall into the local optimality, improves the global searching capability of the parameter identification algorithm, and further improves the fitting degree of the battery model
Summary
The state of charge (SOC) estimation is the most basic function of battery status monitoring. The model-based SOC estimation method is self-adjusted by the difference between the battery’s measured voltage and the model’s output voltage This is done to overcome the error caused due to uncertainty, and achieve the purpose of improving the estimation accuracy of the SOC. To solve the above problems, the H-infinity filter (HIF) algorithm based on the minimum-maximum criterion is adopted in this study instead of the traditional EKF algorithm This algorithm takes into account the time-varying element of battery parameters, and does not require the details of process noise and measurement noise. Chen et al [26] used the HIF algorithm to estimate the SOC of lithium-ion batteries, and the method was tested by real-time experimental data of batteries It ignores the measurement noises and random disturbances.
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