SOC estimation method based on IUPF algorithm and variable parameter battery model

作者：Liu, Xintian; Li, He; He, Yao; Zheng, Xinxin; Zeng, Guojian

来源：Journal of Southeast University(Natural Science Edition), 2018, 48(1): 54-62.

DOI：10.3969/j.issn.1001-0505.2018.01.009

• Least squares methods
• Measurement Noise
• Particle filter
• Scope of application
• State of charge
• Temperature range
• Thevenin models
• Variable parameters

Aiming at the problem that the parameters of commonly used battery model are fixed and the scope of application is limited, the variable parameter Thevenin model affected by the temperature and the state-of-charge (SOC) is established. The model parameters are identified by the design of experiment (DOE) method and the least squares method. To solve the problem that the estimation accuracy of the algorithm is affected when the system noise is larger, an improved unscented Kalman particle filter (IUPF) algorithm is proposed. The system state noise and the measurement noise are simultaneously introduced into the sample point, the noises are symmetrically sampled and imported into the process of the algorithm calculation to ensure the accuracy of the algorithm. The IUPF algorithm adopted based on variable parameter Thevenin model reduces the impacts of noises on the system estimation accuracy while ensuring the scope of the model. The experimental and simulation results show that the SOC estimation method based on IUPF algorithm and variable parameter battery model can keep a higher estimation accuracy over a large temperature range, while solving the problem that the scope of application is limited as well as keeping the accuracy of the model. Especially when the system state noise and measurement noise impact seriously, the accuracy of the model is improved, and the method has better robustness to the disturbance caused by the model parameters.

• 出版日期2018
• 单位合肥工业大学