State of charge estimation of lead-carbon batteries in actual engineering

Lead-carbon Batteries as an energy storage device, its state of charge is an important parameter of the entire battery energy storage system. This paper uses the Improved Thevenin model as the battery mathematical model, and establishes the state-space equations. First of all, it fits the function relationships between the parameters and the SOC. And then it establishes a set of equations combined with the electrical characteristics of the model. Finally it searches out the best parameter estimation according with unconstrained nonlinear optimization methods. It simulates the battery performance very well to use the Battery mathematical models and parameters, the error within 1%. This paper uses Kalman filter based on the wavelet transform estimation method to estimate the SOC, and it finally verifies this estimation method has a high accuracy of estimation, the error within 2%.

[1]  Jon L. Anderson,et al.  Advanced lead carbon batteries for partial state of charge operation in stationary applications , 2015, 2015 IEEE International Telecommunications Energy Conference (INTELEC).

[2]  Jonghoon Kim,et al.  Implementation of EKF combined with discrete wavelet transform-based MRA for improved SOC estimation for a Li-Ion cell , 2013, 2013 Twenty-Eighth Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

[3]  Ruben H. Milocco,et al.  Robust polynomial approach for state of charge estimation in NiMH batteries , 2012 .

[4]  Xiaosong Hu,et al.  Robustness analysis of State-of-Charge estimation methods for two types of Li-ion batteries , 2012 .

[5]  Jasim Ahmed,et al.  Algorithms for Advanced Battery-Management Systems , 2010, IEEE Control Systems.

[6]  Jonghoon Kim,et al.  Application of wavelet transform-based discharging/charging voltage signal denoising for advanced data-driven SOC estimator , 2015, 2015 IEEE Applied Power Electronics Conference and Exposition (APEC).

[7]  Guojun Li,et al.  State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting , 2015 .

[8]  Mansour Sheikhan,et al.  State of charge neural computational models for high energy density batteries in electric vehicles , 2012, Neural Computing and Applications.

[9]  Zheng Chen,et al.  State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering , 2013, IEEE Transactions on Vehicular Technology.

[10]  Guoqiang Hu,et al.  Distributed Control Scheme for Package-Level State-of-Charge Balancing of Grid-Connected Battery Energy Storage System , 2016, IEEE Transactions on Industrial Informatics.

[11]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .