Estimation of state of charge of lithium battery based on parameter identification of fractional order model

• Published in: Journal of physics. Conference series 2021-01, Vol.1774 (1), p.12049
• Main Authors: Shen, H X, Li, X, Chen, L, Xun, H H, Chen, W X
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Convergence; Equivalent circuits; Extended Kalman filter; Genetic algorithms; Least squares; Lithium; Lithium batteries; Lithium-ion batteries; Mathematical models; Model testing; Parameter identification; Physics; Rechargeable batteries; State of charge
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1774/1/012049

Accurate estimation of state of charge of lithium battery is one of the important performance parameters for safe and reliable operation of lithium battery. A fractional order second-order Thevenin equivalent circuit model was proposed by based on the improvement of the traditional Thevenin equivalent circuit model for accurately estimating the state of charge of lithium-ion battery. In order to overcome the shortcomings of the least square method easily enter into local convergence or even unable to converge, an adaptive genetic algorithm is proposed to identify the parameters of lithium battery model, and global parameter identification is carried out to improve the convergence of the algorithm. Matlab simulation shows that the parameters of fractional order model of the second-order Thevenin equivalent circuit identified by adaptive genetic algorithm are better than those of integer order model identified by least square method. Combined with extended Kalman filter, the estimation of state of charge accuracy control is realized, with the accuracy error being within 1.61%.