State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network
Source abstract
Abstract
In order to enhance the accuracy of the traditional extended Kalman filter (EKF) algorithm in the estimation of the state of charge (SoC) of power batteries, we first derived the state space equation and measurement equation of lithium power batteries based on the Thevenin battery model and the modified Ampere-Hour integral algorithm. Then, the basic principles of EKF, backpropagation neural networks (BPNNs), and a biogeography-based optimization (BBO) algorithm were analyzed, and the arc curve mobility model was used to improve the global search ability of the BBO algorithm. By combining these three algorithms, this paper proposes a BP neural network method based on the BBO algorithm. This method uses the BBO algorithm to optimize the incipient weight and threshold of the BP neural network and uses this improved neural network to modify the estimated value of the extended Kalman filter algorithm (BBOBP-EKF). Finally, the BBOBP-EKF algorithm, the extended Kalman filter algorithm based on the BP neural network (BP-EKF), and the EKF algorithm are used to estimate the error value of the SOC of a power battery, and according to the experimental data, it was confirmed that the proposed BBOBP-EKF algorithm has been improved compared to other algorithms with respect to each error index term, in which the maximum error is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, the minimum error is also the smallest, and the estimation accuracy is improved compared to the traditional algorithms.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Fault Detection and Control Systems · Control Systems and Identification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China
Funding
key Science and Technology Research Program of Chongqing Municipal Education Commission · Chongqing Municipal Graduate Research Innovation Project
References (34)
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