Novel reduced-order modeling method combined with three-particle nonlinear transform unscented Kalman filtering for the
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ORIGINAL ARTICLE
Novel reduced‑order modeling method combined with three‑particle nonlinear transform unscented Kalman filtering for the battery state‑of‑charge estimation Wenhua Xu1 · Shunli Wang1 · Carlos Fernandez2 · Chunmei Yu1 · Yongcun Fan1 · Wen Cao1 Received: 22 April 2020 / Revised: 4 August 2020 / Accepted: 31 August 2020 © The Korean Institute of Power Electronics 2020
Abstract Accurate estimation of the lithium-ion battery state of charge plays an important role in the real-time monitoring and safety control of batteries. In order to solve the problems that the real-time estimation of the lithium-ion battery is difficult and the estimation accuracy is not high under various working conditions, a lithium-ion battery is taken as a research object, and the working characteristics of the lithium-ion battery are studied under various working conditions. In order to reduce the computational complexity of the traditional unscented Kalman algorithm, an improved unscented Kalman algorithm is proposed. Considering the importance of accurately estimating the initial state of charge for later estimation, the initial estimation value is calibrated by using the open-circuit voltage method. Then, the improved unscented Kalman filter algorithm based on a reduced-order model is used for assessing and tracking to realize real-time high-precision estimation of the state of charge of the lithium-ion battery. A simulation model is built and combined with a variety of working conditions data for performance analysis. The experimental results show that the convergence speed and tracking effect are good and that the estimation error control is within 0.8%. It is verified that the reduced order of the three-particle nonlinear transform unscented Kalman results in higher accuracy in the state-of-charge estimation of lithium-ion batteries. Keywords Lithium-ion battery · Thevenin model · State of charge · Unscented Kalman filtering algorithm · Nonlinear transform
1 Introduction Energy security and environmental protection play an important role in the development plan of the world’s economy. Pursuing new energy sources to replace traditional fossil fuels has become a focus of attention worldwide [1]. Lithium-ion batteries have been widely used and developed in the field of new energy due to their high energy density, high output power and high-cost performance [2]. With the wide application of lithium-ion batteries in the field of new energy, their health condition detection has received increasing attention. It is of great importance to accurately * Shunli Wang [email protected] 1
School of Information Engineering, Southwest University of Science and Technology, Mianyang, China
School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen, SCT, UK
2
estimate the state of charge (SOC) of lithium-ion batteries for maximizing their performance and for realizing real-time state detection and safety control of lithium-ion batteries [3]. Lithium-ion batteries are often used under complex working conditions, and
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