Abstract
This paper explores the state estimation method of lithium-ion battery pack through theoretical analysis and experimental research. Combining the advantages of the empirical models of various electrochemical models, a new type of composite electrochemistry-dual circuit polarization (E-DCP) model is proposed to better reflect the dynamic performance of the power lithium-ion battery under the conditions of meeting its safe and reliable energy supply requirements. Using the multi-innovation least squares (MILS) algorithm to identify the parameters in the E-DCP model online, so that it has the characteristics of high data utilization efficiency and high parameter identification accuracy. The battery charge and discharge efficiency function is introduced to dynamically modify the battery capacity, and the dynamic function is used to improve the Kalman gain in the extended Kalman filter (EKF), a new type of based on dynamic function improvement and combined with actual capacity correction (FC-DEKF) algorithm is applied to the estimation of battery pack operating characteristics, which solves the problem that the traditional EKF algorithm is difficult to estimate errors when the system input change rate is large. The experimental results of urban dynamometer driving schedule (UDDS) and complex charge-discharge cycle test show that the maximum error of terminal voltage does not exceed 0.04V, the accuracy is 99.05%, and the errors of MILS algorithm combined with FC-DEKF algorithm for SOC estimation are all within 1%. The proposed equivalent circuit modeling method and state estimation correction strategy provide a theoretical basis for the reliable application of high-power lithium-ion battery packs.
Highlights
With the consumption of fossil energy and the pressure of environmental protection, as well as the limitation of the maximum capacity of battery cell, it has become more and more important to improve the mathematical modeling research of battery group management technology [1,2,3,4]
The experimental results of urban dynamometer driving schedule (UDDS) and complex charge-discharge cycle test show that the maximum error of terminal voltage does not exceed 0.04V, the accuracy is 99.05%, and the errors of multi-innovation least squares (MILS) algorithm combined with FC-DEKF algorithm for State of Charge (SOC) estimation are all within 1%
Through urban dynamometer driving schedule (UDDS) and complex charge-discharge cycle test experiments, the accuracy of the electrochemistry-dual circuit polarization (E-DCP) model and the accuracy of the multi-innovation least squares (MILS) algorithm combined with the extended Kalman filter (EKF) algorithm based on dynamic function improvement and combined with actual capacity correction (FC-DEKF) algorithm for SOC estimation are verified
Summary
With the consumption of fossil energy and the pressure of environmental protection, as well as the limitation of the maximum capacity of battery cell, it has become more and more important to improve the mathematical modeling research of battery group management technology [1,2,3,4]. Accurate SOC estimation helps to improve the safety level and cycle life performance of the energy power supply system Based on achieving this goal, while constructing an accurate composite electrochemical circuit model, it is combined with a highprecision SOC filter to achieve real-time monitoring of SOC [21, 22]. Through urban dynamometer driving schedule (UDDS) and complex charge-discharge cycle test experiments, the accuracy of the E-DCP model and the accuracy of the multi-innovation least squares (MILS) algorithm combined with the EKF algorithm based on dynamic function improvement and combined with actual capacity correction (FC-DEKF) algorithm for SOC estimation are verified This kind of modeling and collaborative prediction correction strategy research is of great significance to improve the estimation accuracy and robustness of highpower battery packs
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