Hybrid Approaches of Battery Performance Modeling and Prognosis
Lou, Yangbing
2021
Abstract
Batteries, such as Lithium-ion battery, have the advantages of high specific energy, low pollution and high safety, and thus have become one of the main power sources for new energy automobiles. The State of Health (SOH) and Remaining Useful Life (RUL) of a power battery are the most important performance index in the battery system. The SOH of the power battery is different from other battery parameters such as voltage, current, internal resistance and temperature in that it cannot be obtained through direct measurement by any equipment or instrument. Besides, these electrical parameters will degrade with the use of batteries. For this reason, the accurate online estimation of the SOH and RUL of the battery has become one of the key challenges in the battery management system. This dissertation focuses on investigating key battery performance indicators such as internal resistance, capacity and self-discharge of Ni-H2 battery and lithium battery. The research also concentrates on the SOH estimation of the batteries through the development of Extended Kalman Filter (EKF) algorithm, the online estimation strategy of Dual Extended Kalman Filter (DEKF) and comprehensive assessment of Particle Filter (PF) and Support Vector Regression (SVR). Data-driven models, such as Autoregressive Moving Average (ARMA) model, artificial neural network, and hybrid models are implemented to provide both the degradation analysis and performance prediction for NiH2 battery cells. The prediction model can also help reduce the false alarm rate of the existing battery online monitoring system. The long-term behavior of pressure, as a degradation indicator, is modeled to help understand the battery aging behavior. Combining the electrochemical battery model, a state equation and an output equation for the SOC estimation of the lithium battery are established. Each parameter in the SOC system is observed by using the EKF algorithm. The system simulation results indicate that the SoC estimation of the lithium battery in the EKF algorithm has good precision and accuracy. Furthermore, to solve the problem that the initial parameters of the battery model in the online SOH estimation of the battery cannot be determined in advance, the DEKF algorithm is introduced. Two independent EKFs are established to estimate the state of battery system and the parameters, respectively, and mutually update their states and parameters. The film resistance and discharging capacity are estimated to represent battery’s SOH. The advantages of this proposed method are two-fold: (1) implementing physics-based models to provide physical interpretation of Lithium-ion battery cell, and (2) utilizing dual models to maintain the long-term accuracy of estimates. Finally, a novel battery SOH monitoring model is built to analyze the proposed degradation parameters and to predict the RUL by updating its probability distribution. The Support Vector Regression-Particle Filter (SVR-PF) algorithm is implemented in the research work to make improvement over the standard PF, which has the degeneracy phenomenon. The SVR-PF shows improved estimation and prediction capability compared to the standard PF.Deep Blue DOI
Subjects
power battery state of health remaining useful life
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