Abstract
Estimation of a lithium battery electrical impedance can provide relevant information regarding its characteristics. Currently, electrochemical impedance spectroscopy (EIS) constitutes the most recognized and accepted method. Although highly precise and robust, EIS is usually performed during laboratory testing and is not suitable for any on-board application, such as in battery electric vehicles (BEVs) because it is an instrumentally and computationally heavy method. To address this issue and on-line system applications, this manuscript describes, as a main contribution, a passive method for battery impedance estimation in the time domain that involves the voltage and current profile induced by the battery through its ordinary operation without injecting a small excitation signal. This method has been tested on the same battery with different passive voltage and current profile and has been validated by achieving similar results. Compared to the original idea presented in the published conference paper, this manuscript explains, in detail, the previously developed method of transforming the battery impedance from the frequency domain to time domain. Moreover, this impedance measurement is used to estimate more robustly the battery state of charge (SoC) through Kalman filters. In the original published conference paper, only an extended Kalman filter (EKF) was applied. However, in this manuscript, an EKF and an unscented Kalman filter (UKF) are used and their performances are compared.
Highlights
Thanks to its advantageous characteristics, such as high energy and power density, long lifetime, low cost, and higher safety characteristics [1,2], lithium batteries are currently recognized as the most interesting technology for battery electric vehicles (BEVs)
Compared to the original idea presented in the published conference paper [21], this manuscript explains, in detail, the previously developed method of transforming the battery impedance from frequency domain to time domain
In the original contribution of the published conference paper [21], only an extended Kalman filter (EKF) was applied. In this manuscript, the contribution has been extended by using an EKF and unscented Kalman filter (UKF) and comparing their performance regarding the battery state of charge (SoC) estimation
Summary
Thanks to its advantageous characteristics, such as high energy and power density, long lifetime, low cost, and higher safety characteristics [1,2], lithium batteries are currently recognized as the most interesting technology for battery electric vehicles (BEVs). Electrochemical model-based methods have recently been employed for SoC estimation [39,40,41] Those techniques have the advantage to provide at the same time macroscopic quantities such as cell voltage and current and microscopic quantities such as cell temperature, concentration, and potential. UKF has been proven accurate to the third order, in the sense of Taylor series expansion, for any nonlinearity [51,52,53] In every case, both EKF and UKF depend on the precision of the impedance battery model for estimating the battery SoC.
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