Passive Tracking of the Electrochemical Impedance of a Hybrid Electric Vehicle Battery and State of Charge Estimation through an Extended and Unscented Kalman Filter

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.

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

“…The latter is especially relevant for large battery systems, where high currents are necessary to obtain a sufficient voltage response. Omitting the actuators all together and measuring passively is also a viable option for many systems [112,268,293,[304][305][306][307][308][309][310]. This, however, requires the load current to exhibit dynamic properties in order to be suitable as an excitation signal.…”

“…Before transformation of the acquired data to the frequency domain, preprocessing is performed. Preprocessing steps may include, for example, digital filtering [268,290,314], gain and offset compensation [290,292], segmentation [112,315], windowing [112,306,307,316], zeropadding [308], and down sampling [293,296,312]. Potential uses of digital filtering include HPFs to remove low frequency components [290] and compensate for drift [314], Bandpass Filters (BPFs) as preparation for correlation based postprocessing [268] as well as decimation filters for down sampling [293,296].…”

“…After impedance estimation, averaging may be utilized to reduce result variation and allow for more accurate tracking of impedance changes over time [315]. Window averaging [301], exponential averaging [297,306,315], and more advanced methods [305] are proposed in the literature.…”

Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle

“…BM4 is equivalent to BM4 at the difference that the equivalent resistance value is a function of SoC (f(SoC)) provided by the battery manufacturer A123 Systems. BM5 is a one RC network model, whose data come from [38,39]. Finally, BMref is a two RC network model, whose data comes from [25].…”