The monitoring and control of battery systems can be enhanced by data collection and analysis that provide insight into the internal behavior of the battery. A well-known example is electrochemical impedance spectroscopy (EIS), which is equivalent to estimating the frequency response of the battery impedance at a particular operating condition. System identification provides a method for implementing EIS using hardware commonly found in advanced battery-management systems. In this chapter, a possible implementation of online system identification is discussed and illustrated using both simulation and experimental data.

Chapter Contents:

• 5.1 Introduction
• 5.1.1 Motivation: understanding battery dynamics
• 5.1.2 Traditional methods for measuring EIS
• 5.1.3 Related work
• 5.1.4 Outline of approach
• 5.2 Method
• 5.2.1 Data collection
• 5.2.2 Identification
• 5.2.3 Frequency response and uncertainty estimation
• 5.2.4 Combined frequency response estimate
• 5.2.5 Review of frequency identification method
• 5.3 Example experimental results
• 5.3.1 Experimental conditions for PRBS perturbation
• 5.3.2 Experimental conditions for sinusoidal perturbation
• 5.3.3 Results
• Acknowledgments
• References

Inspec keywords: lithium compounds; battery management systems; secondary cells; frequency response; electrochemical impedance spectroscopy; electrochemical electrodes

Other keywords: electrochemical impedance spectroscopy; frequency response; battery impedance; experimental data; online system identification; EIS; data collection; data analysis; internal behavior; lithium-ion batteries; in situ identification; electrochemical impedance spectra; advanced battery-management systems; Li; battery systems

Subjects: Secondary cells; Electrochemistry and electrophoresis; Secondary cells; Electrochemical analytical methods