A Randles Circuit Parameter Estimation of Li-Ion Batteries With Embedded Hardware

Accurate modeling of electrochemical sources is very important to predict how a source will perform in specific applications related to the load or environmental parameters. A Randles circuit is considered as a reliable equivalent electrical circuit in studying and modeling various electrochemical systems and processes. The classical parameter estimation approach based on use of software packages (ZSim, MEISP, LEVMW, and so on) requires high computational performance processing units, decreasing the reliability as proper maintenance actions can be delayed because of offline analysis. Advancing the state of the art, we propose a low-complexity approach for embedded hardware-based parameter estimation of the Randles circuit. Our noniterative method uses only the measured real and imaginary parts of impedance, with numerical approximation of the first derivative of real/imaginary part quotient, to create closed-form expressions with a unique solution. The initial estimated values are available from partial dataset (after measurement at only three frequencies). Moreover, it is not software platform-specific, which enables a high level of portability. The presented method is verified with theoretical, numerical, and experimental analysis, with more than 1000 datasets. We also demonstrated the applicability in parameter estimation of the Randles circuit of a Li-ion battery. Finally, we verified suitability for embedded hardware platforms, with deployment on a microcontroller-based platform with a clock speed of 16 MHz and 8 kB of SRAM. Reliable parameter estimation processing of a 100-point dataset was performed in just 106 ms with 1% relative error, requiring less than 53 mJ of energy.