Parameter Identification of Li-ion Batteries: A Comparative Study

Lithium-ion batteries are crucial building stones in many applications. Therefore, modeling their behavior has become necessary in numerous fields, including heavyweight ones such as electric vehicles and plug-in hybrid electric vehicles, as well as lightweight ones like sensors and actuators. Generic models are in great demand for modeling the current change over time in real-time applications. This paper proposes seven dynamic models to simulate the behavior of lithium-ion batteries discharging. This was achieved using NASA room temperature random walk discharging datasets. The efficacy of these models in fitting different time-domain responses was tested through parameter identification with the Marine Predator Algorithm (MPA). In addition, each model's term's impact was analyzed to understand its effect on the fitted curve. The proposed models show an average absolute normalized error as low as 0.0057.

Automated summary

Lithium-ion batteries play a crucial role in many applications, and therefore, modeling their behavior is necessary in various fields. This paper proposes seven dynamic models to simulate the discharging behavior of lithium-ion batteries, and their efficacy in fitting different time-domain responses is tested through parameter identification. The results show that these models have an average absolute normalized error as low as 0.0057.