The application of pulse response analysis method in lithium-ion battery modeling and state estimation
Year: 2024
Type: article
Abstract: Battery management depends on accurate battery models. However, comprehensive battery models are often complicated and computationally demanding, limiting their practical usage, Researchers aim to simplify and improve models for better system efficiency. This paper introduces a pulse response analysis method to describe battery polarization characteristics. By combining pulse response analysis, convolution theory, Kalman algorithm, and regression algorithm, we propose a precise calculation method for the battery's pulse response function and establish a simplified battery model structure. Under dynamic conditions, the simulated terminal voltage error using the pulse response model is less than 0.4%, showcasing its accuracy. Additionally, the State of Charge (SOC) estimation error remains below 0.5%, further highlighting the model's reliability. Furthermore, the paper introduces a rapid State of Health (SOH) estimation approach using pulse response functions and time coefficient methodology. This method achieves precise SOH estimation with an error of less than 0.2% through linear regression based on a 1s pulse and 5s relaxation. Our method effectively avoids the subjective biases that conventional methods may encounter in parameter identification and feature extraction, providing a more objective basis for battery simulation and management. This makes our method highly suitable for engineering applications and contributes to enhancing battery management performance.
Source: Journal of Energy Storage
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,030
Awards: G7043099291, 电动汽车驱动电机模拟器多电平逆变谐波抑制机理研究
OpenAlex ID: W4403350949