Two Time-Scaled Battery Model Identification With Application to Battery State Estimation

• Published in: IEEE transactions on control systems technology Vol. 23; no. 3; pp. 1180 - 1188
• Main Authors: Hu, Yiran, Wang, Yue-Yun
• Format: Journal Article
• Language: English
• Published: IEEE 01.05.2015
• Subjects: Batteries; Equations; Integrated circuit modeling; Lithium-ion battery; Mathematical model; model identification; Real-time systems; state-of-charge (SoC) estimation; System-on-chip; Vehicle dynamics; model identification; Lithium-ion battery; state-of-charge (SoC) estimation
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2014.2358846

Electrified propulsion systems have now become an increasingly popular option for automotive companies to meet the more stringent emissions standards. A well-designed battery state estimation (BSE) system, which includes state-of-charge and state-of-health estimation, is one of the most important aspects of a successful electrified propulsion system design. Among different methods, model-based state estimation has proven to be very successful in their accuracy and implementability. A relatively newer approach to model-based BSE is to identify the battery model parameters (typically a low-order control-oriented model) in real time. This allows the battery model parameter to adjust to changing characteristics of the battery, and thus further improving the robustness of the design. However, standard identification algorithms used have very limited capability in performing this identification successfully due to the frequency response characteristics of the battery. In this brief, we describe a two time-scaled battery model parameter identification method, where the slower and faster battery dynamics are identified separately. Compared with standard approach to real-time battery model identification, where no such separation is made, this method can generate a model whose frequency response is much closer to that of the actual battery. Furthermore, this method uses the standard least squares regression method, which can be easily implemented in real time in the form of recursive least squares. Using this identification method, we show how battery SoC can be estimated. Laboratory battery cell data is used to illustrate the difference between this method and the more standard approach. Then, battery pack collected from a test vehicle is used to demonstrate the SoC estimation capability.