For the main energy storage system for EVs, Li-ion batteries are extensively applied owing to their excellent overall performance The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge (SOC) and state-of-temperature (SOT) of Lithium-ion (Li-ion) batteries. A recent breakthrough study presented by researchers from the Tongji University and Chongqing University introduces a co-estimation of state-of-charge and state-of-temperature for large-format lithium-ion batteries based on a novel electrothermal model. This advanced method will accurately estimate soc battery SOC and SOT to avoid overcharging and over-discharging, as well as thermal hazards
The study focuses on the co-estimation of battery SOC and temperature distribution for large-format Li-ion batteries. As a complex electrochemical system, the Li-ion batteries show an evident coupling relationship between SOC and SOT. Specifically, the battery temperature evolution directly affects battery capacity, OCV, Ohmic and polarization resistances, and so forth, thereby affecting the SOC estimation accuracy. Similarly, the prediction performance of battery SOT also strongly depends on SOC. A co-estimation framework of SOC and SOH, at the heart of this research, is based on an innovative electrothermal model and adaptive estimation algorithms. This method takes into account the response between battery states, making up for the greater bias that would result from separately estimating battery SOC and SOT.
Key to the algorithm's success is its ability to the control-oriented electrothermal model—a first-order RC electric model and an innovative thermal model. Specifically, the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional (2-D) thermal resistance network (TRN) submodel for the main battery body. And the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter (AUKF) and an adaptive Kalman filter (AKF), which adaptively update the state and noise covariances. Regarding the estimation results, the mean absolute errors (MAEs) of SOC and SOT estimation are controlled within 1% and 0.4 C at two temperatures, indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35 C.
The thermal model is capable of capturing the detailed thermodynamics of large-format Li-ion batteries. To comprehensively investigate the thermal characteristics of the battery during its operation and acquire a representative temperature dataset, five typical temperature measurement points are preset on the surface of the battery. The electric and thermal models are coupled by utilizing the battery temperature dependence of the electrical model parameters.
By monitoring both temperature and charge state, the research can effectively prevent risks such as thermal runaway, reducing the chances of battery fires or explosions and improving overall vehicle safety. Moreover, the study can help avoid battery failure in extreme conditions, enhancing the stability and reliability of the entire energy storage system.
In conclusion, this innovative approach can accurately and efficiently monitor two critical states of the Li-ion battery-SOC and SOT. It can not only meet the functions of the battery management system, but also help further promote carbon neutrality. As research progresses, further research can provide more input and improvements, and accurate real-time online applications of SOCs and SOTs are not out of reach.
Reference
[1] Hu XS, Feng F, Liu KL, Zhang L, Xie JL, Liu B. State estimation for advanced battery management: key challenges and future trends. Renewable Sustainable Energy Rev Oct 2019;114:109334.
[2] Chen C, Xiong R, Yang R, Li H. A novel data-driven method for mining battery open-circuit voltage characterization. Green Energy Intell Transp 2022;1(1):100001. 06/01/2022.
[3] He HW, Xiong R, Zhang XW, Sun FC, Fan JX. State-of-Charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model. IEEE Trans Veh Technol May 2011;60(4):1461–9.
[4] Ng KS, Moo CS, Chen YP, Hsieh YC. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl Energy Sep 2009;86(9):1506–11.
Author: Chao Yu, Jiangong Zhu, Wenxue Liu, Haifeng Dai, Xuezhe Wei
Title of original paper: Co-estimation of state-of-charge and state-of-temperature for large-format lithium-ion batteries based on a novel electrothermal model
Article link: https://doi.org/10.1016/j.geits.2024.100152
Journal: Green Energy and Intelligent Transportation
https://www.sciencedirect.com/science/article/pii/S2773153724000045
