journal article·2024-06-19Open access

Development of a Fast Running Equivalent Circuit Model with Thermal Predictions for Battery Management Applications

Vijayakanthan Damodaran, Thiyagarajan Paramadayalan, Diwakar Natarajan, Ramesh Kumar C, P. Rajesh Kanna, Dawid Taler, Tomasz Sobota, Jan Taler, Magdalena Szymkiewicz, Mohammed Jalal Ahamed

Batteries· Volume 10· Issue 6

Abstract

Equivalent circuit modelling (ECM) is a powerful tool to study the dynamic and non-linear characteristics of Li-ion cells and is widely used for the development of the battery management system (BMS) of electric vehicles. The dynamic parameters described by the ECM are used by the BMS to estimate the battery state of charge (SOC), which is crucial for efficient charging/discharging, range calculations, and the overall safe operation of electric vehicles. Typically, the ECM approach represents the dynamic characteristics of the battery in a mathematical form with a limited number of unknown parameters. Then, the parameters are calculated from voltage and current information of the lithium-ion cell obtained from controlled experiments. In the current work, a faster and simplified first-order resistance–capacitance (RC) equivalent circuit model was developed for a commercial cylindrical cell (LGM50 21700). An analytical solution was developed for the equivalent circuit model incorporating SOC and temperature-dependent RC parameters. The solution to the RC circuit model was derived using multiple expressions for different components like open circuit voltage (OCV), instantaneous resistance (R0), and diffusional parameters (R1 and C1) as a function of the SOC and operating temperature. The derived parameters were validated against the virtual HPPC test results of a validated physics-based electrochemical model for the voltage behavior. Using the developed RC circuit model, a polynomial expression is derived to estimate the temperature increase of the cell including both irreversible and reversible heat generation components. The temperature predicted by the proposed RC circuit model at different battery operating temperatures is in good agreement with the values obtained from the validated physics model. The developed method can find applications in (i) onboard energy management by the BMS and (ii) quicker evaluation of cell performance early in the product development cycle.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Advanced Battery Materials and Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical, Automotive Materials Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada

Automotive Research Center, Vellore Institute of Technology, Vellore 632014, India

CO2 Research and Green Technologies Centre, Vellore Institute of Technology, Vellore 632014, India

Department of Thermal Processes, Air Protection and Waste Management, Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland

Department of Energy, Cracow University of Technology, Al. Jana Pawła II 37, 31-866 Cracow, Poland

Funding

Natural Sciences and Engineering Research Council of Canada (NSERC) · Discovery

References (23)

Show source-provided references
1. Hariharan, K., Tagade, P., and Ramachandran, S. (2018). Mathematical Modeling of Lithium Batteries: From Electrochemical Models to State Estimator Algorithms, Springer International Publishing.10.1007/978-3-319-03527-7 ↗
2. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation10.1016/j.jpowsour.2004.02.033 ↗
3. Xu, Y., Hu, M., Fu, C., Cao, K., Su, Z., and Yang, Z. (2019). State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model. Electronics, 8.10.3390/electronics8091012 ↗
4. Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles10.1109/ACCESS.2017.2780258 ↗
5. Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique10.1016/j.energy.2017.10.043 ↗
6. Aditya, K. (2023, January 19–21). Simulation and Validation of a Precise 2RC Model of Lithium-Ion cell Incorporating Wire-bond Losses. Proceedings of the 2023 International Conference on Control, Communication and Computing (ICCC), Thiruvananthapuram, India.10.1109/ICCC57789.2023.10165007 ↗
7. Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales10.1016/j.apenergy.2016.10.020 ↗
8. Jiang, S. (2011). A Parameter Identification Method for a Battery Equivalent Circuit Model, SAE International. Available online: https://saemobilus.sae.org/papers/a-parameter-identification-method-a-battery-equivalent-circuit-model-2011-01-1367.10.4271/2011-01-1367 ↗
9. Dagci, O.H., and Chandrasekaran, R. (2014). Li-Ion Battery Pack Characterization and Equivalent Electrical Circuit Model Development, SAE International. Available online: https://saemobilus.sae.org/papers/li-ion-battery-pack-characterization-equivalent-electrical-circuit-model-development-2014-01-1839.10.4271/2014-01-1839 ↗
10. Chen, C. (2020). Parameter Determination for the Battery Equivalent Circuit Model Using a Numerical Integro-Differential Method, SAE International. Available online: https://saemobilus.sae.org/papers/parameter-determination-battery-equivalent-circuit-model-using-a-numerical-integro-differential-method-2020-01-1179.10.4271/2020-01-1179 ↗
11. Hussein, A.A.-H., and Batarseh, I. (2011, January 24–29). An Overview of Generic Battery Models. Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA.10.1109/PES.2011.6039674 ↗
12. H∞-Based Nonlinear Observer Design for State of Charge Estimation of Lithium-Ion Battery with Polynomial Parameters10.1109/TVT.2017.2723522 ↗
13. Barbieri, M., Ceraolo, M., Lutzemberger, G., and Scarpelli, C. (2022). An Electro-Thermal Model for LFP Cells: Calibration Procedure and Validation. Energies, 15.10.3390/en15072653 ↗
14. Surya, S., Samanta, A., Marcis, V., and Williamson, S. (2022). Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison. Energies, 15.10.3390/en15020623 ↗
15. Guo, J., Guo, Q., Liu, J., and Wang, H. (2023). The Polarization and Heat Generation Characteristics of Lithium-Ion Battery with Electric–Thermal Coupled Modeling. Batteries, 9.10.20944/preprints202309.1731.v1 ↗
16. Zhang, R., Xia, B., Li, B., Cao, L., Lai, Y., Zheng, W., Wang, H., Wang, W., and Wang, M. (2018). A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery under Different Temperature. Energies, 11.10.3390/en11092408 ↗
17. Experimental data of three lithium-ion batteries under galvanostatic discharge tests at different C-rates and operating temperatures10.1016/j.dib.2021.106894 ↗
18. Review of computational parameter estimation methods for electrochemical models10.1016/j.est.2021.103388 ↗
19. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models10.1149/1945-7111/ab9050 ↗
20. Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell10.1149/1.2221597 ↗
21. Newman, J., and Thomas-Alyea, K. (2004). Electrochemical Systems, Wiley-Interscience. [3rd ed.].
22. Python battery mathematical modelling (PyBaMM)10.5334/jors.309 ↗
23. Physics-Based Equivalent Circuit Model for Lithium-Ion Cells via Reduction and Approximation of Electrochemical Model10.4271/2022-01-0701 ↗