Equivalent-Circuit Thermal Model for Batteries with One-Shot Parameter Identification

TL;DR

This work addresses accurate battery surface-temperature estimation for real-time thermal management by introducing a novel equivalent-circuit electro-thermal model (ECTM). Heat generation within the cell is approximated with data-driven polynomials in state of charge ($SOC$), current ($I$), and terminal voltage ($V$), allowing the temperature dynamics to be written in a linear-in-parameters form that is solvable from one-shot cycling data. Parameter identification reduces to a standard least-squares problem in a small parameter vector $\Theta$, enabling fast, real-time estimation. Validation on NASA, Oxford, and MIT datasets shows accurate temperature prediction across varying ambient conditions and degradation levels, highlighting the approach’s scalability for large battery packs and practical thermal-management applications.

Abstract

Accurate state of temperature (SOT) estimation for batteries is crucial for regulating their temperature within a desired range to ensure safe operation and optimal performance. The existing measurement-based methods often generate noisy signals and cannot scale up for large-scale battery packs. The electrochemical model-based methods, on the contrary, offer high accuracy but are computationally expensive. To tackle these issues, inspired by the equivalentcircuit voltage model for batteries, this paper presents a novel equivalent-circuit electro-thermal model (ECTM) for modeling battery surface temperature. By approximating the complex heat generation inside batteries with data-driven nonlinear (polynomial) functions of key measurable parameters such as state-of-charge (SOC), current, and terminal voltage, our ECTM is simplified into a linear form that admits rapid solutions. Such simplified ECTM can be readily identified with one single (one-shot) cycle data. The proposed model is extensively validated with benchmark NASA, MIT, and Oxford battery datasets. Simulation results verify the accuracy of the model, despite being identified with one-shot cycle data, in predicting battery temperatures robustly under different battery degradation status and ambient conditions.

Figures (8)

• Figure 1: The standard Thevenin equivalent-circuit model.
• Figure 2: Schematic of the ECTM for battery.
• Figure 3: Prediction performance of the developed ECTM with NB #18 data (ambient temperature 24 $^\circ$C). Left: The fitting performance of ECTM on the base Cycle 15; Middle and Right: Predicted temperatures from identified ECTM for Cycle 40 (a medium degradation with 5.62% capacity fade) and Cycle 128 (a major degradation with 24.16% capacity fade).
• Figure 4: The RMSE between predicted temperature profiles from ECTM and the ground-truth profiles for different batteries, where C-$k$ above the bars represents the Cycle $k$.
• Figure 5: Prediction performance of the developed ECTM with OB #2 data (ambient temperature 40 $^\circ$C). Left: The fitting performance of ECTM on the base Cycle 5; Middle and Right: Predicted temperatures from identified ECTM for Cycle 15 (a medium degradation with 4.30% capacity fade) and Cycle 73 (a major degradation with 27.40% capacity fade).