Data-Driven State of Charge Estimation for Lithium-Ion Batteries Based on Polynomial Surface Fitting Under Dynamic Driving Cycles

Abstract The accurate estimation of state of charge (SOC) for lithium-ion batteries is critically important for battery management systems (BMS) in electric vehicles, yet it remains challenging under highly dynamic driving cycles due to strong nonlinearities, sensor noise, and model complexity. To address these issues, this paper proposes a purely data-driven SOC estimation method based on three-dimensional polynomial surface fitting, using current, SOC, and terminal voltage as the input-output space. The method is developed and validated using three public driving-cycle datasets—FUDS, UDDS, and DST—with preprocessed smoothing and normalization. A bivariate polynomial model (order 3 for current, order 5 for SOC) is fitted to map the relationship (I, SOC) → V, achieving an adjusted R 2 of 0.99 on the DST dataset. The proposed approach eliminates the need for recursive filters, equivalent circuit parameters, or extensive training data typical of neural networks. Experimental results show that the polynomial surface fit generalizes well across different dynamic conditions, providing low estimation errors and high robustness. This study demonstrates that a simple, interpretable polynomial fitting method can achieve excellent SOC estimation accuracy under standard driving cycles, offering a lightweight and practical solution for embedded BMS applications.