Kolmogorov–Arnold Networks under TinyML Constraints: A Study on SoC Estimation for Electric Vehicles

Kolmogorov–Arnold Networks (KANs) represent a promising machine learning architecture that leverages univariate functional decomposition to model complex phenomena using compact and interpretable structures. These characteristics make KANs especially attractive for deployment in TinyML environments, where memory, processing power, and energy consumption are strictly constrained. This paper evaluates the feasibility and trade-offs of using a KAN model to estimate the State of Charge (SoC) in electric vehicle batteries. We design a KAN tailored for embedded systems and compare its performance with a conventional Multilayer Perceptron (MLP) baseline under identical training and deployment conditions. Our evaluation includes predictive accuracy, training cost, model size, inference speed, and energy consumption on microcontrollers. Results show that the KAN model achieves a nearly ten times smaller memory footprint than the MLP (693 bytes vs. 6807 bytes) and maintains comparable energy consumption and inference speed across different embedded platforms. Although the MLP outperforms the KAN during training with faster convergence and lower energy requirements, the KAN demonstrates competitive predictive performance at inference time while significantly reducing deployment costs in terms of memory usage and energy efficiency at the edge. Furthermore, the KAN model produces symbolic mathematical expressions, offering direct interpretability and facilitating analytical validation — a critical advantage for embedded battery diagnostics and safety-critical applications.