A Physics-Guided Framework for Battery State-of-Charge Estimation Based on Improved Dual-Scale Feature-Fusion TCN and Adaptive Zonotopic Filtering

With the rapid proliferation of the Internet of Things (IoT), Li-ion batteries have become the primary energy source for massive interconnected devices, making precise State of Charge (SOC) estimation a pivotal function for IoT-enabled battery management systems (BMS). The proposed methodology integrates a RC equivalent circuit model, a Dual-scale TCN-SE-GRU (DTSG) network, and an adaptive zonotopic filter to estimate the SOC. First, the equivalent circuit model is employed to separate the transient voltage fluctuations from the terminal voltage. The resulting equivalent open-circuit voltage is then used as the network input to reduce estimation fluctuation. Then, the DTSG network is developed to facilitate the extraction of multiple time-scale temporal features and generate a preliminary SOC estimate through dual-scale temporal convolution, squeeze-and-excitation-based adaptive fusion, and GRU-based sequential modeling. Finally, the network output is incorporated into the adaptive zonotopic filter as a pseudo-measurement to realize closed-loop SOC estimation under unknown-but-bounded noise. Moreover, this approach offers a reliable prediction interval for SOC, providing additional decision support to the BMS under bounded but uncertain noise conditions.Empirical evidence confirms that the proposed approach can achieve higher accuracy and greater stability under various operating conditions and temperatures, and can also provide a reliable range for the estimation of SOC.