TY  - EJOU
AU  - Han, Xincheng 
AU  - Ma, Hongyan 
AU  - Meng, Shuo 
AU  - Ren, Chengzhi 

TI  - Hybrid Temporal Convolutional Network-Transformer Model Optimized by Particle Swarm Optimization for State of Charge Estimation of Lithium-Ion Batteries
T2  - Energy Engineering

PY  - 2026
VL  - 123
IS  - 4
SN  - 1546-0118

AB  - Lithium-ion (Li-ion) batteries stand as the dominant energy storage solution, despite their widespread adoption, precisely determining the state of charge (SOC) continues to pose significant difficulties, with direct implications for battery safety, operational reliability, and overall performance. Current SOC estimation techniques often demonstrate limited accuracy, particularly when confronted with complex operational scenarios and wide temperature variations, where their generalization capacity and dynamic adaptation prove insufficient. To address these shortcomings, this work presents a PSO-TCN-Transformer network model for SOC estimation. This research uses the Particle Swarm Optimization (PSO) method to automatically configure the architectural parameters of the Temporal Convolutional Network (TCN) and Transformer components. This automated optimization enhances the model’s ability to represent the dynamically evolving nature of SOC. Additionally, this integrated framework significantly increases the model’s capacity to capture SOC dynamics in complex operational scenarios. During training and evaluation using a comprehensive dataset that covers complex operating conditions and a broad temperature spanning from −20°C to 40°C, the proposed model achieves a root mean square error (RMSE) of less than 0.6%, a maximum absolute error (MAXE) below 4.0%, and a coefficient of determination (<i>R</i><sup>2</sup>) of 99.99%. Additional comparative experiments on data from an energy storage company further verify the model’s superior performance, with an RMSE of 1.18% and an MAXE of 1.95%. The implications of this work extend to the development of optimization strategies and hybrid architectures, providing insights that can be adapted for state estimation across a range of complex dynamic systems.
KW  - Lithium-ion battery; charge state estimation; PSO algorithm; PSO-TCN-transformer network

DO  - 10.32604/ee.2025.072906