Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network

The hysteresis effect represents the difference in open circuit voltage (OCV) between the charge and discharge processes of batteries. An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of ${\text{LiFePO}}_{\text{4}}$ batteries. However, the intricate influence of state-of-charge (SOC), temperature, and battery aging have posed significant challenges for hysteresis modeling, which have not been comprehensively considered in existing studies. This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions, addressing the intricate dependencies on SOC, temperature, and battery aging. First, a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths, temperatures and aging states. Second, the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability. The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information. Third, the conditional matrix, incorporating temperature, health state, and historical paths, is constructed to provide the scenario-specific information for the adversarial network, thereby enhancing the model’s adaptability. Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions, with accuracy improvements of 31.3–48.7% compared to three state-of-the-art models.