Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network
Lisen Yan,
Jun Peng,
Zeyu Zhu,
Heng Li,
Zhiwu Huang,
Dirk Uwe Sauer,
Weihan Li
Affiliations
Lisen Yan
School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany
Jun Peng
School of Electronic Information, Central South University, Changsha, 410083, China
Zeyu Zhu
School of Computer Science and Engineering, Central South University, Changsha, 410083, China
Heng Li
School of Electronic Information, Central South University, Changsha, 410083, China; Corresponding authors.
Zhiwu Huang
School of Automation, Central South University, Changsha, 410083, China
Dirk Uwe Sauer
Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany
Weihan Li
Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany; Corresponding authors.
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 LiFePO4 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.
