A Novel Dual‐Layer Genetic Algorithm With Parameter Interaction Framework for Battery Parameter Identification

ABSTRACT Equivalent circuit models are widely adopted for battery modeling, yet their parameters require frequent updates due to aging‐induced variations. While unit data segment (UDS)‐based methods leverage operational data for parameter identification, existing approaches fail to address two critical issues: (1) the sensitivity of model accuracy to historical data utilization strategies and (2) parameter discontinuity at adjacent segment boundaries. To overcome these limitations, this study proposes a novel dual‐layer genetic algorithm (GA) with a parameter interaction framework. The upper‐layer GA autonomously optimizes historical data selection and initializes parameters for the first segment, while the lower‐layer GA identifies parameters for subsequent segments. A boundary matrix iteration mechanism enforces parameter continuity across segments by propagating constraints iteratively. Experimental validation on Urban Dynamometer Driving Schedule (UDDS) under 25°C datasets demonstrates superior performance: Under UDDS conditions, the maximum error, mean absolute error, and RMSE are 38.6, 4.7, and 6.1 mV, respectively. These values represent improvements of 8.7%, 29.8%, and 31.4% compared to the UDS‐based method; and 45.5%, 42.6%, and 45.0% compared to the Recursive Least Squares‐based method. The multi‐temperature validation results confirm the strong robustness of the proposed approach under disparate operating temperatures. This work advances data‐driven battery modeling by resolving boundary discontinuity and reducing expert dependency in parameter identification, offering a scalable solution for cloud‐based battery management systems.