Efficient and accurate state-of-charge (SOC) estimation of lithium-ion batteries under complex conditions is challenging. To address this, we develop a battery performance prediction framework using a lumped semi-empirical model, incorporating three critical factors: state-of-health (SOH), depth of discharge (DOD), and operational load. Systematic evaluations, including hybrid pulse power characterization (HPPC) tests and new European driving cycle (NEDC) simulations, were conducted to validate the model’s predictive capability across varying SOH and DOD levels. Building on this foundation, we pro-pose an SOC estimation methodology that leverages the model’s framework, analyzing three distinct aging states (unaged, mildly aged, and seriously aged) and comparing offline versus online parameter identification approaches. To enhance accuracy in low SOC regimes, a segmented training strategy is introduced. Additionally, a comparison was made between four different SOC estimation methods. Experimental results show that the lumped semi-empirical model is suitable for complex working conditions of lithium-ion batteries, and the proposed method exhibits high accuracy and robustness in SOC estimation across typical discharge ranges, and it effectively balances estimation accuracy and computational burden, making it beneficial for engineering applications.
Mi Li (Thu,) studied this question.