Online Parameter Identification of Lithium-Ion Batteries With Surface Temperature Variations
Abstract
In this paper, an adaptive parameter identification technique is proposed for lithium-ion batteries. The proposed strategy capitalizes on the power of adaptive control theory to attain robustness to parameter variation. Therefore, accurate state-of-charge (SOC) and state-of-health (SOH) estimation is obtained since they are directly correlated to the battery's parameters. Unlike many estimation procedures, the proposed estimator's convergence and stability are guaranteed by Lyapunov's direct method. In addition, temperature variations introduce a drift on the battery's parameters, which reduces the estimation accuracy. Therefore, a postcompensation methodology is proposed using surface temperature to cope with these effects. The effectiveness of the proposed estimation scheme is validated through a set of experiments under different temperatures.