Accurate estimation of the state of health (SOH) of lithium batteries plays an important role in the health management of battery systems. In order to improve the accuracy of SOH estimation, a SOH estimation method that combines the parameter-optimized multivariate variational modal decomposition (MVMD) and stochastic configuration network (SCN) is proposed. Multiple health factors (HF) are extracted from the lithium battery charging and discharging process as inputs to the SOH model, and adaptive weights and optimal domain fluctuation strategies are introduced in the global stage of the Zebra Optimization Algorithm (ZOA) to improve its global searching ability, to obtain the Improved Global Zebra Optimization Algorithm (IGZOA), which is utilized to search for the optimization of the MVMD and the SCN parameters, and finally, the MVMD and SCN parameters are tested in nine benchmark functions IGZOA performance, the proposed combined method is compared with different methods for lithium battery SOH estimation on NASA and CALCE datasets, and the results show that the average values of root mean square error and absolute error of the proposed method are 0.84% and 0.93%, respectively, and the proposed method has higher prediction accuracy and generalizability.