[16] applied MPC to the bidirectional DC-DC converter of a hybrid energy storage system by minimizing the objective function through duty cycle enumeration of the control signal. Ref. [17] optimized the evaluation function of the DC-DC converter and solved the constraint conditions analytically, making the converter control more efficient. Ref. [18] proposed a Hybrid Model Linearization Predictive Control (HMLPC), which used the Jacobian matrix to establish the optimal linear approximation model of the sampling points to construct a hybrid linearization predictive model. Ref. [19] controlled the inverter of the energy storage system using finite control set model predictive control (FCS-MPC) and designed multiple cost functions for different control modes of the hybrid energy storage systems. For large-capacity HESS in DC microgrids, decreasing switching losses is very important for multiple parallel converters. Unfortunately, the losses are not considered in MPC methods. Active disturbance rejection control (ADRC) is a modern nonlinear control method based on traditional PID control. This control technique is highly resilient to disturbances, robust, and capable of assessing and compensating uncertainties and disturbances in real time [20; 21; 22]. A two-loop controller utilizing ADRC for a bidirectional Buck/Boost converter was designed, and simulation and experimental results indicated that the characteristics of ADRC outperform conventional PI controllers [23]. An ADRC control strategy was proposed for HESS, integrating the fuzzy adaptive and extended state observer (ESO) to observe and compensate for disturbances [24]. Ref. [25] utilizes double closed-loop ADRC to control the energy storage system in DC microgrids, and comparisons between ADRC and PI are conducted. The rated voltage is 600 V, the largest overshoot is 16 V when PIis used, and the largest overshoot is 7 V when ADRC is used. Compared with PI, ADRC could significantly decrease the overshoot of the DC bus by 56% but could not improve the response speed. Ref. [26] adopts MPC for energy storage systems in DC microgrids at the same load changes. The regulation time when MPC is used is 0.0345 s, while the regulation time when PI is used is 0.0442 s. Compared with PI, MPC could reduce the regulation time by about 22%. Consequently, the combination of ADRC and MPC could significantly increase response speed, decrease overshoots, and enhance system stability and immunity. Droop control is commonly adopted to achieve power distribution between multiple parallel battery converters but introduces DC bus voltage deviations [27; 28; 29; 30; 31]. Literature [32] constructs an average bus voltage compensation controller without a PI regulator based on the compensation factor and unit average virtual rated current, which can compensate the bus voltage and quickly recover it to a given value. A robust controller based on an expansive state observer for the fuel cell energy storage converter, in which converter uncertainty and external disturbances are treated as total disturbances, was estimated by an expansive state observer and eliminated in control [33]. Compared with conventional observers, the dilated state observer does not depend on system models or parameters and has strong disturbance immunity. Ref. [34] discussed designing voltage and current observers for secondary control for Microgrids using linear ADRC, which directly and efficiently compensated for any disturbances and was consistent with voltage quadratic compensation. Aiming at improving disturbance immunity and decreasing adjustment time, this paper proposes active disturbance rejection control (ADRC) combined with improved MPC for large-capacity hybrid energy storage systems in DC microgrids. There are n + 1 bidirectional DC/DC converters in HESS, and the control strategies of n battery converters are the same. ADRC is utilized in outer voltage control loops, and improved MPC is employed in inner current control loops.