applied sciences Article  
Active Disturbance Rejection Control Combined with Improved Model Predictive Control for Large-Capacity Hybrid Energy Storage Systems in DC Microgrids  
[PERSON]  
1 School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China; [EMAIL_ADDRESS]  
[PERSON]  
1 School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Beijing Institute of Exploration Engineering, Beijing 100083, China  
[PERSON]  
1 School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China; [EMAIL_ADDRESS]  
[PERSON]  
1 School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China; [EMAIL_ADDRESS]  
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pass filter crossover, Refs. [8; 9] describe a typical double closed-loop control. The unbalanced power is decomposed into a high-frequency part and a low-frequency part. The supercapacitor stabilizes the high-frequency power while the battery stabilizes the low-frequency part. Ref. [10] proposes an adaptive wavelet decomposition method to accomplish power allocation for a hybrid energy storage system, but the wavelet decomposition method deeply depends on the selection of basis function. Traditional proportional-integral (PI) control is often used in HESS, but adjusting parameter procedures of PI dual-loop control is complex. Furthermore, the disturbance rejection capability is weakened, and the regulation time is long. In particular, when steady-state operating points change, the original control parameter values may not maintain good control performances [11; 12; 13]. To eliminate the limitations of PI controllers, many intelligent methods have been proposed and widely used in energy storage systems, and have achieved good control characteristics. These presented methods include model predictive control (MPC), Fuzzy Control, active disturbance rejection control (ADRC), Sliding Mode Control, Neural Network Control, etc. Considering the strong nonlinear and hybrid characteristics of bidirectional DC-DC converter, a neural network control method was proposed in [14]. It used estimated current information from the steady-state voltage equation to suppress the over-compensation phenomenon of neural network control. This method effectively suppresses the over-compensation phenomenon by repeatedly training and changing the neural network reference values. However, it usually requires a large amount of training data to obtain accurate results. Insufficient or low-quality data may lead to a decrease in control performances. Ref. [15] proposed a sliding mode control method and discussed parameter selection. Sliding mode control provides good robustness and adaptability when facing complex control problems such as parameter uncertainty, external disturbances, and nonlinear systems. However, the performances of sliding mode control largely depend on the design of the sliding surface, which has higher requirements for system modeling and parameter tuning. On the other side, model predictive control (MPC) allows for the trade-off of multiple control objectives by adjusting the objective function into the optimization problem. It could effectively handle constraint conditions and ensure that the control strategy operates within the system's constraint range while optimizing system stability, response speed, energy consumption, and other indicators. It is commonly utilized in energy management, motor control, and battery energy storage system control. Ref.