In this manuscript, a hybrid control method is implemented to model and design bidirectional DC-DC converters for the energy management of interconnected renewable energy sources (RES). The proposed hybrid control method is the joint execution of Hybrid Crow Search Algorithm (HCSA) and Radial Basis Function Neural Network (RBFNN). The modeling and design of the Bidirectional DC-DC converter modelling and design topology is developed with the improved efficiency of the converter, efficient use of renewable energy sources, and the reduction of switching loss. In the proposed manner, the HCSA runs the evaluation procedure for establishing correct control signals for the system and creates the control signal database for offline mode in light of power range among source side (RES, battery and supercapacitor (SCAP)) and load side. Here, the crow’s seeking behavior is modified by crossover and mutation. The data set obtained is utilized to operate the AI approach for the online method and leads the control process on less execution time. In the proposed method, the intention function is defined using the data of the system subject to equality and inequality constraints. The constraint is the availability of RES, power requirement and charge level of storage elements. Batteries and SCAP are utilized as an energy source to allow renewable energy system units to operate continuously in stable and constant power output. At that point, the proposed model is implemented on MATLAB/Simulink work platform and the implementation is assessed to the existing techniques like base method, ALORNN and CSA. Furthermore, switching losses, conduction losses and converter efficiency are also analyzed. Switching losses, conduction losses and converter efficiency of the proposed technique are 0.19W, 0.43W and 98%.

在本手稿中，实现了一种混合控制方法来建模和设计双向DC-DC转换器，用于互连可再生能源（RES）的能源管理。提出的混合控制方法是混合乌鸦搜索算法（HCSA）和径向基函数神经网络（RBFNN）的联合执行。双向DC-DC转换器建模和设计拓扑的建模和设计是随着转换器效率的提高，可再生能源的有效利用以及开关损耗的降低而开发的。HCSA以建议的方式运行评估程序，以为系统建立正确的控制信号，并根据电源端（RES，电池和超级电容器（SCAP））和负载端之间的功率范围，为离线模式创建控制信号数据库。这里，乌鸦的寻觅行为被交叉和变异所改变。获得的数据集用于操作在线方法的AI方法，并以更少的执行时间引导控制过程。在所提出的方法中，意图函数是使用受等式和不等式约束的系统数据定义的。约束条件是RES的可用性，电源要求和存储元件的充电水平。电池和SCAP被用作能源，使可再生能源系统单元以稳定和恒定的功率输出连续运行。那时，所提出的模型是在MATLAB / Simulink工作平台上实现的，并且对该实现的评估是针对现有技术（如基础方法，ALORNN和CSA）进行的。此外，开关损耗 还分析了传导损耗和转换器效率。该技术的开关损耗，传导损耗和转换器效率分别为0.19W，0.43W和98％。