Energy management system using artificial fish swarm speed optimized fuzzy controller based on a deep recurrent neural learning classifie

Hybrid Electric Vehicles (HEVs) must ensure power demand through minimum fuel consumption and a control strategy. Existing control methods were easy to implement, showing quick response and good performance. Power demand is linked to numerous factors such as level of social and economic expansion, industrialization, urbanization, and technological growth. However, power demand problems like higher energy waste, poor quality, less accuracy, lack of robustness, and limited operating range were not reduced in existing controller methods. This paper presents an Artificial Fish Swarm Speed Optimization Fuzzy PID Controller (AFSSOF-PIDC). AFSSOFPIDC-DRNLC includes different layers in drive train management. Initially, different vehicle data is considered in the input layer and then sent to hidden layer 1. Fitness is identified by improved Artificial Fish Swarm Speed Optimization to find optimal values that minimize the power demand, and then send it toward hidden layer 2. A Mamdani Fuzzy PID Controller is used in hidden layer 2. If the fitness value of the vehicle information is less than the threshold value, fuel consumption is minimized in the HEV. Otherwise, consumption of fuel is not minimized in the HEV. Finally, energy management is achieved through minimal power demand. The results indicate that the performance of the proposed AFSSOFPIDC-DRNLC technique minimizes fuel consumption by increasing the performance of the controller as compared with existing methods. Los vehículos eléctricos híbridos (VEHs) tienen que garantizar la demanda de potencia utilizando un consumo mínimo de combustible y una estrategia de control. Existen métodos de control , fáciles de aplicar, de respuesta rápida y buen rendimiento. La demanda de energía se debe a numerosos factores, como el nivel de expansión social y económico, la industrialización, la urbanización y el crecimiento tecnológico. Sin embargo, los problemas como el mayor gasto de energía, baja calidad, menor precision, falta de robustez y rango de operación limitado, no se han reducido en los métodos de controlador existentes. Este trabajo presenta un controlador PID difuso (AFSSOF-PIDC) para la optimización de la velocidad de enjambres de peces artificiales. AFSSOF-PIDC-DRNLC incluye varias capas de gestión del tren de potencia. En primer lugar, se consideran varios datos del vehículo como entrada en la capa de entrada y se envían a la capa oculta 1. La aptitud se determina mediante una optimización mejorada de la