GA-trained GRNN for Intelligent Ultra Fast Charger for Ni-Cd Batteries

Abstract

This paper presents an intelligent genetic algorithm successfully applied to Ni-Cd batteries [6]. With less (GA) technique for training of a neural network controller to computational burden, it has been found that its data selection achieve a compact network and to decrease battery charging performance is greater than some techniques, for example time. An ultra fast charging device for Nickel-Cadmium (Ni-Cd) ANFIS. Moreover, the computational afford reduction of batteries is designed through the generalized regression neural GRNN has been improved by further in [7]. Nonetheless, the network (GRNN) and implemented with the data selection of construction GRNN still was not thoroughly MATLAB/SIMULINK for testing and operating on real system. considered. The input-output data for training neural networks were collected from GA. The suitable data were selected to establish Genetic algorithms are probabilistic search techniques that GRNN comprising only 13 processing elements. Each node of emulate the mechanics of evolution [8]. They are capable of RBFs is an extendable support function that overcomes the globally exploring a solution space, pursuing potentially drawback in the existing compact support radial basis functions fruitful paths while also examining additional random points to (CSRBF). Experiments with real time implementation clearly reduce the likelihood of settling for a local optimum. The show that the proposed technique not only requires less neural applied GA-trained RBF network for maximum power point processing units but also yields less MSE than RBF technique.tracking in photovoltaic applications was presented in [9].Keywords-component; genetic algorithm; fast charging; the This paper proposes an efficient approach for battery fastgeneralized regression neural network; radial basisfunctions; charging using GA-trained GRNN controller. Its main advantages over other algorithms are the simple topological I. INTRODUCTION structure, precision in nonlinear dynamics approximation, and fast learning capability. The remainder of the paper is In present the secondary or rechargeable batterles have organized as follows. Following this, a system configuration of been massively used in variety of electrical appliances with th prpoe fs-hrigmtoisadeednScinI.wide range of size and capacity. These batteries can be the proposed fast-charging method is addressed in Section II rechrge afer eingexhustd b pasingcurentthrughThe GA-trained GRNN controller for the fast-charging method recharged after being exhausted by passing current through is reviewed in Section III. In Section IV, the development of them in the opposite direction to that of discharge current [1]. fast-charging is discussed and experimental results with The widely used secondary batteries are Nickel Cadmium (Ni-emphasis on performance of the proposed GRNN controller are Cd), Nickel Metal Hydride (Ni-MH) and Lithium Ion (Li-Ion), given. Finally, conclusion is drawn in Section V.In principal, charging of a battery may be a straightforward task. In prac...