earticle

영어

In dealing with the increment in environment pollution and source consumption, research has focused on the application of renewable energy source. Batteries, especially power batteries, which has great prospects in the fields, are among the attention. Rechargeable batteries are widely used in many electrical systems to store and deliver energy. However, there is a wide variety of Power Batteries and they have different weak Points. In order to develop and apply battery in a more efficient and appropriate method, their response to various operating conditions must be understood. Knowing the battery temperature variation in electric vehicles (EVs) is very important issue. Temperature depends on ambient temperature, charging current and charging time. Recently neural networks have been successful used for power system applications. In the literature, there are many neural networks for power system applications. However, Back Propagation (BP) has demonstrated better capabilities. This paper presents neural network for temperature estimation of power batteries. The main contribution of this paper is consideration of non-uniform temperature field and the temperature effect in batteries. In addition, the results of estimation and actual measured values are compared, proving the feasibility and accuracy of the method.