Currently, the electrical mobility and the intermittent power generation facilities problem are two of the main purposes of batteries. Batteries, in general terms, have a complex behavior. Due to the usual electrochemical nature of batteries, several tests are made to check their performance, and it is very useful to know a priori how they are working in each case. By checking the battery temperatures for a specific voltage and current value, this work describes a hybrid intelligent model aimed at making fault detection of a LFP (Lithium Iron Phosphate - LiFePO4) power cell type, used in Electric Vehicles. A large set of operating points is obtained from a real system to create the dataset for the operation range of the power cell. Clusters of the different behavior zones have been obtained to accomplish the solution. Some simple regression methods have been applied for each cluster. Polynomial Regression, Artificial Neural Networks and Support Vector Regression were the combined techniques to develop the hybrid intelligent model proposed. The novel hybrid model allows to be achieved good results in all the operating range, detecting all the faults tested.