On Selecting a Method of Constructing a Fuzzy Model for Prediction of the Battery State

Abstract

Battery-powered electric vehicles are being currently considered to replace conventional non-environmental vehicles. Batteries could not be used without a control system, which development requires a mathematical model to predict the state of a separate battery. The Takagi --- Sugeno fuzzy system (fuzzy model) could become such a model. There are methods for automatic construction of fuzzy models according to the table of observations. However, unambiguous criteriafor selecting the appropriate method in each specific case are missing. The problem is considered of determining a method making it possible to obtain a fuzzy model that predicts the lithiumion battery voltage from the load current and the state of charge when discharging with direct current with the lowest meansquare error. The existing methods and their classes were reviewed, and five methods were selected for comparison. Prediction error by all the models obtained was unevenly distributed along the axis of the charge state, and it took the highest values in the range of 97--100 %. The lowest meansquare error was registered in the model built by the combined method using subtractive clustering, least squares method and adaptive network based on the adaptive neurofuzzy inference system. The error in such model was changing stepwise, which was associated with feature of the subtractive clustering algorithm, i.e., the formed clusters were of the same size