DECISION MAKING IMPROVEMENT USING FUZZY NEURAL NETWORK FOR ELECTRIC VEHICLE CONTROLLERS

The upcoming need for electric car development is essential in Indonesia. This is because the amount of energy from oil fuel is increasingly limited. To anticipate an energy crisis from oil fuel, especially in motorized vehicles, namely cars, electricity becomes more flexible energy for cars. One of the important parts of an electric car is the control system. This research focuses on the speed control used by electric vehicles so that the speed of the car can change gradually, thereby increasing the comfort and safety of electric vehicles. This makes electric car control system becomes more responsive. The method used in speed control is to combine fuzzy logic and artificial neural networks. The combination of the two methods gives satisfactory results for the artificial neural network model with an MSE value of 0.02566, but it is still not satisfactory for the fuzzy logic model which has an error of 1.732. Improvements to the membership function in the fuzzy logic model need to be done by using more data. In the future, the implementation of the model in the control system needs to be done to get real-time data