A Neuro-Fuzzy System for Power Supply Control

Abstract

Automatic control of power supply sources is considered. Different types of batteries behave differently inside a device and require specific optimal conditions to perform efficiently. Using the wrong operating mode of a battery can lead to malfunctions within it, which may affect the device’s performance. Adaptive methods for optimizing power modes are presented. The adaptivity of the method is achieved by using a baseline, which is based on artificial intelligence concepts. These concepts are neural networks and fuzzy logic, which are used to create systems capable of learning and being interpretable. This method is able to execute control over a batteries state and take actions which are required to save a battery and prolong its lifespan. The adaptivity of the system is demonstrated by the ability to take information about the environment into account.

Keywords

• neural networks
• fuzzy logic
• neuro-fuzzy systems
• power optimization
• battery management systems