Efficient Power Management of Electric Drive system using Fuzzy Logic Control

  • S. Indirani, Pratikshya Mohanty, Souhardya Moulik

Abstract

Electric vehicle (EV) is the currently trending and most talked about technology in the world. The pace of innovation in this field is leading to advancement in production technology as well as optimization of vehicles. The electric vehicles (EV) make use of an electric motor instead of an IC (Internal Combustion) engine as compared to the conventional fuel based vehicles. A gasoline vehicle uses a spark ignited (SI) engine and a diesel vehicle uses compression ignited (CI) engine. In these engines, the fuel is ignited combined with air and combustion takes place which leads to the emission of toxic gases which harm the environment as well as affect the health of human and animal life.Hence, the world is going for a revolution in the automobile industry by replacing current generation automobile with electric vehicles (EV). Electric cars such as “Nissan Leaf”, “Ford Focus Electric”, “Tesla Model S” and“Chevrolet Volt”have already been introduced in the market and they have been a proof that electric vehicles (EV) have lesser wear and tear due to lesser mobile parts, lesser heat generation, more economical and greater performance. The idea behind this work is to develop a strategy to optimize power requirements using a“fuzzy logic controller (FLC)”.By making use of this, the performance of this electric drive system is good enough as compared to that of an “IC engine vehicle”. The proposed idea is to implement fuzzy logic on the vehicle powertrain to improve efficiency as well as drawing less voltage from the power source which is the battery using the techniques of voltage matching and forward driving. The adopted methodology will ensure that high power requirements are mettakingthe various losses involved into consideration.

Published
2020-06-01
How to Cite
S. Indirani, Pratikshya Mohanty, Souhardya Moulik. (2020). Efficient Power Management of Electric Drive system using Fuzzy Logic Control. International Journal of Advanced Science and Technology, 29(08), 2292 - 2300. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/23389
Section
Articles