Due to reduction in the non-renewable sources such as petrol, diesel and increase in pollution levels, we need to find an alternate way to drive the automotive. One of the best alternatives is to use pure electric vehicle which has zero emission and requires electricity as a power source instead of non-renewable sources. These electric vehicles get the required power from batteries but they face challenges like lower range and less battery life, and they fail to provide the same acceleration as of IC engines. If batteries are used with ultracapacitor, then they can meet the power requirements required by the driver. Regenerative braking is a way to restore power at the time of deceleration but it provides a lot of charge in a short period of time, but battery cannot accommodate higher amount of charge in less charge. Ultracapacitors, on the other hand, can be charged using this regenerative braking method while the automobile is in motion, and excess charge can then be transferred to battery for charging. An intelligent energy management system is necessary to take these decisions of discharging and charging of this ultracapacitor and battery HESS, in order to increase the range of vehicle and battery life. Machine learning will be used to design and train the controller for the vehicle to take decision of its own when facing real-time situations.

R. Ramakrishnan

Papers

An Intelligent Energy Management Strategy for Electric Vehicle Battery/Ultracapacitor Hybrid Storage System Using Machine Learning Approach