Batteries and Ultracapacitors

Research begins into how to optimize the energy storage system of an electric vehicle (EV). It has been discussed that ultracapacitors and batteries need to work in conjunction to create an attractive solution. Just as important as the energy storage elements themselves is how to interconnect and control these two elements. Efficient charging/discharging of each element requires a system which knows the state of charge (SOC) of each element, current power requirements, and a driving pattern history. This controller will be able to properly handle power flow and extend battery life, thus leading to lower operating costs for an EV. This hybrid controller will be known as a Hybrid Energy Storage System (HESS).

Energy Storage / Power Systems Control for PHEVs

Plug-in Hybrid Electric Vehicles (PHEV)s are a form of electric vehicle which include an electric drive as well as gasoline-powered generator to recharge the energy storage elements which supply power to the electric drive. The two major elements that comprise Energy Storage systems are ultacapacitors and batteries. Batteries have a higher energy density than ultacapacitors but are unable to supply large, quick bursts of energy and also unable to properly store the power generated during regenerative braking. Ultracapacitors are able to charge/discharge very quickly, making them ideal for power delivery when the driver requires large forward-motion acceleration and also for storing the electrical energy generated during regenerative braking.

In an effort to maximize the efficiency of the Energy Storage system, an optimal control strategy should be implemented to control the charging/discharging of the system depending upon the vehicles current state and driver inputs. This optimal control strategy would direct the flow of energy from the vehicle's regenerative braking system to the ultracapacitor bank and should discharge the battery and ultracapacitors depending upon current driving habits.

This control system could be implemented with Fuzzy Logic controllers, Neural Network controllers, or a dynamic gain controller.

TO RESEARCH: Regenerative Braking, Fuzzy Logic, Neural Network / Adaptive Control.