Meta-Heuristic Optimization Techniques for control of Hybrid Electrical Energy Sources.

Background: Hybrid Electric Vehicle (HEV) uses two sources of energy namely primary and auxiliary source. If only the battery bank is used as a power source of vehicle, then the performance of the vehicle is not satisfactory due to continuous charging and discharging mode, also the direction and amount of the battery current are changed continuously causing stress in the battery bank. Objectives: Objective of the proposed work to control the power flow between battery and supercapacitor (SC) so as the dc bus voltage remains constant. The error is speed is to be minimised for desired operating conditions. Methods: The objective is achieved by using the unidirectional buck-boost converter with battery bank and bidirectional buck-boost converter with SC Auxiliary source provides the energy during acceleration and store the energy at the time of braking. The controller with tuned PID parameters ensures the responses of quickly with minimal overshoot. This paper presents control technique for a Battery and Supercapacitor operated HEV and the tuning of PID controller using Teaching Learning Based Optimization (TLBO), particle swarm optimization (PSO), and Gray Wolf Optimization (GWO). The results are compared under various operating condition on the performance parameters integral absolute error (IAE), integral squared error (ISE), integral of the time-weighted absolute of the error (ITAE) and integral of the time-squired of the error (ITSE). Conclusions: The result suggests the PID parameters tuned with GWO technique give minimum error coefficients, The error is further reduced when SC is introduced with the battery as input source. [ABSTRACT FROM AUTHOR]