Abstract:
Rapid increase in prices, decrease in resources of oil and natural gas, increasing global
warming and pollution have shifted focus of the community towards plug-in hybrid
electric vehicles. Plug-in fuel cell hybrid electric vehicle utilizes a fuel cell as the main
power source while battery and ultra-capacitor as auxiliary power sources. Plug-in
mains are used to recharge the batteries. The robustness of the model predictive
control of induction machine can be increased by using the ultra-local model as it
expresses system output in term of its inputs without requiring any specific
information about the plant. Observer-based controllers utilize available sensors to
estimate state variables, eliminating the need for all sensors. Adaptive backstepping
sliding mode control technique is employed to generate control laws for DC-DC
converters for fuel cell, ultra-capacitor, battery and plug-in mains. A finite set model
predictive current control of induction machine has been proposed as it has a fast
response, less computation and has a relatively simpler controller design. Neutral
point clamped inverter (three level inverter) has been proposed to mitigate common
mode voltage generated at the motor bearing. An integral sliding mode observer has
also been proposed to estimate the stator current and unknown vector variable of the
ultra-local model. Lyapunov’s theory is employed to ensure the asymptotic stability of
the proposed system. The control scheme has been validated in MATLAB/Simulink
(R2022b) using the ODE-45 solver which shows off the comparison of the proposed
controller with conventional model predictive control, two-level inverter, backstepping
controller and its variants. The results indicate that the proposed controller
outperforms the rest of its counterparts in tracking, fast convergence and reachability
and exhibits better dynamical performance.
