This paper presents a novel low-cost and simple phase-current reconstruction algorithm for three-phase induction motor (IM) under direct torque control (DTC) using the information obtained from only one shunt resistor (in series with low-side switches in a conventional three-phase inverter). The aim is to develop a low-cost high-performance IM drive. The proposed algorithm is robust and very simple. It uses the dc current to reconstruct the stator currents needed to estimate the motor flux and the electromagnetic torque. A theoretical concept is developed, the modified look-up table is presented, and current-access tables are designed and used in the phase-current reconstruction. The limitations are also studied and presented.

This paper proposes two intelligent torque distribution strategies based on particle swarm optimization (PSO) and fuzzy logic control (FLC) to provide convenient torque allocation that maximizes hybrid electric vehicle (HEV) propulsion power. PSO torque distribution strategy uses torque transfer ratio (TTR) as a fitness function to select the best torque candidates and differential arrangements that maximize HEV propulsion torque. A proposed FLC controller with adequate membership functions is designed to ensure convenient torque vectoring across vehicle wheels. A new coordinated switching strategy is proposed in this paper to address the undesired transient ripples occurring during drivetrain commutations and power source switchings. The proposed coordinated switching strategy controls the switching period duration through transition functions fitting the transient dynamics of power sources. In non-uniform surfaces, intelligent torque allocation strategies converted $84\sim 86$ % of the generated torque into propulsion torque whereas the equal torque distribution strategy yielded a torque transfer ratio of 50%. Thanks to the proposed coordinated switching strategy, DC bus voltage ripples were reduced to a narrow band of $\pm ~5\text{V}$ , transient power ripples were limited to a narrow band of 600 W and torque jerks were almost suppressed. Real-time simulation using the RT LAB platform confirms that the proposed coordinated switching strategy has reduced transient torque overshoot from 69% to almost zero and this is expected to improve HEV driving comfort.

In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods.

This paper presents a proposed fuzzy energy management strategy developed for a battery−super capacitor electric vehicle. In addition to providing different driving modes, the proposed strategy delivers the suitable type and amount of power to the vehicle. Furthermore, the proposed strategy takes into account possible failures in vehicle power sources. The speed and torque of the HEV traction machine are simultaneously controlled using a genetic algorithm that provides simultaneous tuning via the use of newly proposed cost functions that give the designer the ability to tradeoff and prioritize between the design variables to be minimized. The simulation results show that the intelligent speed and torque control and the fuzzy power management strategy improved the vehicle’s performance in terms of ripple minimization. The real-time simulation is conducted using the RT LAB simulator, and the results obtained correspond to those obtained in the numerical simulation using MATLAB/Simulink.

This paper presents a novel method for phase ac motor current reconstructing with a single current sensor in three-phase direct matrix converter (DMC) drive system using space vector modulation control technique. The main goal is to reduce the cost and to improve the reliability of drive systems that involve closed-loop control strategy. For this purpose, new structure and algorithm are developed which divide the zero-vector application time in two intervals and measure the phase currents using a new placement of the single Hall current sensor in DMC. These proposals constitute a good solution in the low-power-range DMC (below 15 kW), where reducing size and cost is the key objective. The simulation of the three-phase to three-phase DMC feeding an induction motor was done to demonstrate the advantages of the proposed system. Experiments were carried out owing to a DS1104 control board to check the validity of the proposed method.