Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics.

The requirement of sustainable mobility and a clean environment has accelerated the development and adoption of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as an alternative, practical and promising solution against conventional vehicles globally. Such alternative energy vehicles not only provide a critical solution to mitigate fossil fuel dependency and reduce greenhouse gas emissions, but also contribute to producing an energy-efficient transportation system. However, the operational performance, efficiency, and cost-effectiveness of EVs and HEVs are hugely dependent on their powertrain architectures, selection of traction motors and associated control techniques. This paper systematically compares major hybrid architectures: series, parallel, and series–parallel, plug-in, as well as battery and fuel cell electric vehicle platforms, highlighting trade-offs in component sizing, cost, and system integration complexity. The paper critically analyses traction motor technologies with respect to torque–speed characteristics, efficiency behavior, material constraints, and power density. A detailed comparative assessment of traction motor technologies is presented. Furthermore, classical and advanced motor control strategies, including field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC) and AI-enhanced control frameworks, are evaluated with respect to transient performance, robustness, computational requirements, and scalability. The review identifies key technological milestones, emerging next-generation drive technologies, existing limitations, and unresolved research challenges. Finally, critical research gaps and future development pathways are articulated to support the advancement of high-efficiency, reliable, and cost-effective EV/HEV powertrain systems.

This paper proposes a new fuzzy logic-based rotor flux model reference adaptive system (FLC-MRAS) for rotor speed estimation in induction motor drives, replacing the constant-gain PI controller used in conventional MRAS schemes. The proposed observer simultaneously incorporates both rotor flux and electromagnetic torque errors to enhance estimation accuracy and robustness against load torque disturbances. A nonlinear Mamdani-type fuzzy logic controller (FLC) with two inputs and one output, employing triangular membership functions and seven fuzzy sets, is adopted. The effectiveness and useful operational performance of the proposed approach is examined through extensive simulation cases under various vehicle speed driving profiles and load torque conditions using an indirect vector-controlled induction motor drive. In order to investigate the effective operational performance of a speed estimator, different cases are prepared according to the vehicle requirements. To examine the robustness of the proposed observer under realistic operating conditions, rotor resistance variation is incorporated into the simulation framework. This approach allows assessment of MRAS performance under practical nonlinearities and parameter uncertainties encountered in real applications. Comparative results demonstrate superior speed regulation and speed tracking, reduced estimation error, and faster convergence of the adaptive tuning signal for better speed estimation compared to the PI-MRAS observer. The proposed scheme provides the suitable choice of traction drive adoption for electric vehicle (EV) applications.

This paper presents a piecewise-linear single-input fuzzy-controller-based model reference adaptive system (SFC-MRAS) for speed estimation in speed-sensorless vector-controlled induction motor drives for electric vehicle (EV) traction applications. Conventional PI-MRAS observers employ fixed-gain adaptation, which may limit estimation performance under rapid speed variations, load disturbances, and motor parameter uncertainties. To enhance nonlinear adaptation capability while reducing computational requirements, a conventional dual-input fuzzy adaptation mechanism is transformed into a signed-distance-based single-input structure and subsequently reformulated as a piecewise-linear control law. An auxiliary electromagnetic torque-error compensation loop is also incorporated to provide a filtered speed-equivalent correction during dynamic operating conditions. The proposed SFC-MRAS is comprehensively evaluated against conventional PI-MRAS and dual-input fuzzy-controller-based MRAS (DFC-MRAS) under acceleration, load disturbances, speed transitions, forward-reverse operation, and motor parameter variations. In addition, an EV-oriented validation case derives the motor reference speed and load torque from vehicle longitudinal dynamics, incorporating acceleration, cruising, deceleration/braking, re-acceleration, road grade, and drivetrain characteristics. The comparative results demonstrate that the proposed SFC-MRAS achieves improved speed-estimation and tracking performance, faster convergence, and enhanced robustness compared with PI-MRAS, while providing competitive estimation performance with substantially lower computational burden than DFC-MRAS. The results further demonstrate that the proposed single-input piecewise-linear formulation preserves the essential nonlinear adaptation behavior while simplifying the online implementation. Overall, the proposed SFC-MRAS provides an effective balance among estimation accuracy, dynamic robustness, computational efficiency, and EV-oriented operating capability for induction-motor-based sensorless traction drives.