The performance of brushless DC (BLDC) motor drives in electric vehicles is highly dependent on effective speed control, which becomes challenging under dynamic load and environmental variations. However, conventional control strategies typically rely on offline parameter tuning and lack real-time adaptability, making them less effective in rapidly changing operating conditions. To overcome these limitations, this paper presents a novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) self-tuning fractional-order proportional–integral (FOPI) controller for precise speed control of BLDC motors in electric vehicles. In the proposed method, the proportional and integral gains are optimized online using the PSO algorithm, while a fuzzy logic controller continuously adjusts the fractional-order integral coefficient. This combination leverages the local adaptability of fuzzy logic and the global optimization capabilities of PSO, enabling real-time adaptation to system variations. To assess its effectiveness, the proposed approach is compared against three conventional FOPI controllers tuned offline using established metaheuristic methods: PSO, Bee Colony Optimization (BCO), and Grey Wolf Optimization (GWO). For validation, a hardware-in-the-loop (HIL) platform was implemented using two dSPACE CLP1104 systems, along with a dedicated test bench that replicates realistic driving scenarios. The controller’s performance was evaluated under various conditions, including dynamic speed transitions, external load disturbances, and torque ripple behavior, following the New European Driving Cycle (NEDC) profile. The experimental results demonstrate that the proposed controller achieves faster convergence, stronger disturbance rejection, reduced torque ripple, and shorter rise time compared to offline-tuned controllers. These results validate the effectiveness and practical relevance of this adaptive control strategy for high-precision, real-time BLDC motor applications in electric vehicles.

This paper presents a novel methodology for minimizing power losses in brushless DC (BLDC) motors through the implementation of adaptive flux control techniques. Conventional motor control strategies, such as direct torque control (DTC), typically employ fixed flux values, which often result in suboptimal performance, particularly under dynamic load and speed variations. To mitigate this inherent limitation, two adaptive flux control methods are introduced: incremental conductance (IncCond) and fuzzy logic. These proposed strategies facilitate real-time dynamic adjustment of the stator flux, thereby optimizing motor performance and significantly enhancing system efficiency. Experimental validation confirms the efficacy of these adaptive techniques, demonstrating substantial improvements in power loss reduction and overall efficiency when compared to traditional fixed flux control strategies. Notably, the fuzzy logic control strategy achieves the highest efficiency, registering a system efficiency of $66.59$%, which surpasses both the incremental conductance method and conventional fixed flux control. These findings underscore the considerable potential of adaptive flux control in applications where energy efficiency is paramount, including electric vehicles and renewable energy-driven systems.

Secure real-time communication is a critical requirement in modern electric vehicle (EV) networks. These networks transmit safety-critical control commands through vulnerable in-vehicle communication channels. This study proposes a novel three-dimensional symmetric chaotic system for high-security EV communication. The system exhibits extensive multistability and symmetric double-wing attractors. To enhance dynamical complexity, its parameters are optimized using chaotic-enhanced particle swarm optimization (C-PSO). The largest Lyapunov exponent is used as the optimization objective. A fixed-time nonlinear controller is designed for rapid drive–response synchronization. The settling-time bound is independent of the initial conditions. The proposed method is evaluated through realistic Controller Area Network (CAN) bus simulations. These simulations include 12-bit quantization and a 1 ms sampling period. The experimental results show synchronization within 0.057 s. The recovered signal achieves an MSE of $1.202\times {10}^{-4}$. The encrypted signal reaches a Shannon entropy of 7.9904. These results confirm accurate recovery, strong randomness, and improved resistance to cryptographic attacks.

To improve the control performances of permanent magnet synchronous motors (PMSMs), this paper combines the sliding mode controller (SMC) and model predictive controller (MPC) to balance the contradictions between stability and dynamic performance, reduce current and torque ripples, and provide precise, complete current and speed control. At first, the MPC is employed to control the current and speed of motors, which could solve the chattering issue of the traditional sliding mode control method and effectively guarantee the stability of the control system. Second, a sliding mode observer (SMO) is incorporated to eliminate the interference of flux variation and then reduce the current and torque ripples. Meanwhile, the sliding mode controller is used to adjust the speed of motors and generate an adjustable switching surface to implement rapid and accurate control of the motor. This paper presents the design and realization process of each controller in detail, respectively shows the design principle of the combined controller and verifies the control effect of the combined controller under different motor speeds through simulation, and analyzes the control advantages and its application. The simulation results prove that the combined controller reduces the current and torque ripples to a certain extent in speed regulation, and the control performance is more stable and robust over a wide range of speed. In addition, this paper discusses the practical application of combined controllers in industrial and electric vehicle motor control in the later stages and presents the influence and significance of combined controllers on the efficiency and stability of PMSM control. From the perspective of application and system integration, the integrated design of SMC, SMO, and MPC can further promote the development of high-performance PMSM in the industrial and automotive industries in the future.