Adaptive Nonlinear Control of Salient-Pole PMSM for Hybrid Electric Vehicle Applications: Theory and Experiments

Fakir, Chaimae El; Idrissi, Zakariae El; Lassioui, Abdellah; Belhaj, F.Z.; Gaouzi, Khawla; Fadil, H. El; Rachid, Aziz

doi:10.3390/wevj14020030

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…The force exerted by the air on a vehicle's body is referred to as drag force, and it is predominantly influenced by the vehicle's geometry. The equations involved in the formula for calculating aerodynamic drag are as follows and the specification electric vehicle is expressed in table 2.1 [22][23].…”

Section: Aerodynamic Drag Forcementioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

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This study presents a novel approach to enhance the speed control performance of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicles (EVs) through the implementation of a Genetic Algorithm (GA)-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFOPID) controller. PMSM technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in EV drive systems. However, the inherent non-linearity, dynamics, and uncertainties of PMSMs pose significant control challenges. The proposed GA-AFFOPID controller, tuned using a genetic algorithm, exhibits superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances. Comparative analysis with traditional control methods demonstrates the exceptional performance of the GA-AFFOPID controller, achieving a 1.796% lower overshoot, 0.97% faster rise time, 4.25% lower steady-state error, and 0.35% faster settling time compared to the adaptive fuzzy fractional order PID controller. These results highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications, paving the way for improved energy efficiency and overall performance of electric vehicle propulsion systems.

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Section: Aerodynamic Drag Forcementioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Presently, commonly employed control strategies include rule-based approaches (involving logical thresholds and self-judgment segmentation), intelligent strategies (such as fuzzy logic and neural networks), and offline optimization methods (such as dynamic programming and simulated annealing algorithms) [7,8]. Wu X, Fakir, C. E et al introduced an adaptive nonlinear control approach aimed at enhancing the overall efficiency of power systems and motors [9][10][11]. Silva and Jie Li adopted a multi-objective optimization control method to ameliorate energy conversion efficiency, prevent power battery degradation, and achieve the real-time optimization of energy consumption [12,13].…”

Section: Prefacementioning

confidence: 99%

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

Zhang,

Tang,

Zhang

et al. 2024

Energies

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In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

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“…Estimating the vision-based motion of unmanned air vehicles, automatic electric throttles of DC motors, PEM fuel cells, electric machines, and various other real-life dynamical systems is challenging in the current era [1], [2], [3], [4]. To design a feedback system, it is always desirable to find the unknown or missing states of the system; however, for several systems, we cannot find system states owing to the absence The associate editor coordinating the review of this manuscript and approving it for publication was Mouquan Shen .…”

Section: Introductionmentioning

confidence: 99%

Robust Estimation-Based Control for Generalized One-Sided Lipschitz Nonlinear Systems in the Presence of Output Delays

Mirza,

Ahmad,

Masood

et al. 2024

IEEE Access

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This study presents a robust estimation-based control approach for nonlinear systems with generalized one-sided Lipschitz (OSL) conditions projected for delay measurement. A control scheme was devised for this family of nonlinear systems by deploying the Lyapunov-Krasovskii (LK) method for the delayed system and by encompassing the generalized OSL inequality without using the quadratic inner boundedness (QIB) condition to overcome the conservatism introduced by the QIB condition. The stability of the resulting system is attained by employing a delay-range-dependent technique, while the derivative of the LK functional is scouted using Wirtinger's inequality, which shrinks the conservativeness of typical Jensen's inequality, resulting in stability in the form of linear matrix inequalities (LMIs). Moreover, a sufficient and necessary solution for the main results was achieved through a decoupling mechanism to acquire the controller and estimator gains simultaneously using iterative optimization tools. To attenuate the effects of external disturbances on the system, the L2 gain of the error with reference to the system was computed and incorporated into the dynamics. Furthermore, the LMI-based results are handled using the cone complementary linearization (CCL) method to authenticate the controller and observer gains via convex optimization. Finally, a numerical example demonstrates the success of the presented robust observer control formation for generalized OSL nonlinear systems in the presence of output delays.

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Adaptive Nonlinear Control of Salient-Pole PMSM for Hybrid Electric Vehicle Applications: Theory and Experiments

Cited by 5 publications

References 29 publications

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

Robust Estimation-Based Control for Generalized One-Sided Lipschitz Nonlinear Systems in the Presence of Output Delays

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