To address the interaction and collaborative optimization issues of the coupled power transportation network (CPTN) amid the rapid development of electric vehicles (EVs), this paper proposes a robust coupled power transportation network equilibrium (NE) approach for heterogeneous EVs considering charging station (CS) utilization. First, the uncertainty of actual traffic flow is modeled via the distributionally robust chance constraint (DRCC) based on the Chebyshev inequality. Based on M/M/N queuing theory, CS average waiting time is optimized to balance utilization of CS and alleviate power congestion. Then, to reduce redundant computations, a NE iterative solution method is proposed, which solves for the NE solution without the need to enumerate all feasible routes and CSs. Route and CS location constraints are proposed to guide rational CS selection for EVs. A feasible route and CS selection matrix for EVs is proposed, which effectively addresses the NE problem of heterogeneous EVs. A linearization method is developed to reduce the computational complexity of the NE solution approach. CS prices are tied to the locational marginal prices (LMP) of the distribution network (DN) to cut DN operating costs. Finally, the effectiveness of the proposed method is verified using real EV data, the simplified Chengdu and Anaheim transportation network (TN), as well as the modified IEEE 69-bus and 141-bus systems. The results demonstrate that the proposed method can rapidly solve the robust NE for heterogeneous EVs and effectively balance CS utilization.
Electric wheel loaders require both high traction torque and wide operating speed range. Conventional independent traction-working powertrain sizes each motor for its respective local peak load, leading to oversize motors and extra weight. This paper presents a novel multi-motor power sharing (MPS) powertrain to downsize motors. The MPS system allows active power transfer between traction and working pump drive. Clutch-based triple-motor mode and dual-motor mode enable high-torque loading and high-speed driving. A bi-level optimization framework combining a multi-objective genetic algorithm and quadratic programming is proposed to simultaneously minimize total installed motor power and optimize energy efficiency. Control schemes for two operating modes are further designed to guarantee speed tracking performance and reliable operation. Field-measured loading cycle and bench-tested motor efficiency map are used for parameter optimization and performance validation. Optimization results show that, compared with existing independent traction-working powertrain, the MPS powertrain reduces the total installed motor power by 30% with equivalent energy consumption, and achieves a 2.8% energy consumption reduction under the same rated power. Validation with actual low-speed V-type loading and high-speed cycles demonstrates the motor downsizing and energy saving superiority of the MPS powertrain.
IEEEexample:BSTcontrol Yaw stability and energy efficiency are two critical yet often competing objectives for four-wheel-independent-drive electric vehicles (4WID-EVs). Existing hierarchical control strategies typically address these objectives separately in the upper and lower layers, which can restrict coordination between upper-layer yaw regulation and lower-layer energy optimization. To overcome this limitation, this study proposes a hierarchical control framework that incorporates a dual-indicator phase-plane-based cross-layer coordination mechanism for systematic optimization of vehicle stability and energy efficiency. A physically interpretable stability indicator is introduced to facilitate adaptive, energy-efficient torque allocation. Additionally, a continuously differentiable version of this indicator is developed to enable smooth modulation of upper-layer yaw-moment intervention. Utilizing this coordinated stability characterization, a stability-aware, predefined-time prescribed-performance sliding-mode controller is designed to continuously regulate the desired yaw moment based on the vehicle’s stability condition. This approach reduces unnecessary control intervention while maintaining rapid convergence and robust disturbance rejection. Furthermore, a stability-dependent adaptive torque allocation strategy is formulated by jointly considering motor efficiency, tire utilization, and yaw-moment demand to dynamically balance handling stability and energy efficiency. Simulation and hardware-in-the-loop experiments demonstrate that the proposed framework enhances yaw stability and handling performance while reducing electrical energy consumption compared to representative existing methods. These findings indicate that the proposed cross-layer coordination mechanism enhances the interaction between motion regulation and energy optimization, offering an effective solution for simultaneously improving vehicle stability and energy efficiency under diverse driving conditions.
The output performance and stability of electrically excited synchronous motor (EESM) systems using magnetic coupled resonance wireless power transmission (MCR-WPT) are highly sensitive to coupler misalignment. Existing research often optimizes magnetic couplers and motor bodies as independent components, making it difficult to achieve optimal overall system performance. Therefore, an optimized design method based on an anti-misalignment magnetic coupler is proposed for the EESM, aiming to achieve system level integrated optimization of the motor and magnetic coupling device. Firstly, through theoretical analysis, the influence mechanism of coupling coefficient variation on system output power and efficiency was revealed, and the constraints between coupling coefficient and motor excitation parameters are clarified. Then, a multi-objective optimization model integrating coupling coefficient and motor excitation parameters was established to characterize the output power and efficiency of the system. On this basis, a misalignment rebound performance index with the coupling coefficient change rate as the core was constructed, and a genetic algorithm (NSGA-III) was used to achieve collaborative optimization design of the motor and coupler. The experimental results show that the collaborative optimization scheme outperforms traditional single objective optimization methods in terms of startup time, output power fluctuations, and system efficiency.
For open-winding permanent magnet synchronous motor (OW-PMSM) with a common DC bus, achieving precise and satisfactory current performance remains a key objective. The challenge lies in suppressing the inherent zero-sequence current (ZSC) while maintaining accurate fundamental current generation. Furthermore, the widely used model predictive current control (MPCC) method is often burdened by high computational complexity, which limits its practical application. To address these issues, a simplified MPCC (SMPCC) scheme is proposed in this article. First, the optimization objective is converted from current to voltage to realize a standard deadbeat form, thus enhancing the current tracking performance. Then, the cost function is redesigned to directly determine the optimal weighting factors of the basic voltage vectors without iterative searching, and the preliminary switching durations for the six bridge arms in the dual-inverter topology are calculated to ensure accurate fundamental current output. Finally, based on the zero-sequence reference voltage, an additional zero-sequence voltage is injected into the system considering the inverter’s limitations, and the duration of each bridge arm is further optimized to suppress the ZSC. The comparative experimental results verify the effectiveness, superiority and simplicity of the proposed method.
