The inset-surface permanent magnet(ISPM)machine can achieve the desired electromagnetic performance according to the traditional deterministic ***,the reliability and quality of the machine may be affected by the esse...
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The inset-surface permanent magnet(ISPM)machine can achieve the desired electromagnetic performance according to the traditional deterministic ***,the reliability and quality of the machine may be affected by the essential manufacturing tolerances and unavoidable noise factors in mass *** address this weakness,a comprehensive multi-objectiveoptimization design method is proposed,in which robust optimization is performed after the deterministic *** response surface method is first adopted to establish the optimizationobjective ***,the sample points are obtained via Monte Carlo simulation considering the design-variable *** Design for Six Sigma approach is adopted to ensure the robustness of the design ***,the barebones multi-objective particle swarm optimization algorithm is used to obtain a compromise solution.A prototype is manufactured to evaluate the effectiveness of the proposed *** to the finite-element analysis and experimental tests,the electromagnetic performance and reliability of the machine are significantly enhanced with the proposed method.
To achieve the optimal energy allocation for the auxiliary power unit (APU) and battery of a range extended electric vehicle, a novel hybrid-point-line energy management strategy (H-P-LEMS) has been proposed from a mu...
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To achieve the optimal energy allocation for the auxiliary power unit (APU) and battery of a range extended electric vehicle, a novel hybrid-point-line energy management strategy (H-P-LEMS) has been proposed from a multi-scale view. First, a multi-objectiveoptimization (MOO) model is established which takes into account energy consumption, emissions and battery life. The barebones multi-objective particle swarm optimization is applied for solving the MOO problem. And a dynamic programming optimized algorithm is applied to obtain the optimal curve/area of APU to establish objective function of MOO. Then, an adaptive approach uses a fuzzy logic controller with the battery consideration to adjust parameters in real time. Simulation results show that there is a clear conflict that three optimizationobjectives cannot be optimal at the same time and the final optimization solution with optimal comprehensive evaluation index is selected to evaluate the performance of the proposed methodology. Finally, the simulation and experimental results thoroughly indicate that the proposed H-P-LEMS has better balance than conventional rule-based energy management strategy (EMS). As expected, economy improvement, emission reduction and prolonging the battery service life are kept in balance effectively. And this result can be used to develop EMS to improve comprehensive performance levels. (c) 2022 Elsevier Ltd. All rights reserved.
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