Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Shen, Yongpeng; He, Zhendong; Liu, Dongqi; Xu, Bin

doi:10.3390/en9020090

Cited by 9 publications

(3 citation statements)

References 33 publications

(37 reference statements)

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“…The number of particles in the MOPSO algorithm is usually set between 100 and 200, which can guarantee desirable optimization results for a simple optimization problem. 38–40 However, this study addresses a complex optimization problem involving a discontinuous and nested double-loop optimization process. If the number of particles in the inner swarm is too small, the calculation precision of the intervals of f 1 ( X , U ) cannot be ensured.…”

Section: Optimization Of Hard-point Coordinates Based On the Dl-mopsomentioning

confidence: 99%

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Shi

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In the traditional design of a vehicle suspension system, mechanical parameters, such as spring stiffness and tire radial stiffness, are determined before optimizing the coordinates of suspension hard-points based on handling and stability. However, the mechanical parameters of vehicle suspension systems vary with repeated changes in ambient temperature and loading conditions. Consequently, the original hard-point coordinates cannot guarantee satisfactory handling and stability after long-term vehicle use. To address this issue, an A0 class vehicle with a MacPherson suspension was modeled in ADAMS/Car and was validated against the results of field tests. The relationships between the maximum absolute values of front wheel alignment parameters during parallel wheel travel analysis and the coordinates of suspension hard-points, spring stiffness, and tire radial stiffness were determined using support vector regression. Next, a multi-objective optimization function was formulated based on the interval analysis method. Finally, a novel double-loop multi-objective particle swarm optimization algorithm was designed for global optimization of hard-point coordinates. The ADAMS simulation results indicated that, compared with the vehicle with the original hard-point coordinates, the double-loop multi-objective particle swarm optimization algorithm, the traditional multi-objective particle swarm optimization algorithm, and the genetic algorithm can effectively reduce the variation ranges of front wheel alignment parameters, regardless of the values of the mechanical parameters. It also showed that the double-loop multi-objective particle swarm optimization algorithm performs better than the traditional multi-objective particle swarm optimization and the genetic algorithm with both the original and changed mechanical parameters. Except for an increase in the variation range of the caster angle by 9.24–10.69%, the variation ranges of the toe angle, camber angle, and kingpin inclination angle under various mechanical parameters using the double-loop multi-objective particle swarm optimization algorithm were observed to be reduced by 50.45–79.39%, 1.84–4.24%, and 2.11–2.96%, respectively. Last but not least, the proposed double-loop multi-objective particle swarm optimization algorithm provided better handling, stability, and ride comfort values than the traditional multi-objective particle swarm optimization algorithm and the genetic algorithm.

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Section: Optimization Of Hard-point Coordinates Based On the Dl-mopsomentioning

confidence: 99%

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Shi

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…The MOPSO algorithm is proposed by Coello et al [29] in 2004 to solve the multi-objective optimization problem and has been widely researched and applied [30][31][32]. Here, the MOPSO is employed because of its high searching ability and low time complexity [29].…”

Section: Multi-object Optimizationmentioning

confidence: 99%

Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization

Min

Sun

et al. 2017

Energies

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Abstract:Charging performance affects the commercial application of electric vehicles (EVs) significantly. This paper presents an optimal charging strategy for Li-ion batteries based on the voltage-based multistage constant current (VMCC) charging strategy. In order to satisfy the different charging demands of the EV users for charging time, charged capacity and energy loss, the multi-objective particle swarm optimization (MOPSO) algorithm is employed and the influences of charging stage number, charging cut-off voltage and weight factors of different charging goals are analyzed. Comparison experiments of the proposed charging strategy and the traditional normal and fast charging strategies are carried out. The experimental results demonstrate that the traditional normal and fast charging strategies can only satisfy a small range of EV users' charging demand well while the proposed charging strategy can satisfy the whole range of the charging demand well. The relative increase in charging performance of the proposed charging strategy can reach more than 80% when compared to the normal and fast charging dependently.

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“…The engine speed is between 2500 rpm and 4000 rpm, while the engine torque is between 25 N℘m and 45 N℘m, the region that is defined as the fuel economy region. The engine operating area is relative narrow compared with PMSG [18]. is to keep the RE operating in the high efficiency region.…”

Section: Erev Efficiency Improvement Designmentioning

confidence: 99%

A Proportional Resonant Control Strategy for Efficiency Improvement in Extended Range Electric Vehicles

Wang

Sun

et al. 2017

Energies

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Abstract:The key to control the range extender generation system is to improve the efficiency and reduce the emissions of the electric vehicle (EV). In this paper, based on the purpose of efficiency optimization, both engine and generator are matched to get a public high efficiency region, and a partial power following control strategy was presented. The engine speed is constant in the defined power range, so the output power regulation of the range extender is only realized by the adjustment of the torque of the generator. Engine speed and generator torque were decoupled. An improved proportional resonant (PR) controller is adopted to achieve fast output power regulation. In order to ensure the response characteristics of the control system and to improve the robustness, the impacts on system's characteristics and stability caused by PR controller and parameters in the inner-current loop were analyzed via frequency response characteristics. A pre-Tustin with deviation compensation is proposed for PR controller's discretization. A stable and robust power following control method is obtained for the range extender control system. Finally, simulation and experiment of the proposed control strategy illustrated its feasibility and correctness.

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Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Cited by 9 publications

References 33 publications

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization

A Proportional Resonant Control Strategy for Efficiency Improvement in Extended Range Electric Vehicles

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