Parametric optimization of novel electric–hydraulic hybrid steering system based on a shuffled particle swarm optimization algorithm

Zhao, Wanzhong; Luan, Zhongkai; Wang, Chunyan

doi:10.1016/j.jclepro.2018.03.180

Cited by 46 publications

(19 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a multiobjective optimization problem, the objectives usually conflict with each other. Inspired by Reference [46], we established an evaluation index, including fuel consumption and acceleration time, to determine the best driveline parameters with respect to the comprehensive performance of the PHEB, which is governed by…”

Section: Optimization Results Analysismentioning

confidence: 99%

“…To analyze the impact of the driveline parameters on vehicle performance, a design of experiment (DOE) methodology was employed while using the optimal Latin hypercube (OLH) algorithm to randomly generate 500 samples in a five-dimensional vector space [46], where each row vector can represent a combination of the speed ratios of the AMT and final drive. The responses of fuel consumption and acceleration time with respect to the speed ratios are respectively shown in Figures 10 and 11.…”

Section: Boundariesmentioning

confidence: 99%

“…It could be found that the speed ratios and the evaluation index gradually converged with increments of the iteration steps, and both became stable after 600 iteration steps. The optimal speed ratio could be obtained and corresponded to the best-found point with a minimum evaluation index, at the 854th iteration step [46,47]. The optimization process is shown in Figure 13, where the maximum iterations endured for 1000 generations.…”

Section: Optionmentioning

confidence: 99%

See 2 more Smart Citations

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Liu

Zhao

et al. 2019

Processes

View full text Add to dashboard Cite

The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin's minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller.Processes 2019, 7, 477 2 of 23 on a single-objective (or multiobjective) optimization problem of driveline matching, component sizing, topology design, or the EMS [8][9][10][11]. For the predefined single-shaft coaxial parallel plug-in hybrid electric bus (PHEB) in our research, component sizing and the energy management control are extremely significant to fuel economy. To guarantee the optimality of dynamic performance and fuel economy, the driveline design and the EMS should be simultaneously considered, as they are strongly coupled [12]. Previous work has disposed of the combined optimization problem in a bilevel manner, where the outer loop is for the former and the inner loop is for the latter [8,13]. Many optimization algorithms have also been extensively applied to solve this problem, such as a genetic algorithm (GA) [4,14], particle swarm optimization (PSO) [11,15], and simulated annealing (SA) [9,16]. Most of them have been utilized to optimally design the component parameters for an outer loop, while a rule-based EMS is nested in an inner loop. However, the optimization results were suboptimal and influenced by the established rules due to the coupling relationship between the component design and EMS [3,7]. To overcome this drawback, another categ...

show abstract

Section: Optimization Results Analysismentioning

confidence: 99%

Section: Boundariesmentioning

confidence: 99%

Section: Optionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Liu

Zhao

et al. 2019

Processes

View full text Add to dashboard Cite

show abstract

“…As an important part of the fluid power system, the hydraulic servo system has attracted a wide spread of attention in many years [12,13]. However, the existing literatures regarding the hydraulic servo system usually concentrated on the improvement of tracking performance due to the difficulties result from the highly nonlinear nature of the plant.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Robust Control for Direct Drive and Energy Recuperation Hydraulic Servo System

Wei-ping

Zhao

2020

Complexity

View full text Add to dashboard Cite

During the vertical cyclic actuation process of heavy materials handling, the gravitational potential energy will be converted to heat in the form of throttle in the traditional hydraulic system. Therefore, large energy dissipation is inevitable is this process. In order to achieve energy recuperation, as well as precise trajectory tracking, a direct drive and energy recuperation system is developed. To be specific, the function of direct drive and energy recovery is realized via the three-chamber actuator and a hydraulic accumulator. Moreover, the optimized parameters are obtained by the simulated annealing algorithm. To further reduce the energy consumption, the variable supply pressure control circuit is introduced into the system. Furthermore, the prescribed tracking performance is guaranteed by the proposed robust controller. To compensate for the uncertainties, both in the variable supply pressure control circuit and the robust controller, the RBF neural network is employed to approximate the unknown function. The presented approach theoretically possesses the ability to minimize the energy consumption while maintaining satisfied tracking accuracy. The results demonstrate that the proposed approach can save nearly 90 percent of the energy, and the maximum tracking error is 2 mm.

show abstract

“…Recently, electric power steering (EPS) has been widely used in passenger cars due to its safety, energy saving, and environmental protection [1][2][3]. However, the traditional power supply in vehicles can only provide limited electric power which is not enough to let the EPS work normally on heavy-duty vehicles such as agricultural transport vehicles, trucks, and medium and large coaches [4,5]. The steering resistance torque is especially large when the vehicle is turning at low speeds, so the corresponding required steering power is far beyond the rated output power of the vehicle power supply (VPS).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Energy Management Strategy for the EPS with Hybrid Power Supply Based on PSO Algorithm

et al. 2020

View full text Add to dashboard Cite

The traditional vehicle power supply is unable to meet the power requirement of electric power steering system (EPS) in heavy-duty vehicles at low speeds. A novel EPS with hybrid power supply (HP-EPS) is constructed in this paper, and a new optimized rule-based energy management strategy of hybrid power supply system is designed. The strategy determines the power distribution of the vehicle power supply (VPS) and super capacitor (SC), as well as the charging or discharging of SC. Furthermore, to minimize the output current fluctuation of the VPS, the optimization model of parameters in the strategy is established and the particle swarm optimization algorithm (PSO) algorithm is applied to optimize the rules in the energy management strategy. The verification for the designed energy management strategy is carried out in MATLAB/Simulink and results show that the output current peak of VPS decreases by 33% and its fluctuation depresses significantly. In addition, the SC is charged timely and fast, which is beneficial to guarantee enough state of charge (SOC) of SC. In conclusion, the optimized rule-based energy management strategy used for the HP-EPS system can meet the current requirement of EPS and effectively reduce the peak and fluctuation of the VPS output current.

show abstract

Parametric optimization of novel electric–hydraulic hybrid steering system based on a shuffled particle swarm optimization algorithm

Cited by 46 publications

References 20 publications

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Energy-Efficient Robust Control for Direct Drive and Energy Recuperation Hydraulic Servo System

Optimization of Energy Management Strategy for the EPS with Hybrid Power Supply Based on PSO Algorithm

Contact Info

Product

Resources

About