Research on Energy Reclaiming Active Suspension Control Strategy Based on Linear Motor and Hydraulic Hybrid System

Deng, Zongquan; Shi, Bo; Wei, Dongping; Wang, Sheng-Chang

doi:10.1088/1742-6596/1910/1/012041

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…The intelligent suspension system described in this paper can provide four working modes for drivers and passengers [23][24][25], as shown in Figure 8 below. The active control mode can improve the driving safety, driving smoothness, and handling stability of the vehicle.…”

Section: The Working Mode and Workflow Of The Suspension Systemmentioning

confidence: 99%

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Study on Multi-Mode Switching Control of Intelligent Suspension under Full Road Section

Liu

et al. 2023

Processes

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The safety, comfort, and energy feedback of active suspension in a single control mode is mutually restricted. To meet the needs of drivers and passengers for vehicle driving performance under different road conditions, this paper proposes a multi-mode switching control strategy of an intelligent suspension system, aiming at improving the stability and comfort of vehicles under different road conditions. In this paper, the adaptive Kalman filter algorithm with a forgetting factor is used to estimate the road input. The accuracy of the algorithm in estimating the road input is verified by simulation and experiment. The single-double threshold logic judgment method is used to formulate the switching rules between each working mode. In the controller, the PID control of the BP neural network and the LQR control optimized by GA are used to optimize and adjust the vehicle driving performance indexes in different modes, which effectively solves the problem of limited adaptability of suspension control optimization objectives under different road driving conditions. Based on the data from the vehicle acceleration sensor and road condition sensor, the modeling and simulation of the switching control system are carried out. The simulation results show that the designed control system can effectively improve the comprehensive performance of the vehicle under different road driving conditions. Compared with the traditional active suspension, the body acceleration is increased by 26% on the B-grade road surface, which effectively improves the user’s ride comfort. Compared with the traditional active suspension, the tire dynamic displacement is increased by 24% on the D-grade road, which can significantly improve the overall performance of the vehicle and meet the design requirements of the system.

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Section: The Working Mode and Workflow Of The Suspension Systemmentioning

confidence: 99%

“…The intelligent suspension system described in this paper can provide four working modes for drivers and passengers [23][24][25], as shown in Figure 8 The mode-switching control strategies for different roads are briefly introduced a follows:…”

Section: The Working Mode and Workflow Of The Suspension Systemmentioning

confidence: 99%

Study on Multi-Mode Switching Control of Intelligent Suspension under Full Road Section

Liu

et al. 2023

Processes

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“…In recent years, the linear quadratic regulator (LQR) control strategy received increasing attention, as it can easily realize the closed-loop control [18][19][20][21][22]. Compared with other control methods, LQR has a simple mathematical treatment and easily constructs objective function.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension

Zhao

2023

Applied Sciences

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In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.

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Layout analysis of compressed air and hydraulic energy storage systems for vehicles

Jin

Hong

et al. 2022

Advances in Mechanical Engineering

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The compressed air energy storage system has a better energy density, while the widely used hydraulic one is superior in power performance. Therefore, they are suitable for different hybrid vehicles, which require a comparative study on the performances and vehicle applicability of the broad pressure energy storage system layouts. In this paper, an integrated mathematical model of four basic pressure layouts is presented for characteristic analysis and applicability discussion. Results show that the open volume layout achieves the best power performance with the flow specific power of 13.92 MJ/m3, thus it is suitable for heavy hybrid trucks and mobile machinery. The open mass layout achieves the best energy performance with the energy density of 124.35 MJ/m3, which can be used in light new energy passenger vehicles. And the performance of the closed volume layout is close to the open volume layout with the flow specific power of 9.78 MJ/m3, so it could be applied to middle and light hybrid trucks. This research provides a basis for the hybrid method of pressure energy storage system layouts for vehicles, and could be applied in the design and research of non-electric hybrid vehicles in the near future.

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Research on Energy Reclaiming Active Suspension Control Strategy Based on Linear Motor and Hydraulic Hybrid System

Cited by 3 publications

References 2 publications

Study on Multi-Mode Switching Control of Intelligent Suspension under Full Road Section

Study on Multi-Mode Switching Control of Intelligent Suspension under Full Road Section

Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension

Layout analysis of compressed air and hydraulic energy storage systems for vehicles

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