Power control strategy and performance evaluation of a novel electro-hydraulic energy-saving system

Gong, Jinke; Zhang, Daqing; Guo, Yong; Liu, Changsheng; Zhao, Yuming; Hu, Peng; Quan, Weicai

doi:10.1016/j.apenergy.2018.10.066

Cited by 54 publications

(21 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In practice, the EMS for HHVs is usually designed as a rule-based (RB) control strategy because it is simple and has real-time implementable structure [6][7][8][9][10]. However, since its control performance depends heavily on rule-switching thresholds defined by the engineering experience, it actually cannot always provide good results during various driving conditions.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

Zhong

Jiao

2020

Applied Sciences

View full text Add to dashboard Cite

Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power source subsystems in order to achieve target performances in terms of fuel consumption, drivability, component lifetime, and exhaust emissions. For hybrid hydraulic vehicles (HHVs) that are used in construction machinery, the challenge is to design an appropriate control scheme to actually achieve fuel economy improvement taking into consideration the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. To improve fuel economy and adaptability of various driving conditions to online energy management and to enhance the response performance of an engine to a desired torque, a hierarchical model predictive control (MPC) scheme is presented in this paper using the example of a spray-painting construction vehicle. The upper layer is a stochastic MPC (SMPC) based energy management control strategy (EMS) and the lower layer is an MPC-based tracking controller with disturbance estimator of the diesel engine. In the SMPC-EMS of the upper-layer management, a Markov model is built using driving condition data of the actual construction vehicle to predict future torque demands over a finite receding horizon to deal with the randomness of the driving conditions. A multistage stochastic optimization problem is formulated, and a scenario-based enumeration approach is used to solve the stochastic optimization problem for online implementation. In the lower-layer tracking controller, a disturbance estimator is designed to handle the uncertainty of the engine, and the MPC is introduced to ensure the tracking performance of the output torque of the engine for the distributed torque from the upper-layer SMPC-EMS, and therefore really achieve high efficiency of the diesel engine. The proposed strategy is evaluated using both simulation MATLAB/Simulink and the experimental test platform through a comparison with several existing strategies in two real driving conditions. The results demonstrate that the proposed strategy (SMPC+MPC) improves miles per gallon an average by 7.3% and 5.9% as compared with the control strategy (RB+PID) consisting of a rule-based (RB) management strategy and proportional-integral-derivative (PID) controller of the engine in simulation and experiment, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

Zhong

Jiao

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Unlike HEVs, the control strategies of HECMs mainly require the characteristics of operating conditions to be combined. In research on the control strategy of a hybrid excavator, the pressure of the hydraulic pump was often used to identify the working load, and the control rules were made according to the load level [27]. The working process of the excavator and the moving process of each part were analyzed in detail, and the pressure of the operating system was measured in real time to obtain an estimate of the required and recoverable energy [28].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle

et al. 2020

View full text Add to dashboard Cite

The series hybrid electric powertrain is the main architecture of the hybrid electric tracked vehicle. For a series tracked hybrid electric bulldozer (HEB), frequent fluctuations of the engine working points, deviation of the genset working points from the pre-set target trajectory due to an insufficient response, or interference of the hydraulic pump consumed torque, will all result in increased fuel consumption. To solve the three problems of fuel economy, an adaptive smooth power following (ASPF) control strategy based on an optimal efficiency map is proposed. The strategy combines a fuzzy adaptive filter algorithm with a genset’s optimal efficiency, which can adaptively smooth the working points of the genset and search the trajectory for the genset’s best efficiency when the hydraulic pump torque is involved. In this study, the proposed strategy was compared on the established HEB hardware in loop (HIL) platform with two other strategies: a power following strategy in a preliminarily practical application (PF1) and a typical power following strategy based on the engine minimum fuel consumption curve (PF2). The results of the comparison show that (1) the proposed approach can significantly reduce the fluctuation and pre-set trajectory deviation of the engine and generator working points; (2) the ASPF strategy achieves a 7.8% improvement in the equivalent fuel saving ratio (EFSR) over the PF1 strategy, and a 3.4% better ratio than the PF2 strategy; and (3) the ASPF strategy can be implemented online with a practical controller.

show abstract

“…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. Actually, the energy consumption is also a big issue that needs to be dealt with [14][15][16][17]. To achieve the two targets simultaneously, both the control law and the hardware configuration should be taken into account.…”

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

Power control strategy and performance evaluation of a novel electro-hydraulic energy-saving system

Cited by 54 publications

References 33 publications

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle

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

Contact Info

Product

Resources

About