Comparative Study of Energy Management Strategies for Hydraulic Hybrids

Deppen, Timothy O.; Alleyne, Andrew G.; Meyer, Jonathan J.; Stelson, Kim A.

doi:10.1115/1.4028525

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…Molla (2010) evaluated three hybrid power management strategies: a rule-based strategy, a globally optimal (drive cycle optimal) using DP algorithm and instantaneous optimization (ECMS) strategy based on an overall model of a series HHV independent wheel drive system. Considering the sensitivity of EMSs with respect to variations in drive cycle and system parameters, Deppen et al (2015) presented three strategies of the series HHV's powertrain (rule-based, SDP and MPC) and experimentally validated these using a hardware-in-the-loop system. Hung et al (2016) applied the DP methodology to derive the optimal power-splitting factor for the hybrid system for preselected driving schedules, and the results are used to improve rule-based control strategies, achieving further improvement in fuel economy.…”

Section: Introductionmentioning

confidence: 99%

Control Strategy for a Non-Hybrid Hydrostatic Transmission Construction Vehicle Based on Power Follower

Yuan

Wang

et al. 2022

Transport

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To achieve energy-saving control of non-hybrid Hydrostatic Transmission Construction Vehicles (HST-CVs) with traditional closed-loop Hydrostatic Transmission (HST) for both propulsion and working systems, this paper presents a control strategy for non-hybrid HST-CVs by referring to the power follower method of the hybrid Energy Management Strategy (EMS). Through the implementation of the presented control strategy by coordinated control of the engine speed and hydraulic pump displacement, the engine can be controlled to operate at the pre-set low Brake Specific Fuel Consumption (BSFC) area, similar to that of the hybrid vehicles adopting a power follower control strategy but without the additional installation of accumulators in the hydraulic system. The effect of the control strategy is verified via experimental tests and MATLAB/SIMULINK–AMESIM COLlaborative SIMulation (COSIM). The simulation results show that the proposed control strategy can achieve the expected control target under both highway and off-road conditions.

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Section: Introductionmentioning

confidence: 99%

Control Strategy for a Non-Hybrid Hydrostatic Transmission Construction Vehicle Based on Power Follower

Yuan

Wang

et al. 2022

Transport

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“…For hydraulic hybrid engineering vehicles, most of the present researchers are interested in improving the braking energy recovery rate and increasing its energy utilization. [9][10][11] The logic threshold-based control strategy sets the values of initial parameters by engineering experience and adjusts them by combining the trial and error method. 12,13 Although, with these strategies, the energy efficiency can be significantly improved, it is clear that they do not guarantee an optimal result in all situations.…”

Section: Introductionmentioning

confidence: 99%

Working cycle identification–based braking control strategy and its application for hydraulic hybrid loader

Lin

Zhao

et al. 2018

Advances in Mechanical Engineering

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Due to the introduction of multiple subsystems, it is very difficult for a hydraulic hybrid loader to improve its fuel economy with traditional braking control strategy and classic optimization algorithm. To deal with this problem, a combined braking control strategy based on fuzzy control system was proposed. In this strategy, the current working step and working cycle were first recognized with sensor signals. And the optimal control rules for different working cycles were optimized by genetic algorithm off line. After doing that, the obtained control rules were used to control the braking process and the energy distribution process of loaders. Simulation and experimental results show that the control strategy can make full use of the braking energy and can improve the recovery rate of it under the guarantee of safety and stability.

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“…Similar decreases in performance have also been reported in situations differing from the utilised SDP probabilities. 6,7…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model predictive energy management of hydrostatic drive transmissions

Backas

Ghabcheloo

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, we devise a nonlinear model predictive control framework for the energy management of nonhybrid hydrostatic drive transmissions. The controller determines the optimal control commands of the actuators by minimising a cost function over a receding horizon. With our approach, the velocity-tracking error is minimised while keeping the fuel economy of the system high. The hydrostatic drive transmission system studied in this article is a typical commercial work machine, that is, there is no energy storage or alternative power source in the system (a nonhybrid hydrostatic drive transmission). We evaluate success with a validated simulation model of the hydrostatic drive transmission of a municipal tractor. In our experiments, a detailed system model is used both in the system simulation and in the prediction phase of the nonlinear model predictive control. The use of a detailed model in the nonlinear model predictive control framework places our design as a benchmark for controlling nonhybrid hydrostatic drive transmissions, when compared to solutions using simplified models or computationally less intensive control methods as in earlier work by the authors. Our nonlinear model predictive control approach enables numerically robust optimisation convergence with the utilised complex nonlinear model. Above all, this is accomplished with stabilising terminal constraints and distinctive terminal cost, both based on an optimal steady-state solution. In addition, a simple method to generate initial guesses for optimisation is introduced. When compared with the performance of a controller based on quasi-static models, our results show notable improvement in velocity tracking while maintaining high fuel economy. Furthermore, our experiments demonstrate that framing energy management as a nonlinear model predictive control provides a flexible and rigorous framework for fast velocity tracking and high energy efficiency. We also compare the results with those of an industrial baseline controller.

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Comparative Study of Energy Management Strategies for Hydraulic Hybrids

Cited by 11 publications

References 10 publications

Control Strategy for a Non-Hybrid Hydrostatic Transmission Construction Vehicle Based on Power Follower

Control Strategy for a Non-Hybrid Hydrostatic Transmission Construction Vehicle Based on Power Follower

Working cycle identification–based braking control strategy and its application for hydraulic hybrid loader

Nonlinear model predictive energy management of hydrostatic drive transmissions

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