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Research Papers

Optimal Power Management of Hydraulic Hybrid Mobile Machines—Part II: Machine Implementation and Measurements

[+] Author and Article Information
Rohit Hippalgaonkar

Ford Research and Advanced Engineering,
2101 Village Drive,
Dearborn, MI 48121;
School of Mechanical Engineering,
Purdue University,
585 Purdue Mall,
West Lafayette, IN 47907
e-mail: rhippalg@ford.com

Monika Ivantysynova

Department of Agricultural and
Biological Engineering,
Purdue University,
225 South University Street,
West Lafayette, IN 47907;
School of Mechanical Engineering,
Purdue University,
585 Purdue Mall,
West Lafayette, IN 47907

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 9, 2014; final manuscript received February 9, 2016; published online March 9, 2016. Assoc. Editor: Yang Shi.

J. Dyn. Sys., Meas., Control 138(5), 051003 (Mar 09, 2016) (12 pages) Paper No: DS-14-1466; doi: 10.1115/1.4032743 History: Received November 09, 2014; Revised February 09, 2016

The problem of achieving maximum system efficiency through near-optimal supervisory control (or system power management) in mobile off-highway machines is a theoretically challenging problem. It has been tackled for the first time in this work for displacement-controlled (DC) hydraulic hybrid multi-actuator machines such as excavators, through a two-part publication. In Part I, the theoretical aspects of this problem were outlined, supported by simulations of the theoretically optimal supervisory control (relying on dynamic programming) as well as a novel, implementable rule-based supervisory control strategy (designed to replicate theoretically optimal results). In Part II of the publication, the world's first prototype hydraulic hybrid excavator using throttle-less DC actuation is described, together with machine implementation of the novel supervisory control strategy proposed in Part I. The design choice, or set of component sizes implemented on the prototype, was driven by an optimal sizing study that was previously done. Measurement results from implementation of two different supervisory control strategies are also presented and discussed—the first, a conservative, suboptimal strategy that commanded a constant engine speed and proved that drastic engine downsizing can be performed in excavator and similar applications. The second strategy implemented was the novel, near-optimal rule-based strategy (or the “minimum-speed” strategy) proposed in Part I that exploited all available system degrees-of-freedom, by commanding the minimum-required engine speeds (to meet DC actuator flow requirements) at every instant in time. While the actual engine was not downsized on the prototype excavator, both the single-point and minimum-speed strategies showed that for the aggressive, digging cycles that such machines are typically used for, the DC hydraulic hybrid architecture enables engine operation at or near 50% of maximum engine power without loss of productivity. As described in Part I, actually downsizing the engine by 50% with use of the near-optimal, minimum-speed strategy will enable significant gains in efficiency (in terms of grams of fuel consumed) over standard valve-controlled architectures (55%) as well as DC nonhybrid architectures (25%) in cyclical operation.

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Figures

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Fig. 1

Working hydraulic schematic of prototype DC S-P hybrid excavator—with most critical sensors

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Fig. 2

Controller structure on prototype hybrid excavator

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Fig. 3

Actuator control for unit 2

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Fig. 4

Actuator control for DC units (supplying boom, stick, and bucket)

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Fig. 5

Single-point strategy—actuator positions, engine speed, and accumulator pressure

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Fig. 6

Single-point strategy implementation (measurements on prototype DC hybrid excavator)—torque balance, unit 1 displacement, and accumulator pressures during (aggressive) student digging cycle

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Fig. 7

Single-point strategy implementation (measurements on prototype DC hybrid excavator)—engine operation in (aggressive) student digging cycle

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Fig. 8

Single-point strategy simulation: engine operation map in DC S-P hybrid excavator with 50% downsized engine in expert truck-loading cycle

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Fig. 13

Minimum-speed strategy implementation (measurements on prototype DC hybrid excavator)—engine operation in (aggressive) student digging cycle

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Fig. 12

Minimum-speed strategy implementation (measurements on prototype DC hybrid excavator)—accumulator pressures (top) and storage unit displacements (commanded and measured—bottom) in (aggressive) student digging cycle

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Fig. 11

Minimum-speed strategy implementation (measurements on prototype DC hybrid excavator)—system power balance and unit 1 displacement command-tracking during (aggressive) student digging cycle

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Fig. 10

Minimum-speed strategy implementation (measurements on prototype DC hybrid excavator)—commanded and measured engine speeds (top) with operator joystick commands (bottom) during (aggressive) student digging cycle

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Fig. 9

Minimum-speed strategy implementation (measurements on prototype DC hybrid excavator)—actuator positions during (aggressive) student digging cycle

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