Research Papers: Design Automation

EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players

[+] Author and Article Information
Yi Ren

Mechanical Engineering,
Arizona State University,
Tempe, AZ 85287
e-mail: yiren@asu.edu

Alparslan Emrah Bayrak

Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: bayrak@umich.edu

Panos Y. Papalambros

Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: pyp@umich.ed

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 24, 2015; final manuscript received April 11, 2016; published online May 6, 2016. Assoc. Editor: Gary Wang.

J. Mech. Des 138(6), 061407 (May 06, 2016) (10 pages) Paper No: MD-15-1665; doi: 10.1115/1.4033426 History: Received September 24, 2015; Revised April 11, 2016

We compare the performance of human players against that of the efficient global optimization (EGO) algorithm for an NP-complete powertrain design and control problem. Specifically, we cast this optimization problem as an online competition and received 2391 game plays by 124 anonymous players during the first month from launch. We found that while only a small portion of human players can outperform the algorithm in the long term, players tend to formulate good heuristics early on that can be used to constrain the solution space. Such constraining of the search enhances algorithm efficiency, even for different game settings. These findings indicate that human-assisted computational searches are promising in solving comprehensible yet computationally hard optimal design and control problems, when human players can outperform the algorithm in a short term.

Copyright © 2016 by ASME
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Fig. 1

The ecoRacer game interface (a)–(d) and settings (e). The game interface (a) is designed to work for computer and mobile screens. Touching the gear icon to the right enters the design screen (b), where the final drive ratio can be tuned. After each play, the score board (c) is shown, and the player can review speed and motor efficiency profiles of the current play in (d). Vehicle specifications, motor efficiency map, and maximum torque curves are shown in (e).

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Fig. 2

DP results for the optimal final drive ratio. Top to bottom: Road profile, optimal speed profile corresponding to ρ*=18, and optimal control decisions in terms of braking and acceleration.

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Fig. 3

A one-dimensional example of EGO search (a)–(d). At each iteration, the algorithm updates the kriging model (in solid line) and chooses the next sample (triangle) according to the maximum expected improvement (merit) function.

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Fig. 4

The “learned” subspace of solutions provides more rational control strategies. (a) Percentage of test points that follow rules (A) and (B) for a variety of states. The percentage is calculated for all uniformly sampled solutions in S, and for those that satisfy ϕ>0. (b) A summary of tested states and percentages averaged across all state values.

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Fig. 5

Statistics of the ecoRacer game. (a) Distribution of players by the number of games they played, and distribution of plays by their score. (b) Performance comparison of human player and the EGO algorithm for the first 200 plays.

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Fig. 6

Comparison between EGO with and without human knowledge, using the inverse, hill, zigzag and long tracks. The long track is five times as long as the others.

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Fig. 7

EGO performance on the long track with constraints from (1) all human plays (2) all EGO plays, (3) the first 500 human plays, and (4) the first 500 EGO plays




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