Control Optimization of a Charge Sustaining Hybrid Powertrain for Motorsports Clemson University

Format:

Conference/Event

Author/Creator:

Zhu, Zhu, author.

Contributor:

Prucka, Robert

Song, Chuanxue

Song, Shixin

Tan, Xiaoping

Conference Name:

WCX World Congress Experience (2018-04-10 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2018

Summary:

AbstractThe automotive industry is aggressively pursuing fuel efficiency improvements through hybridization of production vehicles, and there are an increasing number of racing series adopting similar architectures to maintain relevance with current passenger car trends. Hybrid powertrains offer both performance and fuel economy benefits in a motorsport setting, but they greatly increase control complexity and add additional degrees of freedom to the design optimization process. The increased complexity creates opportunity for performance gains, but simulation based tools are necessary since hybrid powertrain design and control strategies are closely coupled and their optimal interactions are not straightforward to predict. One optimization-related advantage that motorsports applications have over production vehicles is that the power demand of circuit racing has strong repeatability due to the nature of the track and the professional skill-level of the driver. The repeatable behavior from lap to lap allows for the efficient utilization of dynamic programming (DP) techniques to optimize vehicle speed and power management for a given race track, which is the focus of this research. The DP strategy is derived and described in detail using a hybrid rallycross vehicle as an example. The DP strategy minimizes lap time while sustaining battery charge at the end of each lap. Constraints on engine torque, electric motor power, battery capacity and tire friction are incorporated into the proposed strategy. The DP also generates an execution map that can be used for real-time on-vehicle implementation. This map includes optimal vehicle speed and power management strategies for all possible situations that the vehicle can experience during the real racing event

Notes:

Vendor supplied data

Publisher Number:

2018-01-0416

Access Restriction:

Restricted for use by site license