Co-optimization of speed trajectory and power management for a fuel-cell/battery electric vehicle. (15th February 2020)

• Record Type: Journal Article
• Authors: Kim, Youngki; Figueroa-Santos, Miriam; Prakash, Niket; Baek, Stanley; Siegel, Jason B.; Rizzo, Denise M.
• Is Part Of: Applied energy. Volume 260(2020)
• Publication Date: 2020-02-15
• Subjects: Eco-driving -- Co-optimization -- Speed profile optimization -- Power management -- Electric vehicles -- Optimal control; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.114254
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Co-optimization of speed trajectory and power management is investigated. Pontryagin's Minimum Principle is used to reduce the search space for control. The performance of co- and sequential optimization is comprehensively analyzed. Abstract: With the advent of vehicle connectivity via intelligent transportation systems, the simultaneous optimization of powertrain operation and vehicle dynamics has become an important research problem to improve energy efficiency. However, this problem is still very challenging due to computational loads in terms of time and memory usage, and hence very little effort has been spent to reveal the full potential of co-optimization. Studies in the literature on eco-driving or energy-efficient operation typically preferred using a sequential optimization or eliminating some constraints for fast computation. This paper extends the authors' previous work of determining energy-efficient speed profiles focused on vehicle dynamics by including additional state and control variables due to vehicle hybridization in application to a fuel-cell/battery electric vehicle. The results from Pontriagyn's Minimum Principle analysis reveal that the co-optimization can be formulated with one discrete variable describing vehicle operation and another continuous variable for power distribution to reduce computation in implementing Dynamic Programming. Then, the performance of co-optimization and sequential optimization for energy-efficient driving is comprehensively analyzed and compared in terms of energy consumption by each powertrain component, operating modes, and computational cost. The total energy savings by co-optimization range from 5.3% to 24.2% for aggressive driving on a hilly terrain. Meanwhile, on a relatively flat road, the benefit of co-optimization is less significant. Nonetheless, co-optimization achieved 0.5%–5.3% reduction in total energy consumption as compared to sequential optimization.