This paper develops a new computational approach for energy management in a hydraulic hybrid vehicle. The developed algorithm, called approximate stochastic differential dynamic programming (ASDDP) is a variant of the classic differential dynamic programming algorithm. The simulation results are discussed for two Environmental Protection Agency drive cycles and one real world cycle based on collected data. Flexibility of the ASDDP algorithm is demonstrated as real-time driver behavior learning, and forecasted road grade information are incorporated into the control setup. Real-time potential of ASDDP is evaluated in a hardware-in-the-loop (HIL) experimental setup.
Approximate Stochastic Differential Dynamic Programming for Hybrid Vehicle Energy Management
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received June 2, 2018; final manuscript received December 11, 2018; published online January 14, 2019. Assoc. Editor: Mahdi Shahbakhti.
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Williams, K., and Ivantysynova, M. (January 14, 2019). "Approximate Stochastic Differential Dynamic Programming for Hybrid Vehicle Energy Management." ASME. J. Dyn. Sys., Meas., Control. May 2019; 141(5): 051003. https://doi.org/10.1115/1.4042253
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