Online Optimal Mode Control for Plug-in Hybrid Vehicles Based on Driving Routes

Watanabe, Ryunosuke; Yoshioka, Hayato; Ibuki, Tatsuya; Sakayanagi, Yoshihiro; Sampei, Mitsuji

doi:10.1016/j.ifacol.2020.12.1018

Cited by 1 publication

(3 citation statements)

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“…[17], [21] used machine learning techniques to avoid complex numerical optimization. Our previous studies [22], [23] presented a method for utilizing historical driving data and verified its effectiveness through experiments. The experiments showed that the proposed approach achieves a 15.9% improvement on average compared with the CDCS method.…”

Section: Introductionmentioning

confidence: 87%

“…This paper explores the risk measure characteristics of statistical data and proposes risk-aware energy management for drive mode switching in PHEVs. The proposed method evaluates the risk of energy loss using CVaR cost and EVaR-bound constraints that were not considered in our previous method [22], [23]. The CVaR quantifies heavy fuel consumption events and can prevent the spread of the probability distribution in the positive direction.…”

Section: Introductionmentioning

confidence: 99%

“…The remainder of this paper is organized as follows: Section II explains some preliminaries to provide the main result for the proposed method. Section III provides drivingroute and vehicle modeling with updates from our previous paper [22], [23]. Section IV defines the cost function and the stochastic constraints and then proposes risk-aware energy management for the drive mode switching.…”

Section: Introductionmentioning

confidence: 99%

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Risk-Aware Energy Management for Drive Mode Control in Plug-in Hybrid Electric Vehicles

et al. 2022

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This paper proposes risk-aware energy management for drive mode control in plug-in hybrid electric vehicles (PHEVs) to reduce fuel consumption. This paper focuses on reducing fuel usage in scenarios in which energy demands along a planned route are stochastically estimated using historical driving data. In this scenario, the proposed method evaluates the risk of high energy consumption based on the conditional value at risk (CVaR) and entropic value at risk (EVaR) derived from Chernoff's inequality. The CVaR quantifies the high fuel consumption expected in the tail of the probability distributions as a cost function. In contrast, the EVaR bound constraint provides stochastic constraints of electricity capacity based on the property of the cumulant-generating function. Each risk evaluation is formulated as a mixed-integer exponential cone programming problem by expressing the drive modes of a PHEV as binary variables. The proposed method was demonstrated using a detailed vehicle simulator with real-world driving cycles. The designed controller achieved, on two selected routes, 8.60% and 16.09% improvements on average, and 10.75% and 12.85% reductions at the 75th percentile compared to a commercial method. The simulations indicated that we can design the controller characteristics by adjusting the risk-awareness of energy loss. The conservativeness of the risk evaluation is also discussed based on the simulation results.INDEX TERMS Energy management, Optimization-based control, Plug-in hybrid electric vehicles, Risk measurement

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