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
The solvable condition of nonlinear H ∞ control problems is given by the Hamilton Jacobi inequality (HJI). The state-dependent Riccati inequality (SDRI) is one of the approaches used to solve the HJI. The SDRI contains the state-dependent coefficient (SDC) form of a nonlinear system. The SDC form is not unique. If a poor SDC form is chosen, then there is no solution for the SDRI. In other words, there exist free parameters of the SDC form that affect the solvability of the SDRI. This study focuses on the free parameters of the SDC form. First, a representation of the free parameters of the SDC form is introduced. The solvability of an SDRI is a sufficient condition for that of the related HJI, and the free parameters affect the conservativeness of the SDRI approach. In addition, a new method for designing the free parameters that reduces the conservativeness of the SDRI approach is introduced. Finally, numerical examples to verify the effect of this method are presented.
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