Methods from optimal control theory have been used since the past decade to design model-based energy management strategies for hybrid electric vehicles (HEVs). These strategies are usually designed as solutions to a finite-time horizon, constrained optimal control problem that guarantees optimality upon perfect knowledge of the driving cycle. Properly adapted these strategies can be used for real-time implementation (without knowledge of the future driving mission) at the cost of either high (sometime prohibitive) computational burden or high memory requirement to store high-dimensional off-line generated look-up tables. These issues have motivated the research reported in this chapter. We propose to address the optimal energy management problem over an infinite time horizon by formulating the problem as a nonlinear, nonquadratic optimization problem. An analytical supervisory controller is designed that ensures stability, optimality with respect to fuel consumption, ease of implementation in real-time application, fast execution and low control parameter sensitivity. The approach generates a drive cycle independent control law without requiring discounted cost or shortest path stochastic dynamic programming introduced in the prior literature.
- Optimal Control Problem
- Energy Management
- Driving Cycle
- Drive Cycle
- Battery Energy
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The author would like to deeply thank Roberto Mura for taking this research a step forward, Lorenzo Serrao for the enjoyable and productive discussions, Yann Guezennec, Giorgio Rizzoni, and Stephen Yuorkovich for the productive iterations.
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Onori, S. (2014). Model-Based Optimal Energy Management Strategies for Hybrid Electric Vehicles. In: Waschl, H., Kolmanovsky, I., Steinbuch, M., del Re, L. (eds) Optimization and Optimal Control in Automotive Systems. Lecture Notes in Control and Information Sciences, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-319-05371-4_12
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Print ISBN: 978-3-319-05370-7
Online ISBN: 978-3-319-05371-4