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PLUG-IN HYBRID ELECTRIC VEHICLE POWER MANAGEMENT : OPTIMAL CONTROL AND BATTERY SIZING

This thesis examines plug-in hybrid electric vehicles (PHEVs), i.e. automobiles that can extract power from either chemical fuels or stored electricity, in which the latter is typically regenerated by plugging into the electric grid. This research contains two goals. First, it develops power management algorithms that optimize the way a PHEV splits power demand among its various, and often redundant, actuators. Optimal power management enables PHEVs to attain improved fuel economy and operating cost levels while maintaining battery pack health. Furthermore, the optimal “blending” of fuel and electricity usage in a PHEV provides significant economic benefits to vehicle owners, especially for certain fuel-to-electricity price ratios. Secondly, the thesis evaluates the impact of varying battery size on the performance and efficiency of our previously developed power management algorithms. Specifically, our aim is to determine the extent to which power management algorithm choice can enable the use of smaller batteries, while retaining certain cost and performance characteristics, over distributions of trip duration and drive cycle trajectories. The first part examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). The current state of art optimizes PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The results provided herein focus on a single-mode powersplit PHEV configuration for mid-size sedans, but the approach is general to other configurations and sizes as well. The second part builds on the first by establishing a framework for assessing and quantifying the performance tradeoffs between battery size and control algorithm choice. In particular, it rigorously analyzes operating costs and efficiency using two different control strategies explicitly optimized for certain battery sizes within a finite set. Existing studies examine this effect for power management algorithms derived using either rule-based or deterministic dynamic programming methods. The approach presented here extends research on PHEV battery energy capacity to PHEVs that use power management algorithms identified with stochastic dynamic programming. Futhermore, it treats both PHEV trip duration and PHEV power demand as stochastic functions informed by drive cycle data and travel surveys. The results indicate that a blending strategy enables the use of smaller battery sizes for a broad range of energy price ratios. However, the benefits diminish with increasing battery size and rising fuel-to-electricity price ratios. As a result, our analysis demonstrates how optimal control algorithms can reduce battery cost and potentially catalyze the i market penetration of PHEVs.