Predictive real-time energy management strategy for PHEV using lookup-table-based Dynamic Programming

Abstract

This paper proposes a predictive real time energy management strategy for plug-in-hybrid electric vehicles (PHEV) based on an adaptation of Dynamic Programming (DP). The computational load of predictive real time strategies increases with the trip length. Therefore, for online computation by the onboard computer, they strongly depend on an efficient implementation. To reduce computation cost, current approaches for predictive strategies rely on strongly simplified intern vehicle models. The here proposed energy management strategy (EMS) uses a different approach, which is based on the use of precalculated lookup tables for the different operating points of the powertrain. This precalculation make the use of more exact vehicle models possible by using more detailed loss models of the powertrain components. The proposed EMS separates the optimization process, i.e. the calculation of the power distribution to engine and electric motor and gear in two calculation steps. The first step, which is computationally more intensive, has only to be executed once for a certain vehicle configuration. The obtained results are saved in lookup tables to avoid a later recomputation. In the second step, which is done online in the vehicle, a shortest path search algorithm is employed which is based on the predicted vehicle speed and rode slope of the trip. Techniques are integrated which decrease the rounding error caused by the use of lookup tables. The resulting difference of the consumed fuel mass between the lookup table based DP and standard DP is smaller than 0.03% by an approximately 50 times faster calculation. Using the proposed algorithm, even complex intern vehicle models do not affect the online computation cost and can be implemented by real time strategies.

Design of Real-Time Control Based on DP and ECMS for PHEVs

“…In order to reduce the amount of memory use and improve the calculation speed, the offline simulation is used to calculate the fuel cost in series mode and mode selection for a given combination (T w , ω w , SoC) [7,19,25]. Because the efficiency of the battery does not change greatly with the change of SoC in the desired operating region, the SoC is found to have minor effects on the optimal solution, so that effect is ignored.…”

“…In this case, the vehicle speed is regulated to follow a speed cycle using a torque at the wheel controller. Examples of such methods include Pontryagin's minimum principle [1,2], dynamic programming (DP) [3][4][5][6][7], and genetic algorithm [8]. A second class of algorithms is real-time optimal control strategy that can be used to control a vehicle.…”

A Binary Controller to Ensure Engine Peak Efficiency for a Parallel Hybrid Electric Car

“…This causal relationship is captured by high-dimensional look-up tables. These tables can be used for online control [27], [28]. The online control consists of the supervisory and actuator controls.…”

Energy efficient navigation management for hybrid electric vehicles on highways

“…Zhang and Vahidi [25] proposed a dynamic programming (DP)-based technique to find strategies to operate the powertrainbased energy management system (EMS) efficiently with the optimal use of (stored) electric charge and gasoline until the next charging station. Bader et al [26] developed a DP-based mechanism for predictive real-time energy management with the help of precalculated lookup tables for different points of the powertrain. Larsson et al proposed an energy-optimal route selection mechanism based on historical driving data (logged GPS data) about commuter routes and the EMS optimization [27].…”