Comparing Two Approaches to Precompute Discharge Strategies for Plug-in Hybrid Electric Vehicles

Abstract

This paper investigates two alternative approaches to precompute a discharge strategy for the main commuter route of a plug-in hybrid electric vehicle. The first approach is based on the idea of computing a state of charge reference trajectory by solving a convex program; while the second approach utilizes dynamic programming to determine an optimal cost-to-go function. During real-time operation the torque split is decided by an equivalent consumption minimization strategy where the main difference between the two approaches is how the equivalence factor is determined. With the first approach it is adapted to track the state of charge reference trajectory and in the second approach it is given by the partial derivative of the cost-to-go function with respect to state of charge. To evaluate the two approaches a simulation study is performed in the dynamic vehicle modelling software Autonomie using logged commuter driving data. The simulation results indicate no clear difference between the two approaches in terms of fuel economy and battery usage. Both approaches are, however, significantly better than a charge depleting charge sustaining discharge strategy.

Control of connected and automated vehicles: State of the art and future challenges

“…In [172,173], two approaches to compute the reference state of charge of a plug-in HEV. Both approaches use logged data of velocity and altitude on a given route (that is assumed to be a commuting route).…”

GPS measurement of Swedish car movements for assessment of possible electrification

“…In another study route identification routines are used to find regularities in the car movement patterns to be able to predict where it is heading. By combining route identification with optimizing algorithms for energy management, the fuel use in a PHEV may be considerably reduced [4].…”

“…This is of major importance for optimal battery design, estimation of consumer viability and assessment of the potential for PHEVs to shift energy use in the transport sector from fuel to electricity [1], [2], [3]. Comprehensive data sets are also needed in order to design and optimize the powertrain by vehicle simulations [4]. Publicly available data of high quality on individual car movement patterns is today unfortunately not very common.…”

Least costly energy management for Electric Vehicles with plug-in Range Extenders

“…Well acknowledged techniques tackle the problem in terms of energy consumption, namely the Equivalent Consumption Minimization Strategy [2], [3], [4]. While these approaches are mainly conceived for parallel HEVs, more recent works extend them considering the peculiarities of pHEVs [5], [6], [7]. Further research is devoted to different management goals, such as reducing driving cost [8], [9], pollutant emissions [10], [11], battery aging [12].…”

“…As it happens for pHEVs [6], [13], doing this in an effective way requires at least some basic a priori information from the user on the trip to perform: in the simplest case it can be an indication of the distance and of the availability of the RE, in a more advanced configuration it shall integrate with a navigation system (for accurate road slope profiles and traffic information). Moreover, depending on the business model considered, the RE can be either owned by the user or only rented when needed at some given rental stations.…”