An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information

Abstract

Energy management strategies play an important role in performance optimization of plug-in hybrid electric vehicles (PHEVs), and can be further improved by incorporating external traffic information. Motivated by this, an adaptive equivalent consumption minimization strategy considering traffic information is proposed in this study to facilitate the effective energy management of PHEVs. First, the initial equivalent factors in terms of different initial state of charge (SOC) and driving distance are searched by genetic algorithm. Then, the simplified dynamic programming is leveraged to determine the optimal SOC trajectory according to the traffic information with fast calculation speed. The fuzzy controller is employed to regulate the equivalent factor dynamically, thus enabling effective tracking of the reference SOC trajectory. A hardware-in-the-loop simulation platform based on the virtual scene is developed to validate the performance of controller. Simulation and experimental results highlight that the proposed strategy can lead to less fuel consumption, compared to traditional equivalent consumption minimization strategy, thereby proving its feasibility.

Reinforcement Learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles

“…There have been many studies to estimate the equivalent factor. An optimization-based method using DP can also be used to estimate these equivalent factors [12][13][14][15][16]. In particular, in [13], DP-informed ECMS was studied, where the optimal result from DP was used to predict the equivalent factor at the desired charge sustaining SOC, and the ECMS was further modified using the adaptation based on the battery SOC.…”

“…In particular, in [13], DP-informed ECMS was studied, where the optimal result from DP was used to predict the equivalent factor at the desired charge sustaining SOC, and the ECMS was further modified using the adaptation based on the battery SOC. In [14], traffic information was incorporated with an adaptive ECMS for the energy management of PHEVs. Here, using a genetic algorithm, an initial equivalent factor map was constructed and a fuzzy controller was used to correct the equivalent factor in an adaptive manner, while the reference SOC trajectory was determined using simplified DP.…”

Adaptive Nonlinear Control of Unified Model of Fuel Cell, Battery, Ultracapacitor and Induction Motor Based Hybrid Electric Vehicles

“…where i uc and v uc are the current and voltage of ultracapacitor, u 3 is the duty cycle of the switch S 3 . The equations (7) and (8) show the boost and buck mode of DC-DC converter respectively. Now a variable n is introduced to combine the two different equations for simplicity such that for the boost mode n = 0 and for the buck mode n = 1 for the average control input u 23 as,…”

“…Both PHEVs and BEVs require some external source like charging station or power grid for continuous supply of energy without any interruption. Since FHEVs have no internal combustion engine (ICE), they provide emission free and long driving range as compared to PHEVs and BEVs [6], [7]. An important part of fuel cell based HEVs is hybrid energy VOLUME X, 2021…”

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

“…It provides an important reference for how to use electric energy to complete a green trip. In literature [119], the genetic algorithm is used to search the initial equivalent factors under different initial SOC and driving distance. On this basis, the simplified DP method is used to determine the optimal SOC trajectory by using traffic information.…”