A novel optimal power management strategy for plug-in hybrid electric vehicle with improved adaptability to traffic conditions

Abstract

Adaptability to various driving conditions (TCs) is one of the essential indicators to assess the optimality of power management strategies (PMSs) of plug-in hybrid electric vehicles (PHEVs). In this study, a novel optimal PMS with the improved adaptability to TCs is proposed for PHEVs to achieve the energy-efficient control in momentary scenarios by virtue of advanced internet of vehicles (IoVs), thus contributing to remarkable promotion in fuel economy of PHEV. Firstly, the optimal control rules in the novel PMS, corresponding to diverse driving conditions, are optimized offline by the chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP), which can effectively endow the global optimization knowledge into the rule inspired method. Then, an online TC identification (TCI) method is designed by cooperatively exploiting multi-dimensional Gaussian distribution (MGD) and random forest (RF), where the MGD based analysis on the macrocosmic state of traffic contributes to valuable inputs for the RF based TC classification, and additionally the super regression ability of RF further improves the identification accuracy. Finally, the numerical simulation validations showcase that the novel optimal PMS can reasonably and instantly manage the power flow within power sources of PHEV under different TCs, manifesting its anticipated preferable controlling performance.

A Novel Learning-Based Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

“…In the comparison study, the selected serial PHEV, parallel PHEV, and 4WD PHEV are simulated and controlled by the designed algorithm. The detailed description on the configurations and component parameters can be found in our previous research [44][45][46]. In Table VIII, average EFC denotes the average equivalent fuel consumption of simulation on NEDC, UDDS, US06, HWFER, and JC 08 driving cycles.…”

A review on hybrid source energy management strategies for electric vehicle

“…It is an effective EMS and shows its scope with online data-based approaches such as artificial intelligence and machine learning techniques. 63 The combination of multiple EMS can improve the fuel economy and energy efficiency. Thus, this review paper also discusses integrated EMS and recommends a novel methodology implemented as EMS to tackle EV challenges.…”

“…17 Multiple EMS are combined to develop an integrated EMS control, which is receiving significant attention. 63 HESS-based energy distribution can be effectively optimized using DP, and a rule-based approach is developed based on the previous one. Song et al 49 proposed the two-control algorithm which performs the EMS of EV and the advantages of both approaches brings the system to achieve optimal energy management.…”

“…These aspects should be improved without compromising battery life and capacity 25 which got highly affected due to varying vehicle, driver, and environmental related factors. 63,64 The review paper highlights the importance of an EMS that utilizes hybrid sources with a scheduled power allocation to improve the performance of vehicles without affecting the battery life cycle for any type of users with different driver behaviors, driving profiles, road terrain, load, and environmental conditions. The paper's reminder is organized as: Section 2 investigates various topologies utilized for energy management of battery and SC hybrid systems.…”

Hierarchical energy-saving optimization control of networked hybrid electric vehicles under urban Conditions

“…In recent years, the use of vehicle networking technology to solve the energy-saving optimization control problem of hybrid vehicles has attracted the attention of scholars [5][6][7] . Some scholars proposed a hierarchical controller based on V2X information.…”