Energy Management Optimization of Master–Slave Hybrid Electric Vehicle under Rule‐Based Control Strategy

Abstract

In response to the imperfections and issues with conventional fuel vehicles and electric vehicles (EVs), this article proposes a master–slave hybrid electric vehicle (MSHEV) with multiple energy sources. The research team establishes the rule‐based control strategy for MSHEV with the aid of reviewing existing theories. The control strategy enables the MSHEV to transition between several working modes. The simulation consequence verifies that the MSHEV has lower power consumption and energy loss than the EV under actual vehicle driving cycle. One of most critical initial steps in ameliorating a vehicle's performance is parameter optimization. This article selects the battery state of charge of MSHEV as the optimization objective and optimizes applicable parameters. Thereafter, an approximate model is constructed based on the Response Surface Model, and an optimization model is built based on the Multi‐Island Genetic Algorithm. The energy management of the optimized MSHEV is more reasonable and the state of charge is further enhanced. The accomplishment of this article is of considerable significance and reference value in the optimization of energy management of hybrid electric vehicles.

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

“…The main benefits of rule-based control methods for energy management include their simplicity and usability, which make it simple to comprehend and accomplish the primary control goals in actual automobiles [76]. Rule-based control strategies are now commonly used in the production vehicle market because of their low computational effort, adaptability, and good reliability [77]. Despite its widespread use, there are still several major issues that need to be addressed in the rules-based approach to HEV control.…”

Research on Multistate Variable Prediction of a Master‐Slave Electric‐Hydraulic Hybrid Vehicle Based on CNN‐LSTM Model with Attention Mechanism

“…It achieves the mutual conversion of electric energy, hydraulic energy, and mechanical energy, ultimately delivering the power required for vehicle propulsion. [ 29 ] Meanwhile, MSEHV can realize multimode driving. According to the different power demanded by the vehicle, the control strategy selects the working elements in real time to ensure that the vehicle can output the best output torque in real time according to the driver's wishes.…”

Energy Management Strategy Based on Deep Reinforcement Learning and Speed Prediction for Power‐Split Hybrid Electric Vehicle with Multidimensional Continuous Control

“…[6,7] Different optimization algorithms are applied to various energy management problems, [8] such as in renewable microgrids [9] and HEVs. [10] The energy management strategies (EMSs) of hybrid vehicles can be classified into rule-based strategy (RBS), [11] optimization-based strategy (OBS), and learningbased strategy (LBS). [12] RBS-based EMSs include deterministic rule-based control and fuzzy rule-based control.…”