An efficient method for energy management optimization control: Minimizing fuel consumption for hybrid vehicle applications

Abstract

One of the main research areas of the automotive sector is energy consumption and the reduction of pollutant emissions. Therefore, the hybrid electrical vehicle Vehicle electrical hybrid (VEH) is a possible approach to improve the vehicle components or its architecture to maximize system efficiency. This vehicle benefits from levels of consumption and emission of pollutants lower than the thermal vehicle. The problem is then to find the right split power between the two hybrid sources, the internal combustion engine (ICE) and the electrical machines (EM) to minimize fuel consumption. These energy management strategies are the algorithms that achieve instant split power between the two sources in the hybrid electric vehicle (HEV) while controlling the battery state of charge (SOC), in order to improve the fuel economy and optimize the performance HEV. In this research, a parametric study on the impact of optimization of the energy management strategy on the optimal operation of the vehicle is presented. A hybrid vehicle model is described and rules-based energy management strategy is presented. Later these rules are optimized with an optimization algorithm based on Grey Wolf optimizer (GWO). It is a new meta-heuristic based on the population. The results of the simulation make it possible to deduce the impact of this optimal strategy of energy management on fuel consumption and CO2 emissions. The obtained results show that the optimal operation of the vehicle varies according to the best energy management strategies.

Energy Management System Design for Good Delivery Electric Trike Equipped with Different Powertrain Configurations

“…Both equations above are used for current that flows out of the battery (discharging). For current, which flows into the battery (charging), Equations (17) and (18) are used:…”

“…To obtain the best component configuration of electric vehicles, modeling of the energy management system is required. For example, Hmidi et al were modeling the system and implementing the New European Driving Cycle (NEDC), which aims to minimize fuel consumption in hybrid vehicles [18]. Another study was performed by Islameka et al in which they compare the full mechanical, serial, and parallel braking strategies simulated on the WLTC Class 2 driving cycle [19].…”

MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

“…Generally, the state of charge (SOC) of the power battery, required torque, and the speed of the motor and engine are selected as the threshold values. [5][6][7][8] In this case, a series of optimization curves for engine and motor operation are usually developed in advance according to the MAP diagram, or a series of control rules are formulated by using fuzzy logic to ensure that the hybrid electric vehicle runs in the high-efficiency range. [9][10][11] The rule-based strategy cannot deal with multi-objective optimization problems effectively, and the formulation of rules often depends on the designers' engineering experience, so the improvement of fuel economy based on this strategy is not obvious.…”

Real-Time Energy Management Strategy of Multi-Wheel Electric Drive Vehicles With Load Power Prediction Function

“…Sabri et al [4] proposed a fuzzy logicbased energy management strategy for selecting the appropriate power distribution to reduce the fuel consumption of an on-road hybrid electric vehicle (HEV). Hmidi et al [5] used the Grey Wolf algorithm to achieve optimal power distribution between the two power sources in an HEV, making it possible to minimize fuel consumption and improve performance. Feng and Dong [6] developed a real-time intelligent control algorithm to manage the energy output of a hybrid electric mining truck, achieving a 21% reduction in total operational cost.…”