Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Energy Internet: The business perspective

“…Information and communication technologies (ICTs), especially the emerging information technologies such as cloud computing, Internet of Things, big data analytics and mobile intelligence, are reshaping the landscape of many traditional industries and the daily life style of the general public [6][7][8][9][10]. For the energy sector, emerging ICTs continue to penetrate into the whole process of energy production, transmission, distribution and consumption, thus forming the ICT-enabled intelligent energy system [11][12][13][14][15]. At the same time, the increasingly diverse distributed energy resources (DERs) [16][17][18] and the increasingly abundant energy big data [19][20][21] are combined to promote the formation of more flexible, personalized and efficient energy production and consumption systems.…”

Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions

“…The findings indicate controlled charging using the current grid mix produces overall negative net social benefits due to increased coal generated energy utilized for vehicle charging even though operator costs were reduced by 23%e34%. Khayyam and Bab-Hadishar [14] modeled powertrain, driving factors and environment factors for PHEVs to develop an intelligent energy management system to improve the vehicles overall energy efficiency. Due to growth in EV market penetration, EV charging research remains focused on minimizing the strain on the electrical grid by minimizing costs for operators.…”

Techno-economic design of hybrid electric vehicles using multi objective optimization techniques

“…G c a (14) When replacing (13) and (14) in (12), U b can be expressed as a function of the battery coefficients, the SoC, and the battery power. Similarly to batteries, the state of charge is evaluated from the terminal current I sc and the nominal capacity Q 0 .…”

“…Dynamic mathematical programming is applied to the energy management optimization, including heuristic management strategies. In [12] Khayyam et al propose a soft computing based intelligent management system developed using three fuzzy logic controllers. The fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system.…”