An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle

Designing a Sustainable University Recharge Area for Electric Vehicles: Technical and Economic Analysis

“…In [35] the authors state that the LCOE is equal to 338 €/MWh for fuel cells, 1670 €/MWh for lithium ion and 3072 €/MWh for lead acid. Furthermore in [52] it is suggested a LCOE for percentage of energy of the combined PV and storage system equal to 255 €/MWh, which is closer to the one stated in [57] (about 290 €/MWh for solar PV and battery systems in Germany to 2020). The LCOE conversion from the German case to the Italian one is not immediate: although the regions produce a greater amount of energy, the storage system is affected by harsh weather conditions, due to the high temperatures.…”

“…The battery has to provide energy for the traction, counteracting resistive forces. For the sake of simplicity, we suppose that the route is inside a city, without major changes in elevation and the velocity of the EV can be described by the Simplified Federal Urban Driving Schedule cycle (SFUDS, Figure 8), which are a series of tests defined by the US Environmental Protection Agency (EPA) to measure tailpipe emissions and fuel economy of cars [51], but other interesting approach can be found in [52][53][54], in which the driving cycles are based on future driving information with a stochastic simplicity, we suppose that the route is inside a city, without major changes in elevation and the velocity of the EV can be described by the Simplified Federal Urban Driving Schedule cycle (SFUDS, Figure 8), which are a series of tests defined by the US Environmental Protection Agency (EPA) to measure tailpipe emissions and fuel economy of cars [51], but other interesting approach can be found in [52][53][54], in which the driving cycles are based on future driving information with a stochastic prediction method based on the Markov approach or on a telematics technology-based approach, requiring global positioning system (GPS) and intelligent transportation system (ITS) information . During the route, there are moments with high load and moments with almost zero load.…”

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

“…where T mot is the torque of the motor; T ICE is the torque of the engine; n ICE is the rotation speed of the engine; P bra is the braking torque; n d is the demand speed. To ensure the BP is performing in proper condition and protect the BP from over discharge, the battery's state of charge should obey [32]:…”

Clean, efficient and affordable energy for a sustainable future

“…An integrated power management was proposed by Zhang et al [80] with multiple energy sources, including a semi-active hybrid energy storage system and an assistance power unit, which can promote fuel economy especially in the charge sustain mode under the MANHATTAN driving cycle. Chen et al [81] also proposed a novel predictive energy management approach based on dynamic-neighborhood particle swarm optimization algorithm for plug-in hybrid electric vehicles, which could reduce the energy consumption by up to 9.70% compared with the charge-depleting and charge-sustaining energy management strategy.…”