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Proton exchange membrane technology is proven for stationary application but one of crucial aspect of this technology is its durability which is subjected to the operating conditions i.e. load transients, switch ON/OFF events and idling conditions. Such operating conditions may lead to premature failure of electrolyzer (ELY) and fuel cell (FC). However, transient load variation and rapid switch ON/OFF are unavoidable conditions in microgrid due to erratic variation in the photovoltaic source power during cloudy condition and load transients. Therefore, it is challenging to develop a control (energy management) strategy which can ensure the smooth operation of FC and ELY and achieve the others multi-objectives such as balance in demand and supply, higher energy efficiency, reliability and resiliency. The smooth operations of FC and ELY can be achieved by controlling the rate of change of power during transient operating conditions. Present study is dedicated to energy management system (EMS) of hydrogen storage based microgrid. In present study, photovoltaic (PV) generator is used as primary generator while battery and hydrogen (metal hydride) units are used as energy storage mediums. Different control strategies i.e. model predictive control (MPC), hysteresis band control strategy (HBCS), equivalent consumption and minimization strategy (ECMS) and state machine (SM) are used in proposed EMS. The operating cost of system is found to be 8.79, 8.88, 29.54 and 14.73 USD while efficiency is obtained 79.0%, 80.5%,77.4% and 80.0% in HBCS, MPC, ECMS and SM modes, respectively. • Energy management system (EMS) for integration of hydrogen technologies. • Model predictive, hysteresis band, ECMS and state machine are used in EMS. • Effect of different control strategies in EMS on microgrid performance. • Smooth operation of Electrolyzer and Fuel cell in microgrid. • Effect of the EMS on the utilization factor of system components. [ABSTRACT FROM AUTHOR]

As a result of global actions for decarbonization, the rapid development of electrified vehicles (EV), including plug-in hybrid vehicles (PHEV) leads to increasing demands for maximizing battery useful life since recycling EV batteries would bring new environmental issues. PHEVs can have a higher fuel economy while maintaining the battery state-of-charge (SoC) through the equivalent consumption minimization strategy (ECMS); however, it is still a great challenge to maximize battery life through PHEV control since it is hard to balance diverse time scale control objectives (fuel economy, SoC control and the battery life). To this end, this paper proposes a Cuboid Equivalent Consumption Minimization Strategy (C-ECMS) for multimode PHEVs. A new concept of the "Hamiltonian matrix" is introduced by adding an additional control degree-of-freedom to the conventional Hamiltonian vector to enable optimal dual motor control in the multi-mode PHEVs. Then, an aging factor (AF) is introduced in associated with the equivalent factor (EF) to generate three Hamiltonian matrices that establish a cuboid knowledge base for the optimal control considering diverse time scale objectives. Experiments under five different driving cycles are conducted to study 1) the impact of Hamiltonian matrix dimensions on SoC control accuracy and 2) the impact of EF and AF settings on the Pareto Frontier considering fuel economy, SoC accuracy, and battery aging. The unified setting for C-ECMS is obtained based on the experimental study, and the result is demonstrated by a comparison study with the rule-based strategy and the standard ECMS implemented in the same PHEV. The results show that the proposed C-ECMS outperforms the standard ECMS control and achieves more accurate SoC sustaining (0.4% of SoC error), less fuel consumption (8.4% improvement), and less battery capacity loss (10.4% improvement). • A cuboid equivalent consumption minimization strategy is proposed for PHEV • Diverse time scale control objectives are incorporated into a new Hamilton matrix • Pareto analysis is conducted to extract the optimal control settings • 8.4% fuel and 10.4% battery capacity loss can be saved by the proposed method [ABSTRACT FROM AUTHOR]

The fuel economy and battery charge sustaining capability are two key criteria for the energy management of a full-power fuel cell hybrid vehicle equipped with small-capacity battery pack. In order to achieve stable battery charge sustenance and near-optimal fuel consumption, this study proposes an optimization-oriented adaptive equivalent consumption minimization strategy (A-ECMS) based on demand power prediction achieved via an iterative predictor. The proposed strategy updates the optimal equivalent factor periodically via local optimization process according to the predicted power to converge the state of charge (SOC) and guarantee fuel economy. The simulation results show that the iterative predictor has considerable accuracy, and the correlation between the predicted data and the real data reaches up to 0.987. The proposed strategy can quickly recover the battery SOC within 40 s in a 500-s driving cycle, which is shorter than existing feedback-oriented A-ECMS. At charge sustaining stage, the proposed strategy maintains the battery SOC around the reference value with an extremely low fluctuation degree of 0.36. The equivalent fossil-fuel consumption of the proposed strategy is 8.003 L/100 km, which is lower than that of existing A-ECMS. Besides, further investigation reveals that the proposed strategy has robust performance against the disturbance of power prediction errors. [Display omitted] • An optimization-oriented predictive adaptive equivalent consumption minimization strategy is developed. • A short-term demand power predictor is designed based on iterative learning framework. • A local optimization method is proposed to update near-future optimal equivalent factor periodically. • Better battery charge sustaining capability is achieved while guaranteeing fuel economy. [ABSTRACT FROM AUTHOR]

This work investigates an innovative low-voltage (<60 V) hybrid device that enables engine boosting and downsizing in addition to mild hybrid functionalities such as regenerative braking, start-stop, and torque assist. A planetary gear set and a brake permit the power split supercharger (PSS) to share a 9 kW motor between supercharging the engine and direct torque supply to the crankshaft. In contrast, most e-boosting schemes use two separate motors for these two functionalities. This single motor structure restricts the PSS operation to only one of the supercharging or parallel hybrid modes; therefore, an optimized decision making strategy is necessary to select both the device mode and its power split ratio. An adaptive equivalent consumption minimization strategy (A-ECMS), which uses the battery state of charge (SoC) history to adjust the equivalence factor, is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces vehicle fuel consumption by 18.4% over standard FTP75 cycle, compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to the baseline. [ABSTRACT FROM AUTHOR]