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This work proposes a predictive equivalent consumption minimization (P-ECMS) strategy for a power-split hybrid electric vehicle (HEV) using predicted driving cycle speed based on Monte Carlo (MC) algorithm. The proposed P-ECMS fully takes advantage of the predicted speed profiles by the MC algorithm to optimally determine the power split among energy sources. In this study, to validate the workings of the MCbased P-ECMS scheme, a series of simulations under a total of seven replicated driving cycles including New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92), and a combination of all (ALL-CYC) are conducted. The MC-based P-ECMS strategy is compared with a baseline ECMS in terms of fuel-saving. The fuel economy saving up to 6.01% under NEDC, 9.09% under WLTP, 6.33% under UDDS, 5.14% under HWFET, 1.96% under NYCC, 11.47% under LA-92, and 7.92% under ALL-CYC is achieved. The results in this article put forward that the proposed strategy delivers competitive fuel savings compared to the widely used baseline method. [ABSTRACT FROM AUTHOR]

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]