Search

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]

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]

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]

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper. • Reduce stress on battery and fuel cell to lift power performances and fuel economy. • Adaptive low-pass filter to allocate peak power and braking energy to supercapacitor. • Minimizing hydrogen consumption while improving the working efficiency of fuel cell. [ABSTRACT FROM AUTHOR]

This work investigates an innovative low-voltage (

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]

• Integrating powertrain optimization and optimal control of a fuel cell mining truck. • Jointly considering energy efficiency and degradation cost of fuel cell and battery. • Multi-level fuel cell powertrain design/control optimization and implementation. • Robust and efficient global optimization method for complex optimization problem. • New methods to apply fuel cell to heavy duty vehicles at reduced lifecycle cost. Proton Exchange Membrane Fuel Cell (PEMFC) system, as a highly efficient, zero pump-to-wheel emission and quick refuelling energy conversion device, is particularly suited for heavy-duty transportation applications. An optimally designed hybrid powertrain with a battery energy storage system and optimal operation control can extend the operating life of the PEMFC system and reduce its lifecycle cost. This work focuses on the minimization of the lifecycle cost of a PEMFC hybrid electric propulsion system through the joint powertrain system design and control optimizations. The approach models and predicts the cost of hydrogen fuel determined by the energy efficiency of the powertrain, as well as the costs associated with the performance degradation and life-shortening of the PEMFC and the battery under a given vehicle operation profile. A nested, tri-level optimization problem is formulated for identifying the optimal powertrain component sizes and energy management parameters, and an advanced surrogate-based global optimization method is used to solve the complex optimization problem. The advantage of the new approach is revealed through the optimal design and control development of a representative mining truck operating under a real-world operation profile, reducing its mining tonnage specific cost by ten percent over its lifecycle. The new integrated design and control optimization method improved energy efficiency, reduced environmental pollutants, and lowered the lifecycle cost of fuel cell mining trucks, contributing to broader applications of the PEMFC technology. [ABSTRACT FROM AUTHOR]