Optimized power management based on adaptive-PMP algorithm for a stationary PEM fuel cell/battery hybrid system

Ou, Kai; Yuan, Weiwei; Choi, Mihwa; Yang, Seugran; Jung, Seunghun; Kim, Young‐Bae

doi:10.1016/j.ijhydene.2018.06.072

Cited by 81 publications

(27 citation statements)

References 28 publications

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“…1) The energy efficiency characteristic related to the load current (Iload) is dependent on the control mode used, the switching frequency, and coil inductance [42]; 2) For low [38] and high [39] power applications using the control mode based on the pulse width modulation technique and optimal control, respectively, the energy efficiency can be approximated by (16): The effect of a variable energy efficiency of the boost converter on the results obtained at constant energy efficiency will be analyzed and discussed in the next section.…”

Section: Discussionmentioning

confidence: 99%

Better Fuel Economy by Optimizing Airflow of the Fuel Cell Hybrid Power Systems Using Fuel Flow-Based Load-Following Control

et al. 2019

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In this paper, the results of the sensitivity analysis applied to a fuel cell hybrid power system using a fuel economy strategy is analyzed in order to select the best values of the parameters involved in fuel consumption optimization. The fuel economy strategy uses the fuel and air flow rates to efficiently operate the proton-exchange membrane (PEM) fuel cell (FC) system based on the load-following control and the global extremum seeking (GES) algorithm. The load-following control will ensure the charge-sustained mode for the batteries’ stack, improving its lifetime. The optimization function’s optimum, which is defined to improve the fuel economy, will be tracked in real-time by two GES algorithms that will generate the references for the controller of the boost DC-DC converter and air regulator. The optimization function and performance indicators (such as FC net power, FC electrical efficiency, fuel efficiency, and fuel economy) have a multimodal behavior in dithers’ frequency. Furthermore, the optimum in the considered range of frequencies depends on the load level. So, the best value could be selected as the frequency where the optimum is obtained for the most load levels. Considering a dither frequency of 100 Hz selected as the best value, the sensitivity analysis of the fuel economy is further analyzed for different values of the weighting parameter keff, highlighting the multimodal feature in the parameters for the optimization function and fuel economy as well. A keff value around of 20 lpm/W seems to give the best fuel economy in the full range of load.

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Section: Discussionmentioning

confidence: 99%

Better Fuel Economy by Optimizing Airflow of the Fuel Cell Hybrid Power Systems Using Fuel Flow-Based Load-Following Control

et al. 2019

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“…A representative strategy of the former is fuzzy logic-based strategy [18]. The latter are based on optimization theories, such as dynamic programming (DP) [19], Pontryagin's minimum principle [20], equivalent consumption minimization strategy (ECMS) [21], and model predictive control (MPC). They are crucial in resolving the complex energy management problems of FCVs.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimal Predictive Energy Management for Fuel Cell/Battery Hybrid Construction Vehicles

Liu

Hui

et al. 2020

IEEE Access

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Fuel cell/battery hybrid construction vehicles (FCHCVs) have shown great promise; however, the complex working conditions of construction vehicles pose considerable challenges to the performance and energy management of a fuel cell/battery hybrid system. In this paper, multiobjective optimal model predictive control (MOMPC)-based energy management for FCHCVs is explored. A system model is established that includes an economic model and a lifetime model. In the MOMPC framework, multiobjective optimization is conducted to enhance fuel cell durability and battery lifetime while minimizing costs. Since the energy management problem is a nonlinear problem with hard state constraints, it can be difficult to resolve online. The multiobjective approach employs an adaptive weight-adjustment method based on a fuzzy logic algorithm. An economic evaluation of the FCHCV is conducted over its life cycle with respect to the power source size. Simulation results indicate economic savings and prolonged battery lifetime with the MOMPC-based strategy, compared with conventional benchmarks. INDEX TERMS Model predictive control, multiobjective optimization, energy management, fuel cell, construction vehicle.

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“…Some studies have been conducted based on optimization by mathematical models [3]- [5]. Caliano et al [3] proposed the economic optimization framework of a micro-combined heat and power (CHP) system producing electricity and heat during the cold season, for housing complexes in Italy.…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic programming was another popular scheme used to optimize the operation schedule of a CHP unit with a storage tank that supplies backup power to a Smart Grid [4]. Ou et al developed real-time EMS for a polymer electrolyte membrane (PEM) fuel cell/battery hybrid operating system [5].…”

Section: Introductionmentioning

confidence: 99%

Operational Planning of a Residential Fuel Cell System for Minimizing Expected Operational Costs Based on a Surrogate Model

et al. 2020

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This study proposes a novel operational planning method for polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process, and hot water is stored in an attached tank. Generating and storing hot water based on an optimal operational plan according to household demand leads to further energy saving; therefore, operational planning methods based on household demand prediction have received significant attention. However, the improvement in the demand prediction accuracy does not necessarily lead to efficient PEFC-CGS operation in terms of operational costs; in other words, the accuracy in the demand prediction does not directly indicate the resulting operational efficiency. In this study, the authors propose a novel approach based on a surrogate model for deriving an appropriate plan that minimizes the expected operational costs among the operational plan candidates. In the proposed scheme, the error between expected and actual operational costs explicitly represents the relevance of the operational plan, so that the optimal operational plan can be selected directly from the perspective of the resulting operational efficiency. The practicality of the proposed approach is evaluated with the existing demand prediction-based approach via numerical simulations using real-world measurements of multiple customers in Japan. The proposed method reveals 30% reduction of the excessive operational costs by avoiding the inefficient operation of the auxiliary gas-heater in the experiments and will further enhance the value of introducing highly efficient residential fuel cell system that contributes to a low-carbon society. INDEX TERMS Cost minimization, machine learning, operational planning, polymer electrolyte fuel cell cogeneration systems, surrogate model.

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Optimized power management based on adaptive-PMP algorithm for a stationary PEM fuel cell/battery hybrid system

Cited by 81 publications

References 28 publications

Better Fuel Economy by Optimizing Airflow of the Fuel Cell Hybrid Power Systems Using Fuel Flow-Based Load-Following Control

Better Fuel Economy by Optimizing Airflow of the Fuel Cell Hybrid Power Systems Using Fuel Flow-Based Load-Following Control

Multiobjective Optimal Predictive Energy Management for Fuel Cell/Battery Hybrid Construction Vehicles

Operational Planning of a Residential Fuel Cell System for Minimizing Expected Operational Costs Based on a Surrogate Model

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