Modeling and thermal management of proton exchange membrane fuel cell for fuel cell/battery hybrid automotive vehicle

Xing, Lu; Xiang, Wentao; Zhu, Ruizhi; Tu, Zhengkai

doi:10.1016/j.ijhydene.2021.10.146

Cited by 65 publications

(9 citation statements)

References 51 publications

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“…The battery is characterized by high energy densities, high cycle lifespans, high discharge currents, high rated voltages, low self-discharge rates, no memory effects, and low pollution. Therefore, fuel cells and batteries are more widely used than other configurations [38].…”

Section: Topologies Of the Fuel Cell Hybrid Power Systemmentioning

confidence: 99%

A review of energy management optimization based on the equivalent consumption minimization strategy for fuel cell hybrid power systems

Zhang

Wang

et al. 2022

Fuel Cells

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Section: Topologies Of the Fuel Cell Hybrid Power Systemmentioning

confidence: 99%

A review of energy management optimization based on the equivalent consumption minimization strategy for fuel cell hybrid power systems

Zhang

Wang

et al. 2022

Fuel Cells

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“…Since liquid-gas intercoolers offer better cooling than gas-gas intercoolers due to the higher heat capacity of the liquid coolant, they are more compact and hence are more suitable for vehicular integration. Some studies do consider the modeling and control of the crucial auxiliary components [7], [10], [11]. PI-based control strategies have been used for the control of the cooling circuits to ensure coolant temperature references are tracked.…”

Section: Introductionmentioning

confidence: 99%

Optimization-Based Thermal Control Strategy for Auxiliary Cooling Circuits in Fuel Cell Vehicles

Eldigair

Kunusch

Ocampo‐Martínez

2023

IEEE Trans. Transp. Electrific.

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In this research work, a thermal management concept for the auxiliary components in fuel cell vehicles is discussed. A control-oriented model of the subsystem under analysis is developed and then used as basis for controller development. Two control strategies are implemented and compared. The first is a PI-controller strategy and it is used as a baseline for performance comparison. The second is an optimizationbased controller (OBC) strategy focused on minimizing the power consumption of the main actuator. The OBC features a novel steady-state observer (SSO) developed for thermal circuits and provides an estimation of the steady-state conditions and conveniently simplifies the optimization problem by disregarding the dynamics of the states so that it can be implemented online with lower computational burden. Comparisons between PIcontrol strategy and OBC demonstrate the OBC's capabilities in overcoming typical problems associated with the PI-control strategy including high power consumption of the actuators and temperature constraint violations under particular operating conditions.

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“…Different control strategies have been employed to ensure an adequate temperature in BESS. The PID controller for regulating the thermal balance of the stack in hybrid vehicles was proposed in [29]. It was shown that the PID controller could stabilize the stack's temperature even with low cooling water flow at the cost of a higher fan and pumping power.…”

Section: Introductionmentioning

confidence: 99%

Modeling Thermal Management of Battery Energy Storage System with Machine Learning

Mosavi¹,

Kristina²

2023

Preprint

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Battery energy storage systems (BESS) are nowadays essential parts of microgrids. A thermal management system (TMS) belongs to substantial control components ensuring optimal operation and long lifespan of batteries. Advanced control strategies implemented in TMS require accurate thermal models to keep battery temperature within predefined bounds while minimizing operating costs. This paper proposes machine learning-based models to predict temperature inside real industrial BESS. Challenges represent partially continuous and partially discrete input signals. Furthermore, inner fans located inside modules affect the temperature in this particular BESS. Unfortunately, the information on fans’ operations is not available. This study also provides an accuracy analysis of bagged classification and regression trees (CART), multi-layer perceptron (MLP), and averaged neural network (avNNet). The results report high prediction accuracy, over 95%, for all models, even the ones with a more straightforward structure.

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Modeling and thermal management of proton exchange membrane fuel cell for fuel cell/battery hybrid automotive vehicle

Cited by 65 publications

References 51 publications

A review of energy management optimization based on the equivalent consumption minimization strategy for fuel cell hybrid power systems

A review of energy management optimization based on the equivalent consumption minimization strategy for fuel cell hybrid power systems

Optimization-Based Thermal Control Strategy for Auxiliary Cooling Circuits in Fuel Cell Vehicles

Modeling Thermal Management of Battery Energy Storage System with Machine Learning

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