Energy management strategy for FCEV considering degradation of fuel cell

Yan, Sun; Xia, Changgao; Yin, Bifeng; Gao, Haiyu; Jian, Han; Liu, Jing

doi:10.1080/15435075.2021.2023546

Cited by 17 publications

(3 citation statements)

References 33 publications

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“…The rate of change of the setpoint is limited in (21) to reduce the fuel cell transient operation. The EMS parameters r 1 , r 2 , and r 3 are defined in (22) as function of the battery and ambient temperatures, making the strategy adaptive. On the other hand, the battery setpoint P bat,set is calculated as: P bat,set = P el,des − P fcs + P fcs,cool + P bat,cool , (23) compensating for the remaining part of the desired load and the electric losses of the cooling systems.…”

Section: On-board Adaptive Energy Management Strategymentioning

confidence: 99%

Optimal Calibration of an Adaptive and Predictive Energy Management Strategy for Fuel Cell Electric Trucks

et al. 2022

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Energy management strategies have a significant impact on the hydrogen economy of fuel cell trucks and the lifetime of battery and fuel cell systems. This contribution presents the design and optimal calibration of an energy management strategy that is adaptive to the battery and ambient temperatures. Indeed, fuel cell trucks face critical operating conditions due to high ambient temperatures or high loads on long uphill roads. However, the presented adaptive energy management strategy shifts the electric loads to the fuel cell system to limit the battery usage, avoiding accelerated degradation due to battery temperature peaks without hindering the hydrogen economy. The strategy design and calibration involves a multi-objective optimization of performance indicators related to hydrogen consumption, fuel cell degradation, battery thermal state, equivalent charge/discharge cycles, and charge control. This work uses AVL CAMEO to systematically vary the adaptive curve parameters to explore the trade-off between the key performance indicators. The calibration considers real-world driving cycles of road freight vehicles, including measured speed, road elevation, and variable vehicle mass. Moreover, the energy management design is robust because the performance indicators are evaluated over 8935 km, covering an extensive range of real-world driving scenarios. Eventually, the adaptive and predictive energy management strategy proposed in this work can meet all the performance targets thanks to the optimal calibration, and it is particularly effective in avoiding battery temperature peaks.

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Section: On-board Adaptive Energy Management Strategymentioning

confidence: 99%

Optimal Calibration of an Adaptive and Predictive Energy Management Strategy for Fuel Cell Electric Trucks

et al. 2022

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“…Previous research seldom integrates all four factors, often only analyzing two or three. [24] To improve and optimize the existing EMS, this article proposes an online lifecycle operating costs minimization strategy (OCMS) for FCBs with the following main contributions. Figure 1 depicts the structure and main elements of the OCMS: 1) The online OCMS incorporates hydrogen consumption, battery energy consumption, and power source degradation into the objective function, comprehensively considering vehicle operating costs from a full lifecycle perspective; 2) The cycle life models estimate the state of health (SOH) of the fuel cell and battery in real-time, updating the objective function accordingly to ensure the adaptability of the EMS throughout the full lifecycle; 3) The proposed OCMS is compared with two benchmark strategies under different SOH of power sources to demonstrate its advantages in reducing hydrogen consumption, extending power sources life, and reducing vehicle operating costs.…”

Section: Introductionmentioning

confidence: 99%

Online Lifecycle Operating Costs Minimization Strategy for Fuel Cell Buses Considering Power Sources Degradation

Qin,

He,

Liu

et al. 2024

Energy Tech

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The high operating costs limit the popularity and development of fuel cell buses (FCBs). To enhance the lifecycle operating economy of FCBs, this article analyzes the impact of power sources degradation on operating costs and proposes an online operating costs minimization strategy, which integrates online state of health (SOH) estimation, online multiobjective optimization, and online adaptive adjustment into the control strategy framework. Specifically, the SOH of the power sources is estimated by cycle life models in real time. The hydrogen consumption, battery energy consumption, and power sources degradation are incorporated into the objective function and adjusted in real time according to the SOH. Under different SOH, the simulation results indicate that compared with the equivalent consumption minimization strategy, the proposed strategy can reduce fuel cell and battery degradation by 13.8% and 5%, and decrease operating costs by 5.3%. Moreover, the adaptability of the control strategy is enhanced throughout the full lifecycle.

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“…materials, compositions, structure, and dimensions) [4][5][6][7][8][9][10][11], and system design (e.g. reactant supply, water and heat management, and electric load) [12][13][14][15][16]. Therefore, the fuel cell operation is complicated with many degrees of freedom, and the reliable and stable operation of fuel cells requires a proper control strategy for industrial applications.…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient and high-fidelity 1D steady-state performance model for PEM fuel cells

Zhao

Li²,

Shum³

et al. 2023

J. Phys. Energy

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The performance of a proton exchange membrane (PEM) fuel cell is determined by many factors, including operating conditions, component specifications, and system design, making it challenging to predict its performance over a wide range of operating conditions. Existing fuel cell models can be complex and computationally demanding or may be over-simplified by neglecting many transport phenomena. Therefore, a high-fidelity and computationally efficient model is urgently needed for the model-based control of fuel cells. In this study, semi-implicit multi-physics numerical models have been established, taking the mass, momentum, reactants, liquid water, membrane water, electrons, ions, and energy in all fuel cell components into account. The developed 1D model is of high fidelity by incorporating the two-phase flow, non-isothermal effect, and convection, and is still computationally efficient. These models are validated against data from an auto manufacturer with good agreements, and the computing efficiency is evaluated on a modest laptop computer. The modeling results suggest that the two-phase flow model exhibits better prediction accuracy than the single-phase flow model when reactants are fully humidified, while under low humidity conditions, the two models present equivalent performance as liquid water does not exist in the fuel cell components. The results also suggest that the maximum convective/diffusive ratio of H2, O2, and vapor mass fluxes can be 12%, 5.3%, and 35%, respectively, which are ignored in most diffusion-dominant models. The developed models are computationally efficient, requiring only 0.56 s and 0.26 s to simulate a steady-state operation of fuel cells for the two- and single-phase flow models, respectively. This implies that the developed models are suitable for the control of PEM fuel cells.

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Energy management strategy for FCEV considering degradation of fuel cell

Cited by 17 publications

References 33 publications

Optimal Calibration of an Adaptive and Predictive Energy Management Strategy for Fuel Cell Electric Trucks

Optimal Calibration of an Adaptive and Predictive Energy Management Strategy for Fuel Cell Electric Trucks

Online Lifecycle Operating Costs Minimization Strategy for Fuel Cell Buses Considering Power Sources Degradation

A computationally efficient and high-fidelity 1D steady-state performance model for PEM fuel cells

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