Cost-Optimal Energy Management of Hybrid Electric Vehicles Using Fuel Cell/Battery Health-Aware Predictive Control

Hu, Xiaosong; Zou, Changfu; Tang, Xiaolin; Li, Teng; Hu, Luojia

doi:10.1109/tpel.2019.2915675

Cited by 280 publications

(72 citation statements)

References 61 publications

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“…Fuzzy logic control optimized by GA in [14], MPC based sequential quadratic programming in [15] and DP in [16,17] consider all hydrogen cost and degradation costs of fuel cell and battery. But empirical degradation models of fuel cell and battery used to calculate their degradation costs are not precise, which cannot describe the dynamical degradation rates of power sources under the dynamical conditions of vehicles like the changeable external environment condition, temperature, and operating conditions.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Zhou

Gualous

et al. 2021

IEEE Trans. Ind. Inf.

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The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. A road, health, and price-conscious optimal cost minimization strategy based on decision making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of vehicles including the hydrogen cost and degradation costs of fuel cell and battery. Second, a decision making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate health states of power sources and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach optimal results on the overall cost and charge sustaining of battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed.

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Section: B Literature Reviewmentioning

confidence: 99%

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Zhou

Gualous

et al. 2021

IEEE Trans. Ind. Inf.

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“…This model is based on wavelet analysis, extreme learning machine, and genetic algorithm and considers the influence of PEMFC load current, relative humidity, temperature, and hydrogen pressure. In [29], an EMS is formulated based on model predictive control and a cost function is proposed inclusive of hydrogen, PEMFC degradation, and battery degradation costs. In [30,31], two degradation models are proposed for PEMFC and battery and incorporated in the sizing problem of a FCHEV.…”

Section: Literature Surveymentioning

confidence: 99%

“…In the light of the discussed papers, it is clear that the vast majority of the existing studies do not take the degradation of the power sources into account while designing an EMS for a FCHEV. However, recently, some studies have attempted to take this point into account [29][30][31][32]. Moreover, the developed EMSs are mainly based on one control variable as they just determine the reference current from the PEMFC stack.…”

Section: Contributionmentioning

confidence: 99%

Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies

et al. 2020

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This paper studies the impact of two significant aspects, namely fuel cell (FC) degradation and thermal management, over the performance of an optimal and a rule-based energy management strategy (EMS) in a fuel cell hybrid electric vehicle (FCHEV). To do so, firstly, a vehicle model is developed in simulation environment for a low-speed FCHEV composed of a FC stack and a battery pack. Subsequently, deterministic dynamic programming (DP), as an optimal strategy, and bounded load following strategy (BLFS), as a common rule-based strategy, are utilized to minimize the hydrogen consumption while respecting the operating constraints of the power sources. The performance of the EMSs is assessed in different scenarios. The first objective is to clarify the effect of FC stack degradation on the performance of the vehicle. In this regard, each EMS determines the required current from the FC stack for two FCs with different levels of degradation. The second objective is to evaluate the thermal management contribution in improving the performance for the new FC compared to the considered cases in scenario one. In this respect, each strategy deals with determining two control variables (FC current and cooling fan duty cycle). The results of this study indicate that negligence of adapting to the PEMFC health state, as the PEMFC gets aged, can increase the hydrogen consumption up to 24.8% in DP and 12.1% in BLFS. Moreover, the integration of temperature dimension into the EMS can diminish the hydrogen consumption by 4.1% and 5.3% in DP and BLFS respectively.

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“…3 Electric vehicles (EVs) are regarded as a potential solution to environmental pollution, energy source shortages and global climate issues, highly concerned worldwide caused by transportation with fast development of automobile industry and increasing car ownership. 4,5 Electric vehicles with internal combustion engine (ICE) are called as hybrid electric vehicle (HEV) [6][7][8] and result low-emission 9,10 compared to conventional ones. However, the goal is to have zero-emission vehicles (ZEVs) 11 which has no ICE in its powertrain.…”

Section: Introductionmentioning

confidence: 99%

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

2020

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Due to increased environmental pollution and global warming concerns, the use of energy storage units that can be supported by renewable energy resources in transportation becomes more of an issue and plays a vital role in terms of clean energy solutions. However, utilization of multiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure. Hybrid energy storage system (HESS) consists of battery, ultra-capacitor (UC), fuel cell (FC) and the vehicle is propelled with two complementary propulsion machines. To increase powertrain efficiency, traction power is simultaneously shared at different rates by propulsion machines. Propulsion powers are shared by HESS units according to following objectives: extending battery lifetime, utilizing UC and FC effectively. Primarily, to optimize the power split in HESS, a convex optimization problem is formulated to meet given objectives that results 5 years prolonged battery lifetime. However, convex optimization of complex systems can be arduous due to the excessive number of parameters that has to be taken into consideration and not all systems are suitable for linearization. Therefore, a neural network (NN)-based machine learning (ML) algorithm is proposed to solve multi-objective energy management problem. Proposed NN model is trained with convex optimization outputs and according to the simulation results the trained NN model solves the optimization problem within 92.5% of the convex optimization one.

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Cost-Optimal Energy Management of Hybrid Electric Vehicles Using Fuel Cell/Battery Health-Aware Predictive Control

Cited by 280 publications

References 61 publications

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

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