Online energy management of a hybrid fuel cell vehicle considering the performance variation of the power sources

Ghaderi, Razieh; Kandidayeni, Mohsen; Soleymani, Mehdi; Boulon, Loïc; Chaoui, Hicham

doi:10.1049/iet-est.2020.0035

Cited by 6 publications

(5 citation statements)

References 50 publications

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“…Regarding the RL EMS, Ettihir [19] developed an extremum-seeking process to trace the maximum power and maximum efficiency of FC considering the FC aging. Sharing a similar idea, Ghaderi1 et al [20] proposed an online parameters identification model to tackle the EMS uncertainties owing to the performance drifts of the power sources. Davis [21] proposed a rule-based two-mode EMS for low-power and high-power events according to the total cost of ownership, which includes fuel consumption, FC, and battery lifetime degradation.…”

Section: Review Of Ems Development For Fc Evmentioning

confidence: 99%

The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle

Zhou

Feng

et al. 2022

Sustainability

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Considering the limited driving range and inconvenient energy replenishment way of battery electric vehicle, fuel cell electric vehicles (FC EVs) are taken as a promising way to meet the requirements for long-distance low-carbon driving. However, due to the limitation of FC power ability, a battery is usually adopted as the supplement power source to fill the gap between the requirement of driving and the serviceability of FC. In consequence, energy management is essential and crucial to an efficient power flow to the wheel. In this paper, a self-optimizing power matching strategy is proposed, considering the energy efficiency and battery degradation, via implementing a deep deterministic policy gradient. Based on the proposed strategy, less energy consumption and longer FC and battery life can be expected in FC EV powertrain with optimal hybridization degree.

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Section: Review Of Ems Development For Fc Evmentioning

confidence: 99%

The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle

Zhou

Feng

et al. 2022

Sustainability

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“…In order to estimate the SOC, coulomb counting formula is used. To see the impact of battery degradation on the performance of the multi-stack FC-HEV, the common battery ageing criteria are utilized in which the internal resistance is doubled, or the capacity is faded by 20 percent [19]. As a result, the SOC calculations are updated according to the state of the health of the battery.…”

Section: B Battery Modelmentioning

confidence: 99%

“…To the best of the authors' knowledge, this is one of the first attempts, if any, to distribute the power/energy flow in a multi-stack FC-HEV using GT. According to the literature, one of the main causes of mismanagement in any EMS is the performance drifts of the power sources owing to degradation and operating conditions variations [19]. To deal with this issue, each power source in this work is combined with an online parameter estimation tool using recursive least square (RLS) to track the health state of the components and extract the updated energetic characteristics.…”

Section: Introductionmentioning

confidence: 99%

Online Health-Conscious Energy Management Strategy for a Hybrid Multi-Stack Fuel Cell Vehicle Based on Game Theory

Ghaderi¹,

Kandidayeni²,

Soleymani³

et al. 2022

IEEE Trans. Veh. Technol.

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The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FC-HEV with three FCs and a battery pack. GT is a wellapproved method for characterizing the interactions in multiagent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rulebased strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rulebased EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system.

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“…where L(k) is the instantaneous cost. In fact, the optimal fuel consumption of the whole drive cycle can be determined by calculating the optimal fuel consumption of each state utilising (15). Accordingly, the objective function can be simplified to a single state variable SOC and a single control variable P Bio-Gen .…”

Section: Dynamic Programing Emsmentioning

confidence: 99%

“…The EMSs of the HEVs are broadly classified into rule-based and optimisation-based methods [13,14]. Rule-based methods, such as thermostatic control strategy (TCS) and fuzzy logic control (FLC), are easier to implement in online applications; however, they are less capable of finding optimal power management solutions [15]. Optimisation-based methods provide near-optimal solutions and can be combined with rule-based methods to revise the set of rules and inferential knowledge.…”

Section: Introductionmentioning

confidence: 99%

An intelligent energy management strategy for an off‐road plug‐in hybrid electric tractor based on farm operation recognition

Ghobadpour

Mousazadeh

Kélouwani

et al. 2021

IET Electrical Syst in Trans

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Due to the growing emergence of vehicle electrification, agricultural tractor developers are launching hybrid powertrains in which energy management strategy (EMS) assumes a prominent role. This work mainly aims at developing an EMS for a plug-in hybrid electric tractor (PHET) to minimise fuel consumption and increase the operating range. The developed off-road PHET power sources are composed of a biogas-fuelled Internal Combustion Engine Generator (Bio-Gen), a photovoltaic system, and a battery pack. To control the power flow among different sources, a two-layer EMS is formulated. In this regard, initially, the farm operating mode is recognised by means of classification of a working cycle's features. Then, a control strategy based on a multi-mode fuzzy logic controller (MFLC) is employed to manage the power flow. At each sequence, the classifier identifies the farm operation condition and accordingly activates the relative mode of the MFLC to meet the requested power from the Bio-Gen. The performance of the proposed EMS has been evaluated based on three real-world typical agricultural working cycles. The results demonstrate the successful performance of the proposed intelligent EMS under farm conditions by maintaining the energy sources' operation in a highefficiency zone which can lead to the extension of the working range and decrease fuel consumption.

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Online energy management of a hybrid fuel cell vehicle considering the performance variation of the power sources

Cited by 6 publications

References 50 publications

The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle

The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle

Online Health-Conscious Energy Management Strategy for a Hybrid Multi-Stack Fuel Cell Vehicle Based on Game Theory

An intelligent energy management strategy for an off‐road plug‐in hybrid electric tractor based on farm operation recognition

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