Hardware-in-the-loop validation of a power management strategy for hybrid powertrains

Kim, Youngki; Salvi, Ashwin; Siegel, Jason B.; Filipi, Zoran; Stefanopoulou, Anna G.; Ersal, Tulga

doi:10.1016/j.conengprac.2014.04.008

Cited by 29 publications

(15 citation statements)

References 33 publications

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“…In the frequency-based strategy, a low-pass filter or wavelet transform is used to split the traction power demand in components of low and high frequencies [14], [124], [125]. Low-frequency demands will be covered by the FC, while high-frequency components with steep power transients are covered by the battery or the ultracapacitor.…”

Section: A Rule-based Strategiesmentioning

confidence: 99%

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

et al. 2021

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The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavyduty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling

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Section: A Rule-based Strategiesmentioning

confidence: 99%

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

et al. 2021

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“…In the battery modeling and estimation literature, HIL and BIL testing has been limited to the use of battery equivalent circuit models (Song et al, 2013;Kim et al, 2014aKim et al, , 2014bZhang et al, 2018;Tara et al, 2013) and estimation algorithms ranging from unscented Kalman filters (He et al, 2016), adaptive H-infinity filters (Zhang et al, 2016), dual H-infinity filters (Chen et al, 2017) to adaptive extended Kalman filters (Zhang et al, 2017). BIL experiments have been used, wherein a real physical battery system is connected to a controller, to evaluate energy management strategy algorithms for plug-in hybrid and hybrid electric vehicles (Kim et al, 2014a(Kim et al, , 2014bZhang et al, 2018;Tara et al, 2013). Battery equivalent circuit models hosted in an HIL platform are used for testing commercial BMS controllers (Barreras et al, 2016;Dai et al, 2013).…”

Section: Related Literature and Contributionsmentioning

confidence: 99%

Pushing the Envelope in Battery Estimation Algorithms

2020

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Accurate estimation of lithium-ion battery health will (a) improve the performance and lifespan of battery packs in electric vehicles, spurring higher adoption rates, (b) determine the actual extent of battery degradation during usage, enabling a health-conscious control, and (c) assess the available battery life upon retiring of the vehicle to re-purpose the batteries for ''second-use'' applications. In this paper, the real-time validation of an advanced battery health estimation algorithm is demonstrated via electrochemistry, control theory, and batteryin-the-loop (BIL) experiments. The algorithm is an adaptive interconnected sliding mode observer, based on a battery electrochemical model, which simultaneously estimates the critical variables such as the state of charge (SOC) and state of health (SOH). The BIL experimental results demonstrate that the SOC/SOH estimates from the observer converge to an error of 2% with respect to their true values, in the face of incorrect initialization and sensor signal corruption.

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“…For example, in Region-I in Figure 9, classic conditional reasoning can be improved by defining nested case-based modules to apply related optimal control parameters. These cases are correlated to specific load levels, driving patterns, or frequency analysis of power demand [13,15,18]. Moreover, a simple nearest-optimal point selection can be defined within the deterministic rules using look-up tables [19,20].…”

Section: Integration To Power Management Methodsmentioning

confidence: 99%

“…A hardware-in-the-loop validation of this method is conducted using an experimental HEV. The results have been compared to the thermostatic strategy stating fuel economy improvement of 5.9% soot emissions reduction of 62.7%, and a reduction of high current demand to the battery that lowered its average operating temperature by 3 • C. The battery life is then estimated using Ah-processed model revealing a reduction of 23% and consequently extending lifespan [18].…”

Section: Frequency-based Approachmentioning

confidence: 99%

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

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Hardware-in-the-loop validation of a power management strategy for hybrid powertrains

Cited by 29 publications

References 33 publications

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

Pushing the Envelope in Battery Estimation Algorithms

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

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