Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization

Hu, Xiaosong; Zou, Yuan; Yang, Yalian

doi:10.1016/j.energy.2016.06.037

Cited by 148 publications

(56 citation statements)

References 25 publications

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“…2) Health Model: A semi-empirical battery aging model developed in [51] is adopted in this paper to simulate the capacity loss of the battery cell. This model was already widely used for vehicular energy management [52], battery charging control [53], power-source sizing [34], [54], [55], Fig. 6: Battery pack configuration [14].…”

Section: ) Hydrogen Consumption Modelmentioning

confidence: 99%

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

Zou

Tang

et al. 2020

IEEE Trans. Power Electron.

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281

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Energy management is an enabling technology for increasing the economy of fuel cell/battery hybrid electric vehicles. Existing efforts mostly focus on optimization of a certain control objective (e.g., hydrogen consumption), without sufficiently considering the implications for on-board power sources degradation. To address this deficiency, this article proposes a cost-optimal, predictive energy management strategy, with an explicit consciousness of degradation of both fuel cell and battery systems. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, a model predictive control framework, for the first time, is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation. The efficacy of this framework is evaluated, accounting for various sizes of prediction horizon and prediction uncertainties. Second, the effects of driving and pricing scenarios on the optimized vehicular economy are explored.

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Section: ) Hydrogen Consumption Modelmentioning

confidence: 99%

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

Zou

Tang

et al. 2020

IEEE Trans. Power Electron.

Self Cite

281

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“…EVs are regarded as one of the most effective strategies to reduce the oil dependence and gas emission, and to increase the efficiency of energy conversion [3]- [5]. When integrated with the power grid based on charging and/or discharging operations, EVs become energy storage units, and can not only serve as a transportation tool but also act as controllable loads and distributed sources for the power grid [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Flexible Energy Management Protocol for Cooperative EV-to-EV Charging

Zhang

Cheng

Yang

2019

IEEE Trans. Intell. Transport. Syst.

126

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In this paper, we investigate flexible power transfer among electric vehicles (EVs) from a cooperative perspective in an EV system. First, the concept of cooperative EV-to-EV (V2V) charging is introduced, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, a flexible energy management protocol with different V2V matching algorithms is proposed, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, a max-weight V2V matching algorithm is proposed in order to optimize the network social welfare. Moreover, taking individual rationality into consideration, we further introduce the stable matching concepts and propose two stable V2V matching algorithms, which can yield the EV-consumer-optimal and EVprovider-optimal stable V2V matchings, respectively. Simulation results verify the efficiency of our proposed cooperative V2V charging based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs.

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“…LiFePo 4 battery has been widely used in electric vehicles (EVs) due to its high energy, mass ratio, long service life, and good low temperature performance . The precise state of charge (SOC) is crucial to effective and reliable use of the battery.…”

Section: Introductionmentioning

confidence: 99%

A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo₄ battery

Chao

Gong

2017

Int J Energy Res

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Summary LiFePo4 battery is widely used in electric vehicles; however, its flatness and hysteresis of the open‐circuit voltage curve pose a big challenge to precise state of charge (SOC) estimation. The issue is discussed and addressed in this paper. First, a cell model with hysteresis is built to describe real‐time dynamic characteristics of the LiFePo4 battery. Second, the model parameters and SOC are estimated independently to avoid the possibility of cross interference between them. For model identification, an adaptive unscented Kalman filter (AUKF) algorithm is used to identify the cell parameters as they change slowly. While SOC could change rapidly, wavelet transform AUKF algorithm is put forward to estimate SOC. In the novel algorithm, the measurement noise can be estimated and updated online. Finally, the performance of the proposed method is verified under dynamic current condition. The experimental results show that estimated value based on the proposed method is more accurate than unscented Kalman filter‐based method and AUKF‐based algorithm. Meanwhile, the proposed estimator also has the merits of fast convergence and good robustness against the initialization uncertainty.

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Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization

Cited by 148 publications

References 25 publications

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

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

Flexible Energy Management Protocol for Cooperative EV-to-EV Charging

A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo₄ battery

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Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization

Cited by 148 publications

References 25 publications

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

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

Flexible Energy Management Protocol for Cooperative EV-to-EV Charging

A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo4 battery

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A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo₄ battery