Charging, power management, and battery degradation mitigation in plug-in hybrid electric vehicles: A unified cost-optimal approach

Hu, Xiaosong; Martínez, Clara Marina; Yang, Yalian

doi:10.1016/j.ymssp.2016.03.004

Cited by 169 publications

(72 citation statements)

References 31 publications

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“…Additionally, it should be noted that the nominal cycle in this paper is regarded as the C-rate being 1 (1/h), temperature being 25 ( • C) and SOC being 0.5. According to (16), the severity factors of different SOC and temperatures are shown in Figure 5a.…”

Section: Battery Modelmentioning

confidence: 99%

“…An optimization-oriented model integrating vehicle simulation, battery degradation dates and driving cycle data is developed by Shiau et al [15] for minimizing battery cycle life cost, fuel consumption and exhaust emissions. Additionally, in order to reduce the total daily consumption of PHEVs, a new convex programming is proposed by Hu et al [16] to optimize charging, on-road power management and battery degradation mitigation. In [17] the state of battery health (SOH) is also taken as a state variable in the control problem, and two separate controllers, SOC controller and SOH controller, are designed to maintain SOC and SOH in the desired states.…”

Section: Introductionmentioning

confidence: 99%

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Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

et al. 2017

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This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.Keywords: plug-in hybrid electric vehicle (PHEV); battery health; energy management strategy; stochastic dynamic programming (SDP); particle swarm optimization (PSO)

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Section: Battery Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

et al. 2017

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“…The result will be greater division in the types of vehicles bought in other countries without minimizing emissions or consumption of gasoline. Proper management can ensure loss minimization of the grid and enhance the transmission and generation of electric power supply [7]. The control of PHEVs charging depending on the locations can be classified into two groups; household charging and public charging.…”

Section: Policymentioning

confidence: 99%

Adoption of Plug-in Hybrid Electric Vehicle among Malaysian Consumers

Adnan¹,

Vasant²

2016

Ind Eng Manage

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“…The EV's battery pack will need to be replaced more frequently with V2G operation [18]. The battery aging cost induced by V2G may exceed the benefit brought from V2G, and substantial subsidies are required to trigger V2G service [19]. It is important for residential home EV users to consider whether V2G is economically beneficial in reducing their overall energy costs.…”

Section: Introductionmentioning

confidence: 99%

Minimization of Residential Energy Cost Considering Energy Storage System and EV With Driving Usage Probabilities

Sun

Yue

Zhang

et al. 2019

IEEE Trans. Sustain. Energy

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This version is available at https://strathprints.strath.ac.uk/65407/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Abstract-Electric vehicle (EV) can be applied to discharge power back to the grid, which is called vehicle-to-grid (V2G) technology. There is a constant debate on whether V2G is an economically viable option due to the high battery degradation cost. In this work, the cost benefit of EV customers participating in V2G has been studied using different feed-in tariffs (FITs). A model is developed for minimisation of energy cost for residential users, which includes an EV, a separate energy storage system (ESS) and renewable energy supply. Key factors such as the EV driving usage, the degradation cost of EV and ESS batteries are considered. The EV driving usage is established through a designed survey, from which the probability of vehicle parking and plug in at home, the probabilities of EV under driving and parking outside can be calculated. Comprehensive case studies have been undertaken to investigate the optimisation strategies under various scenarios. Two types of electricity tariffs, time-ofuse (TOU) and fixed tariffs, are considered. It is revealed that certain threshold levels of FITs are expected to allow users benefit from V2G technology. Compared with non-optimised operation, the cost saving with the optimised strategy is evident in the case studies. Index Terms-Electric vehicle (EV), vehicle to grid (V2G), energy storage system (ESS), battery degradation, driving usage, photovoltaic (PV), energy cost optimisation I. INTRODUCTIONA. Background lectric vehicles (EVs) have become popular in the past decade, one benefit of which is to reduce greenhouse gas emission in transportation systems. The charging load of EV supplied from renewable energy resources can further reduce transportation emission [1]. A reduction in gree...

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Charging, power management, and battery degradation mitigation in plug-in hybrid electric vehicles: A unified cost-optimal approach

Cited by 169 publications

References 31 publications

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

Adoption of Plug-in Hybrid Electric Vehicle among Malaysian Consumers

Minimization of Residential Energy Cost Considering Energy Storage System and EV With Driving Usage Probabilities

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