Battery technology for electric and hybrid vehicles: Expert views about prospects for advancement

Baker, Erin; Chon, Haewon; Keisler, Jeffrey M.

doi:10.1016/j.techfore.2010.02.005

Cited by 71 publications

(53 citation statements)

References 12 publications

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“…In this paper, the results obtained from Matlab curvefitting will bechecked with LogLetLab [4] results with 90% confidence level. In Figure 6, the graph shows the same valueof the year as the patents to reach a plateau [18][19][20][21][22][23][24][25].…”

Section: Robustness Of the Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Technology Forecast for Electrical Vehicle Battery Technology and Future Electric Vehicle Market Estimation

Alankus¹

2017

Adv Automob Eng

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Electric Vehicle (EV) battery technologies is a limiting factor for the wide spread diffusion of electric vehicles. EV battery's energy density compared to fossil fuels is still very low, thus EV's have still stringent driving range with voluminous, heavy and high cost batteries. Automotive OEM's are trying to estimate the future of batteries to do their plans related to electric vehicle manufacturing. This article attempts to estimate the future of EV batteries and mainly that of Li_Ion, Li_S and Li_Air Technologies which seem to be the most promising Technologies as of today. The article explains in detail the methodology used, and the results with an estimation of future EV market as a result of the EV battery development time scale.

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Section: Robustness Of the Methodologymentioning

confidence: 99%

“…Logistic Pearl shows that around 2030 patents will reach to a plateau and around 5 years later the technology roll-out could be realized. Therefore, around 2035 technological limit of Li_Ion Technology can be reached Figure 5 [18][19][20][21][22][23][24][25][26][27].…”

Section: Li_ion Battery Technology Forecastingmentioning

confidence: 99%

Technology Forecast for Electrical Vehicle Battery Technology and Future Electric Vehicle Market Estimation

Alankus¹

2017

Adv Automob Eng

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“…Expert elicitation has been recently applied to investigate the uncertain effects of RD&D investments on the prospect of various energy technologies: carbon capture and storage (CCS; Chan et al, 2011;Baker et al, 2009a), hybrid electric vehicles Baker et al, 2010), solar…”

Section: Figure 2: Technology Paths That Have Been Assessed In the Inmentioning

confidence: 99%

The power of biomass: Experts disclose the potential for success of bioenergy technologies

et al. 2014

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This paper focuses on technologies which use thermochemical or biochemical processes to convert biomass into electricity.We present the results from an expert elicitation exercise involving sixteen leading experts coming from different EU Member States. Aim of the elicitation was to assess the potential cost reduction of RD&D (Research, Development and Demonstration) efforts and to identify barriers to the diffusion of these technologies. The research sheds light on the future potential of bioenergy technologies both in OECD (Organisation for Economic Co-operation and Development) and non-OECD countries. The results we present are an important input both for the integrated assessment modeling community and for policy makers who draft public RD&D strategies.

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“…With the development of electric vehicles (EVs), portable devices and even smart grids [1,2], battery technology has attracted more and more attention worldwide. As one of the key parameters of a battery, the state of charge (SOC) is one of the main study topics of battery technology.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Zou

et al. 2014

Energies

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Abstract:Four model-based State of Charge (SOC) estimation methods for lithium-ion (Li-ion) batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS) current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods.

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Battery technology for electric and hybrid vehicles: Expert views about prospects for advancement

Cited by 71 publications

References 12 publications

Technology Forecast for Electrical Vehicle Battery Technology and Future Electric Vehicle Market Estimation

Technology Forecast for Electrical Vehicle Battery Technology and Future Electric Vehicle Market Estimation

The power of biomass: Experts disclose the potential for success of bioenergy technologies

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

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