Electric vehicles' battery parameter tolerances analysis by fuzzy logic

Pokorádi, László; Menyhárt, József

doi:10.1109/saci.2016.7507402

Cited by 6 publications

(9 citation statements)

References 2 publications

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“…The measured results show variance in practice [1][2]14]. The use of fuzzy logic enabled the authors to find a precise bounds of the batteries' regimes where they can work safely.…”

Section: Data Evaluation Using Fuzzy Logicmentioning

confidence: 99%

“…The μ values can take any result between 0 and 1 [24]. The general formula of [1,24] determines the Fuzzy membership function of parameter tolerances in the following form:…”

Section: Data Evaluation Using Fuzzy Logicmentioning

confidence: 99%

“…The authors had leaned on measurements executed over an electrical system of the e-vehicle designed at University of Debrecen [1][2]. Based on these studies and experiences, a brand new operational strategy for the batteries and for the electrical machines can be established.…”

Section: Introductionmentioning

confidence: 99%

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Battery Voltage Limit Analysis with Support Vector Machine and Fuzzy Logic

Szabolcsi

Menyhárt

2017

AiMT

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The efficiency and the range of modern electric vehicles are crucial points in their design. Designers and engineers are highly motivated to find solutions to these problems, or, as a rule, to improve existing electrical systems. Considerable number of modern batteries is available for use in electric vehicles and robots. The authors will propose new technology and new methods to use batteries with better efficiency. The authors propose to use the Support Vector Machine and Fuzzy logic in a new approach, which is the battery technical status management. The results show that it is possible to use these two methods simultaneously and they can ensure better results at the operation site.

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“…The measured results show variance in practice [1][2]14]. The use of fuzzy logic enabled the authors to find a precise bounds of the batteries' regimes where they can work safely.…”

Section: Data Evaluation Using Fuzzy Logicmentioning

confidence: 99%

“…The μ values can take any result between 0 and 1 [24]. The general formula of [1,24] determines the Fuzzy membership function of parameter tolerances in the following form:…”

Section: Data Evaluation Using Fuzzy Logicmentioning

confidence: 99%

See 1 more Smart Citation

Battery Voltage Limit Analysis with Support Vector Machine and Fuzzy Logic

Szabolcsi

Menyhárt

2017

AiMT

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“…The article is based on studies and experiences, from previous works from material handling science and MATLAB ® simulations [1][2][3][4]. The motivation of the authors is to fi nd an ideal method for Stewart Platform simulation design which can give a better accuracy for the patient holding system and it can give a better design solution for a high accuracy Stewart Platforms.…”

Section: Introductionmentioning

confidence: 99%

Kinematical simulation methods for Stewart Platform in medical equipments

Hajdú

Bodnár

Menyhárt

et al. 2017

IRASE

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The effi ciency and accuracy of the modern positioning systems are crucial points in their design. The designers and engineers are highly motivated to fi nd new methods and solutions to the accuracy problem with the biggest mobility and safety. Considerable numbers of solutions are on the market but only some of them can be used in medical industry. The authors will propose some kinematical simulation methods using MATLAB ® software. The purpose of these methods is to use Stewart Platform with a better accuracy. The results show that it is possible to simulate Stewart Platform in MATLAB ® and it can be used in medical industry.

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“…These methods share a feature: they take advantage of the inaccuracy and uncertainty associated with the tolerances and the boundaries to make a better representation model of the real world. [1] In a previous work [32] lithium polymer battery parameters were analyzed by Fuzzy logic. The result of this paper presents a brand new operational strategy for the batteries, which are very sensitive to their operational parameters.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine and Fuzzy Logic

2016

APH

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The article gives a short description of the history of the Support Vector Machine (SVM) method and fuzzy logic and their main parameters. It describes how SVM can be used for classification and regression calculation. In addition, it includes the description of the fuzzy logic and the three main defuzzification methods. Examples will be given to show simultaneous use of the two main Support Vector Machine-methods. It ends with the authors' description of the new possibilites for the use of the SVM and fuzzy logic in evaluation of the parameter deviation in the batteries of electric vehicles as well as those of robots.

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Electric vehicles' battery parameter tolerances analysis by fuzzy logic

Cited by 6 publications

References 2 publications

Battery Voltage Limit Analysis with Support Vector Machine and Fuzzy Logic

Battery Voltage Limit Analysis with Support Vector Machine and Fuzzy Logic

Kinematical simulation methods for Stewart Platform in medical equipments

Support Vector Machine and Fuzzy Logic

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