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PhD in Prognostics and Condition Monitoring of Lithium Ion Battery System

克兰菲尔德大学

Cranfield University

  • 学历文凭

    学历文凭

    Ph.D.

  • 专业院系

    专业院系

    Manufacturing

  • 开学时间

    开学时间

  • 课程时长

    课程时长

  • 课程学费

    课程学费

    汇率提示

国际学生入学条件

A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in the Project element or equivalent with a minimum 60% overall module average.
the potential to engage in innovative research and to complete the PhD within a three-year period of study.
a minimum of English language proficiency (IELTS overall minimum score of 6.5).
Also, the candidate is expected to:

Have excellent analytical, reporting and communication skills
Be self-motivated, independent and team player
Be genuine enthusiasm for the subject and technology
Have the willing to publish research findings in international journals
IELTS overall minimum score of 6.5).
TOEFL iBT (we accept TOEFL iBT, TOEFL iBT Home Edition and TOEFL iBT Paper Edition) - 92 total and minimum skill component scores of 20 reading, 20 listening, 21 speaking and 20 writing.

Pearson PTE
PTE Academic, PTE Academic Online, PTE Academic UKVI - 65 overall and 62 in all skill components.
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  • 雅思总分:6.5
  • 托福网考总分:92
  • 托福笔试总分:160
  • 其他语言考试:PTE Academic UKVI - 65 overall and 62 in all skill components.

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课程简介

This PhD project will focus on developing, evaluating, and demonstrating a novel prognostics and condition monitoring solution for Lithium-Ion battery system which is able to address all main three challenges of prognostics concurrently. A comprehensive test-bed for in-depth studies will be used for experiments for demonstration and evaluation. Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life (RUL) is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Different solutions have been developed to accurately estimate the state of the charge and state of health because they play important roles in safe and reliable usage of lithium-ion batteries. However, these solutions still stays immature to handle simultaneously all main three challenges (robustness, reliability and applicability) of prognostics solution, which are still open areas.
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