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Reliability Expert for Energy Storage Battery System

Ampace

Ampace (???) is a global battery-technology company specializing in lithium-ion cells and systems for energy storage, data centers, and critical power. Our R&D pairs advanced cell chemistry with semi-solid-state technology to deliver safe, high-performance batteries at scale. This position is based at our R&D center in Xiamen, China.

Scroll down for a complete overview of what this job will require Are you the right candidate for this opportunity

Responsibilities:

  1. Build a cell-to-system reliability simulation framework coupling electrochemical, thermal, mechanical, aging, and system-condition analysis.
  1. Develop life and degradation models covering capacity fade, resistance growth, balancing failure, thermal-runaway risk, and consistency degradation.
  1. Identify, calibrate, and validate model parameters against lab, test, and field data, with uncertainty analysis.
  1. Build digital-twin and surrogate models for life prediction, risk early-warning, and design optimization.
  1. Support reliability assessment and accelerated-life-test design from cell to module, pack, and container level.
  1. Partner with R&D, test, quality, process, and product teams to turn simulation results into design rules.

Requirements:

  1. PhD in reliability engineering, electrochemistry, materials, mechanical, automation, thermal, or computational modeling.
  1. Experience in Li-ion or energy-storage reliability, life modeling, degradation mechanisms, thermal safety, or multiphysics simulation.
  1. Familiarity with at least one modeling method: equivalent-circuit, single-particle, P2D/DFN, thermal, mechanical, or system-level simulation.
  1. Ability to connect battery testing, BMS data, operating conditions, and failure modes into mechanistic insight.
  1. Strong English research and technical communication for work with global teams.

Preferred:

  1. Cross-scale, cell-to-system modeling experience; UPS energy-storage project experience.
  1. Machine learning, Bayesian inference, digital-twin, or uncertainty-quantification experience.
  1. Background with a recognized reliability-engineering platform such as Maryland CALCE / CRR; patents, publications, or standards work.

???(Ampace)??????????????,???????????????????,????????????????????????????????????????????,?????????????????????????????

  1. ???????????????????,??????????????????????????????????? xhyhwjd
  2. ?????????????(Surrogate Model),????????????????????

  1. ????????????,????????(ECM)??????(SPM)?P2D/DFN???????????????????????
  2. ???????????BMS??????????????,???????????????

  1. ???????(Cell to System)???????,?UPS??????????????
  2. ????????????????????????(Uncertainty Quantification)?????????
  3. ?????????????(? Maryland CALCE?CRR)??????,???????????????????????

Vacancy posted 21 hours ago
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