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Related Concept Videos

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A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
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Updated: Jul 15, 2025

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
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A Sensor-Fault-Estimation Method for Lithium-Ion Batteries in Electric Vehicles.

Tianyu Lan1, Zhi-Wei Gao1, Haishuang Yin1

  • 1Research Centre for Digitalization and Intelligent Diagnosis to New Energies, College of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163000, China.

Sensors (Basel, Switzerland)
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Summary

This study introduces a new model-based fault diagnosis scheme for electric vehicle battery sensors. It enables early detection of voltage, current, and temperature sensor faults for improved safety and reliability.

Keywords:
descriptor observerfault diagnosisfault estimationlithium-ion batterysensor fault

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Automotive Engineering

Background:

  • Electric vehicles (EVs) and lithium-ion batteries (LIBs) are rapidly advancing.
  • Battery safety and reliability are critical concerns for LIBs.
  • Battery management systems (BMS) rely on accurate sensor data for optimal performance.

Purpose of the Study:

  • To develop validated electrical and thermal models for LIBs.
  • To establish a model-based fault diagnosis scheme for battery sensors.
  • To achieve early-stage detection of sensor faults in LIBs.

Main Methods:

  • Utilized descriptor proportional and derivative observer systems for sensor diagnosis.
  • Developed validated electrical and thermal models of LIBs using experimental data.
  • Implemented real-time estimation of voltage, current, and temperature sensor faults.

Main Results:

  • Successfully simulated various fault types to verify the proposed diagnosis scheme.
  • Demonstrated the capability of the observer systems to estimate sensor faults in real-time.
  • Validated the effectiveness of the model-based approach using experimental battery data.

Conclusions:

  • The proposed model-based fault diagnosis scheme effectively detects sensor faults in LIBs.
  • Early fault detection enhances the safety and reliability of battery management systems.
  • This approach contributes to the advancement of safer and more dependable electric vehicles.