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Detection of connection faults and estimation of contact resistance in lithium-ion battery packs with canonical variable analysis and local Mahalanobis distance

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  • Shen, Dongxu
  • Lyu, Chao
  • Yang, Dazhi
  • Hinds, Gareth
  • Xu, Shaochun
  • Bai, Miao

Abstract

Connection faults between cells of a battery pack can lead to increased contact resistance (CR) and thus abnormal heating at the connections, which can seriously damage the battery pack. Existing methods often confuse connection faults with increased internal resistance in cells or lack the ability to estimate CR quantitatively. This work proposes to detect connection faults and estimate CR in lithium-ion battery packs using canonical variable analysis (CVA) and local Mahalanobis distance (LMD). First, an interleaved voltage measurement circuit is used to capture information about connection faults. Subsequently, various statistics are extracted from the voltage measurements using CVA, and the fault-free control limits for these statistics are established using kernel density estimation. Consequently, any exceedance of these control limits would indicate the occurrence of anomalies. With this method, connection faults and increased internal resistance can be differentiated by the number and location of anomalous voltage measurements. Following fault detection, the LMD between the sample with the connection fault and the healthy domain containing normal samples is calculated. An analytical expression describing the relationship between CR and LMD is derived to estimate CR. Experimental results demonstrate that the average error in CR estimation is 1.04mΩ, which empirically validates the proposal.

Suggested Citation

  • Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Xu, Shaochun & Bai, Miao, 2025. "Detection of connection faults and estimation of contact resistance in lithium-ion battery packs with canonical variable analysis and local Mahalanobis distance," Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225002671
    DOI: 10.1016/j.energy.2025.134625
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    References listed on IDEAS

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    1. F. Degen & M. Winter & D. Bendig & J. Tübke, 2023. "Energy consumption of current and future production of lithium-ion and post lithium-ion battery cells," Nature Energy, Nature, vol. 8(11), pages 1284-1295, November.
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    3. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Wang, Lixin, 2023. "Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network," Energy, Elsevier, vol. 274(C).
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