Improving state-of-charge estimation accuracy and stability by incorporating ultrasonic signal features

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Abstract
Accurate state-of-charge (SOC) estimation is essential for the safe and reliable operation of lithium-ion batteries. However, conventional SOC estimation methods mainly use electrical signals, which provide limited information about internal battery changes. Ultrasonic sensing provides additional information related to internal structural changes and has attracted increasing attention for SOC estimation. To better assess the value of ultrasonic features as auxiliary inputs for SOC estimation, this study examines them in terms of both correlation with SOC and stability under varying operating conditions. Two representative features, time of flight (TOF) and signal amplitude (SA), are systematically compared under different temperatures and C-rates. Their effectiveness is then further validated by fusing ultrasonic features with voltage, current, and temperature in a multilayer perceptron model. The results show that SA exhibits stronger correlation with SOC and better thermal stability than TOF, and that incorporating SA into the input set of voltage, current, and temperature reduces the root mean square error by 29.0% under cycling conditions and 48.4% under dynamic load profiles. These results indicate that ultrasonic information can effectively improve SOC prediction performance, and the improvement is greater when more stable features are used.
Funding Information
  • and Development Project (2022Z092)
  • National Natural Science Foundation of China (62471267)
  • Graduate Student Scientific Research and Innovation Project of Ningbo University (IF2025028)
  • Natural Science Foundation of Chongqing, China (CSTB2023NSCQ-LZX0045)

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