An accurate state of health estimation method for lithium-ion batteries based on expansion force analysis

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for their safe and efficient management. However, commonly used electrical signals often fail to effectively represent battery aging and are particularly sensitive to external disturbances. To overcome this limitation, a novel SOH estimation method is proposed based on the expansion force. An experimental setup is developed to measure expansion force during long-term cycling and dynamic conditions, allowing for a systematic analysis of the relationship between expansion force and battery aging. Key aging indicators are innovatively extracted from phase transition inflection points in the expansion force curve and utilized as inputs to a neural network regression model. Results show that this new method achieves a root mean square error of 0.058 %, an order of magnitude improvement over traditional methods. The effectiveness of the approach is further validated across multiple prediction algorithms, demonstrating its robustness and adaptability, with all errors remaining below 0.2 %. This study offers an innovative and effective solution for SOH estimation, enhancing battery management practices.