Efficient energy utilization is a critical aspect of the concept of green and sustainable development. With the advances in technology and the development of more efficient energy technologies, there is a need to increase energy generation and storage to meet the increasing demands. In particular, lithium-ion batteries are deemed promising candidates for clean and efficient energy storage owing to their high energy and volume energy density and low self-charge properties. However, there are still numerous challenges in the practical use of lithium-ion batteries that need to be addressed. This includes accurate monitoring and evaluation of the state of the heath (SOH) and state of the charge (SOC) of the batteries that are necessary to improve their safety and service life.
Several SOH and SOC detection methods have been proposed, and they can be categorized either as model-based or non-model-based approaches. Ultrasonic detection has been widely used for the convenient and cost-effective estimation of SOH/SOC. Unfortunately, most of the existing studies on ultrasonic detection methods are based on compressional waves that suffer from numerous limitations leading to inaccurate results. Based on recent research findings, most of these drawbacks have been addressed through ultrasonic wave-guided technology that has proved feasible and effective for the accurate detection of SOH/SOC. It can also be rapidly developed in nondestructive testing owing to its advantages. Nevertheless, despite the good progress, the application of ultrasonic wave-guided technology faces numerous challenges. The signal processing methods used are limited in the time domain due to a lack of detailed analysis of the multi parameters.
Non-contact detection based on scanning laser doppler vibrometer (SLDV) is more efficient than wave-guided detection methods due to its ability to detect wave signals at various positions and provide comprehensive information about the battery structures. Equipped with this research, a group of researchers at Jiangsu University: Dr. Guoqi Zhao, Mr. Yu Liu, Dr. Shiping Jiang, and Professor Wenfeng Hao, in collaboration with Dr. Gang Liu from Guangzhou Tianma Group Tianma motorcycle Co. proposed a non-contact guided-wave-based method for estimating the SOH and SOC of lithium-ion battery based on ultrasonic guided wave technology. Their work is currently published in the journal, Journal of Energy Storage.
In their experiment, a single fixed piezoelectric transducer was used to activate the guided wave signals while the guided waves were recorded using an SLDV. Based on the multi-parameter analysis, the direct wave signals were analyzed in the time-frequency distribution, time-domain and frequency-domain. This enabled the use of the three-wave guided parameters: power spectral density, signal amplitude and flight time combined with the aging and charge-discharge cycle to monitor and evaluate the SOC/SOH of the lithium-ion battery. The evaluation accuracy was improved by filtering the response signals in the time domain.
Results showed good consistency between the SOC and SOH and the guided wave parameters in the frequency-domain, time-domain and time-frequency distribution. Therefore, the transitions in the aging process and the charge-discharge cycle were effectively represented by the signal amplitude analysis. On the other hand, the sensitivity of the ultrasonic guided wave technology to the estimation of the SOH and SOC exhibited a non-linear relationship with the battery aging and was attributed to the changes in the lattice structure of the electrode during aging.
In summary, the authors evaluated the state of health and state of charge of lithium-ion batteries using non-contact ultrasonic guided wave technology. Based on the experimental results, a comprehensive analysis of the direct wave signals was presented, providing more information about the battery structure. Additionally, different factors affecting the propagation of the guided waves, such as the electrochemical coupling changes during the charging/discharging cycles of the battery, were identified. In a statement to Advances in Engineering, Professor Wenfeng Hao explained the study provided more opportunities for advanced application of this technology to develop efficient lithium-ion batteries for different applications.
Zhao, G., Liu, Y., Liu, G., Jiang, S., & Hao, W. (2021). State-of-charge and state-of-health estimation for lithium-ion battery using the direct wave signals of guided wave. Journal Of Energy Storage, 39, 102657.