An Active Acoustic Emission Method for EV Battery State of Heath Monitoring

In dealing with various challenges, including high complexity, limited applicability and low robustness in different practical applications, the active acoustic emission (AE) technology is developed to achieve the rapid, simple, low-cost, and accurate battery health monitoring that can be more flexibly applied to different scenarios, including battery production line, real-time monitoring on EVs and retired battery classification. The proposed technology puts insights into changes in material property rather than voltage and current measurements of the battery. It combines the application of both ultrasound and AE. The ultrasonic excitation can sufficiently sense the changes in the battery material property caused by the degradation. Based on the effective development of the signal processing methods for AE signal analysis, the effective health-related features under different state of health (SOH) can be analysed and extracted. Therefore, the monitoring and prediction model used considering different influencing factors (e.g. temperature and state of charge) can be built using the machine learning modelling. The project will be carried out in collaboration with the partner to promote the knowledge exchange and academic partnership, thus contributing to project success and maximising benefits to academics, industrial applications and UK economy growth. In particular, the partner will contribute to the guidance on the experimental test and the developed technology will technically support the development on the online/offline EV battery SOH monitoring. The secondment will gain more benefits from the collaborative project, including: • Opportunities for further academic development and self-improvement • Potentials for commercialization • Building long-term research and/or knowledge exchange collaborations The main research activities and anticipated outputs are given as follows: Main research activities: 1) Study the working mechanism of a series of acoustic activities coupled within the battery for acoustic emission excitation; 2) Establish an active acoustic emission model and explore the influence of battery electrode mechanical properties on active acoustic emission signals; 3) Design the active acoustic emission test platform; 4) Develop efficient signal processing methods for the analysis of raw emission signals; 5) Extract health-related features from acoustic emission signals and analyse the effects of various influencing factors on the features; 6) Develop a multi-factor coupling model for battery health state monitoring. Outputs and outcomes: 1) Establish an active acoustic emission theoretical model for sensing mechanical properties of battery electrodes; 2) Establish an active acoustic emission method for fast and reliable monitoring of battery state of health; 3) Attend 1 international academic conference and publish 1-2 papers. The identified key challenges within the partner are as follows: • Long-term time for battery degradation experiments and data acquisition; • Gap for laboratory research and industrial applications.