The research team of NARI Technology has proposed a method for early warning of potential safety hazards in lithium-ion power battery charging across multiple time scales. The breakthrough in its key technologies will significantly improve the safety and reliability of power battery applications.
With the rapid increase in the number of new energy vehicles being promoted and applied, power battery safety accidents have become one of the key bottlenecks restricting the rapid development of new energy vehicles, and at the same time, they seriously threaten the personal safety of passengers.
With the strong support of the National Key Research and Development Program for New Energy Vehicles, specifically the project "Safety and Interoperability Technology of Electric Vehicle Infrastructure Operation", the research team of NARI Technology Co., Ltd. has proposed a multi-timescale lithium-ion power battery charging safety hazard early warning method and established an interactive response mechanism between electric vehicles, charging infrastructure, and an integrated monitoring platform. The breakthrough in its key technologies will significantly improve the safety and reliability of power battery applications.
The project investigated the thermal runaway mechanism of power batteries under overcharge conditions and the performance degradation mechanism under different stresses, and established a relatively complete characterization system of power battery safety parameters.
By comprehensively utilizing real-time and historical vehicle operating data and applying data mining techniques to analyze the coupling characteristics between power battery characteristic parameters and safety hazards, a monitoring method for the safe operation of power battery systems was established.
By utilizing the characteristic parameters of the capacity increment curve, a power battery health status diagnosis method based on support vector machine was established, realizing the identification of power battery degradation modes;
To address the safety hazards caused by sudden capacity changes in high-energy-density power batteries, a capacity acceleration degradation identification and early warning technology based on quantile regression was developed. By combining battery health status assessment, accelerated degradation identification, and real-time battery operating status, a multi-timescale power battery safety risk assessment and early warning method was formed, which effectively improved the safety and reliability of power battery use.
