LG Energy Solution Ltd. has joined hands with U.S. semiconductor company Qualcomm Technologies Inc. to commercialize a cutting-edge battery management system (BMS) diagnostic solution.

The South Korean company announced on Monday that it signed a co-promotion agreement with Qualcomm Technologies to develop a BMS diagnostic solution based on a system-on-chip platform.

The solution integrates Qualcomm’s Snapdragon chip with the car to cloud connected service platform, enabling automakers to utilize the technology.

Earlier this year, LG Energy Solution and Qualcomm unveiled plans to co-develop a next-generation BMS diagnostic solution for electric vehicles.

Since then, a dedicated task force has been established to advance the research.

“The partnership has proven the excellence of LG Energy Solution’s BMS technology and the synergies achieved through collaboration with Qualcomm,” said an official from LG Energy Solution. “Through the technology-sharing and validation process, we have confirmed the superiority of our BMS solutions and decided to move forward with full-scale commercialization.”

The new BMS solution is the industry’s first to leverage high-performance SoC computing capabilities, unlike previous systems that operated on low-spec hardware.

Its safety diagnostic functions have been significantly enhanced, enabling faster and more accurate detection of anomalies, such as potential fire hazards, through expanded data collection compared to existing BMS systems.

Additionally, the degradation diagnostic capabilities of the solution are 80 times more sophisticated, owing to improved computational power.

Advanced algorithms, previously incompatible with older systems, are now implemented. These include features such as predicting remaining battery capacity over time and providing detailed performance indicators for components like the anode and cathode.

The advanced BMS diagnostic software offers real-time functionality without the need for a separate server connection, enabling in-vehicle analysis and diagnosis of driving data.