Battery management system based on adaptive digital twinning

By using an adaptive digital twin system, combined with sensor networks, multi-scale models, and reinforcement learning controllers, the dynamic adaptation problem of traditional battery management systems under battery aging and environmental changes is solved. This enables high-precision estimation of battery status and fault early warning, thereby improving the safety and lifespan of the system.

CN121076282APending Publication Date: 2025-12-05深圳市华芯控股有限公司
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Patent Information

Application Number
CN202511605857.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-05
Publication Date
2025-12-05

AI Technical Summary

Technical Problem

Traditional battery management systems struggle to adapt to battery aging and environmental changes, leading to increased SOC estimation errors, an inability to respond to sudden changes in battery state in real time, and a lack of dynamic adaptability, thus affecting the effectiveness of intelligent decision-making.

Method used

An adaptive digital twin system is adopted, which combines a physical layer sensor network, edge computing nodes, an adaptive multi-scale model and a real-time data engine. Through a reinforcement learning controller and a fault prediction module, dynamic simulation of battery status and optimization of charging and discharging strategies are realized, and multi-source data is integrated for real-time updates and fault warnings.

Benefits of technology

It achieves accurate long-term mapping between the physical battery and the digital twin model, improving the accuracy of SOC estimation and system safety. It can maintain SOC balance under different conditions, dynamically optimize charging and discharging strategies, provide early warning of potential faults, and extend battery life.

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Abstract

The invention relates to the technical field of BMS and the like, and provides a battery management system based on adaptive digital twinning, a physical layer of the battery management system comprises a battery pack, a sensor network and an edge computing node, and is responsible for data acquisition and preprocessing; the digital twin layer comprises a self-adaptive multi-scale model and a real-time data engine, battery behaviors are dynamically simulated by coupling electrochemical, thermal and aging models, a future state trajectory prediction result is output, and the real-time data engine fuses sensor data, historical data and simulation data to drive model updating; the intelligent decision-making layer comprises a reinforcement learning controller and a fault prediction module which are deployed in a local server, the reinforcement learning controller dynamically optimizes a charging and discharging strategy according to a prediction result of the digital twinborn layer and issues and executes the charging and discharging strategy, and the fault prediction module analyzes multi-source time sequence data based on an LSTM network so as to early warn thermal runaway and short circuit risks in advance. According to the invention, long-term accurate mapping and adaptive adjustment between the battery physical entity and the digital model can be realized.
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