The proposed decentralized DNN-based battery management system achieved a Root Mean Square Error (RMSE) of 1.853% in state-of-charge estimation using BMW i3 battery data.
A decentralized node-level BMS chip using a DNN framework achieved high precision in state-of-charge estimation with an RMSE of 1.853%.
Absolute Event Rate: 0% vs 0%
As Battery Management Systems (BMSs) continue to expand in both scale and capacity, conventional state-of-charge (SOC) estimation methods—such as Coulomb counting and model-based observers—face increasing challenges in meeting the requirements for cell-level precision, scalability, and adaptability under aging and operating variability. To address these limitations, this study integrates a Deep Neural Network (DNN)–based estimation framework into a node-level BMS architecture, enabling edge-side computation at each individual battery cell. The proposed architecture adopts a decentralized node-level structure with distributed parameter synchronization, in which each BMS node independently performs SOC estimation using shared model parameters. Global battery characteristics are learned through offline training and subsequently synchronized to all nodes, ensuring estimation consistency across large battery arrays while avoiding centralized online computation. This design enhances system scalability and deployment flexibility, particularly in high-voltage battery strings with isolated measurement requirements. The proposed DNN framework consists of two identical functional modules: an offline training module and a real-time estimation module. The training module operates on high-performance computing platforms—such as in-vehicle microcontrollers during idle periods or charging-station servers—using historical charge–discharge data to extract and update battery characteristic parameters. These parameters are then transferred to the real-time estimation chip for adaptive SOC inference. The decentralized BMS node chip integrates preprocessing circuits, a momentum-based optimizer, a first-derivative sigmoid unit, and a weight update module. The design is implemented using the TSMC 40 nm CMOS process and verified on a Xilinx Virtex-5 FPGA. Experimental results using real BMW i3 battery data demonstrate a Root Mean Square Error (RMSE) of 1.853%, with an estimation error range of 4.324%, −4.346%.
Shiue et al. (Fri,) reported a other. The proposed decentralized DNN-based battery management system achieved a Root Mean Square Error (RMSE) of 1.853% in state-of-charge estimation using BMW i3 battery data.