| 摘要(英) |
The estimation of State-Of-Charge (SOC) in Battery Management Systems (BMS) plays a crucial role, and its accuracy is closely related to system safety. However, with the rapid development of deep learning and neural networks, there is a shift towards utilizing neural networks to search for battery state features, replacing traditional estimation methods. This trend becomes the forefront of future developments in battery state estimation. This paper, based on the characteristics and applicable architecture of neural network models, adopts the Elman neural network [1-4] as the central model for the neural network estimation system. The system is designed to estimate battery SOC based on the neural network algorithm framework.
In the system design, the estimation system is divided into two parts: Parameter Training Module (Training Part) and Real-Time Estimate Module (Real-Time Estimate Part). The Parameter Training Module utilizes past charge-discharge data to identify battery feature parameters. The Real-Time Estimate Module updates the circuit in real-time based on the updated feature parameters, achieving deep learning chip planning.
In the Real-Time Estimate Module, during actual battery operation, the significant fluctuation in battery voltage and current caused by dynamic changes in battery load leads to a decrease in SOC estimation accuracy. To mitigate estimation errors, this paper incorporates a three-stage weight adapter in the chip to fine-tune weights and enhance real-time estimation accuracy, overcoming estimation inaccuracies. The system simulation for parameter training and chip design planning is conducted using dynamic stress testing (DST), Federal Urban Driving Schedule (FUDS), and real driving data from a 2014 BMW i3 BEV (SOC 90% - 10%) and a 2014 BMW i3 BEV (SOC 56.8% - 9.9%). The chip design is based on the TSMC-40nm process, and its functionality is verified using the SMIMS Veri Enterprise Xilinx FPGA, successfully achieving the goal of deep learning algorithm-based battery SOC estimation. |
| 參考文獻 |
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