Machine Learning-Enabled Battery Management System on FPGA for Electric Vehicles

This work implements a field-programmable gate array (FPGA) architecture for an intelligent battery management unit (i-BMU) designed for an electric vehicle (EV) and investigates how it can increase the mileage of the same. The unique feature of the suggested i-BMU is the development of an FPGA architecture to allocate power to various electrical and electronic components of an EV by considering the run-time driving pattern and the state of charge (SoC) of the Li-ion battery. The proposed methodology involves dynamically estimating the SoC of the Li-ion battery using a Long Short-Term Memory Neural Network (LSTM-NN) model while the vehicle is in motion, predicting different driving cycles such as urban, highway, and downhill in real-time using a regression tree algorithm, and intelligently allocating electric power to various EV components based on the predicted driving cycle and the estimated SoC using a proposed power distribution algorithm. The proposed system is designed on the Zynq Ultrascale+ MPSoC development board, and the data given to the system for verification is collected through simulation of various sensor values for an electric bike, such as speed, throttle position, battery voltage, battery current, and GPS coordinates, by generating random data within typical operational ranges. The proposed system is compared with the existing system in terms of chip power consumption (W), area of the chip (mm²), computation time (μs), and throughput. Additionally, the suggested method evaluates the mileage of the EVs and extends their range by 17 km to 36 km depending on the driving pattern.

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