Artificial Intelligence-Based Field-Programmable Gate Array
Accelerator for Electric Vehicles Battery Management System 12-07-03-0016
This also appears in
SAE International Journal of Connected and Automated Vehicles-V133-12EJ
The swift progress of electric vehicles (EVs) and hybrid electric vehicles (HEVs)
has driven advancements in battery management systems (BMS). However, optimizing
the algorithms that drive these systems remains a challenge. Recent
breakthroughs in data science, particularly in deep learning networks, have
introduced the long–short-term memory (LSTM) network as a solution for sequence
problems. While graphics processing units (GPUs) and application-specific
integrated circuits (ASICs) have been used to improve performance in AI-based
applications, field-programmable gate arrays (FPGAs) have gained popularity due
to their low power consumption and high-speed acceleration, making them ideal
for artificial intelligence (AI) implementation. One of the critical components
of EVs and HEVs is the BMS, which performs operations to optimize the use of
energy stored in lithium-ion batteries (LiBs). Due to the nonlinear
electrochemical nature of these batteries, estimating states of charge (SoC),
states of health (SoH), and remaining useful life (RUL) is challenging. This
article proposes an advanced AI-based BMS that uses LSTM to accurately estimate
LiB states, providing crucial information for battery performance optimization.
The proposed design is implemented in Python for training and validation. The
hardware prototype is synthesized using Xilinx Vitis High-Level Synthesis (HLS)
and implemented on Xilinx Zynq System-on-Chip (SoC) PYNQ Z2 board, achieving low
root mean squared error (RMSE) values of 0.3438 and 0.3681 in training and
validation, respectively.
Citation: Nagarale, S. and Patil , B., "Artificial Intelligence-Based Field-Programmable Gate Array Accelerator for Electric Vehicles Battery Management System," SAE Intl. J CAV 7(3):2024, https://doi.org/10.4271/12-07-03-0016. Download Citation
Author(s):
Satyashil D. Nagarale, B. P. Patil
Affiliated:
Savitribai Phule Pune University, Department of Electronics and
Telecommunication Engineering, Pimpri Chinchwad College of Engineering,
India, Savitribai Phule Pune University, Department of Electronics and
Telecommunication Engineering, Army Institute of Technology, India
Pages: 16
ISSN:
2574-0741
e-ISSN:
2574-075X
Related Topics:
Battery management systems (BMS)
Hybrid electric vehicles
Electric vehicles
Lithium-ion batteries
Energy conservation
Energy consumption
Machine learning
Integrated circuits
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