Transportation

AI based data-driven approach and hardware accelerators (FPGA) to predict the SoC, SoH and RuL of the LiBs

AP012

Satyashil Nagrale (Pimpri Chinchwad College of Engineering Pune)

Aug 04, 2021 2140 views

AI based data-driven approach and hardware accelerators (FPGA) to predict the SoC, SoH and RuL of the LiBs

The advancement in digitalization and availability of reliable sources of information that provide credible data, Artificial Intelligence (AI) has emerged to solve complex computational real-life problems which was challenging earlier. However, Artificial Neural Networks(ANN) need rigorous main processors and high memory bandwidth, and hence cannot provide expected levels of performance. As a result, hardware accelerators such as Graphic Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs) have been used for improving overall performance of AI based applications. FPGAs are widely used for AI implementation as FPGAs have features like high-speed acceleration, low power consumption which cannot be done using central processors and GPUs. In Electric-powered vehicles (E-Mobility), Battery Management Systems (BMS) perform different operations for better use of energy stored in lithium-ion batteries (LiBs). The LiBs are a non-linear electrochemical system which is very complex and time-variant in nature. Because of this nature, estimation of States like State of Health (SoH) and Remaining Useful Life (RuL) is very difficult. The goal is to develop an advanced AI based BMS that can precisely indicate the LiBs states which will be useful in E-Mobility. This gives useful information for the prediction of when the battery should be removed or replaced and helps to optimize the battery performance and extend battery lifespan.

Project Proposal


1. High-level project introduction and performance expectation

2. Block Diagram

3. Expected sustainability results, projected resource savings

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