Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles

TL;DR

This paper explores the use of NARXNN, a neural network model, for accurate State of Charge estimation in Li-ion batteries across various EV drive cycles, demonstrating superior performance over traditional methods.

Contribution

The paper introduces a NARXNN-based approach for SOC estimation and shows its effectiveness across multiple real-world drive cycles, outperforming conventional machine learning techniques.

Findings

NARXNN achieves MSE in the 1e-5 range.

Outperforms traditional machine learning methods.

Effective across diverse drive cycles.

Abstract

Electric Vehicles (EVs) are rapidly increasing in popularity as they are environment friendly. Lithium Ion batteries are at the heart of EV technology and contribute to most of the weight and cost of an EV. State of Charge (SOC) is a very important metric which helps to predict the range of an EV. There is a need to accurately estimate available battery capacity in a battery pack such that the available range in a vehicle can be determined. There are various techniques available to estimate SOC. In this paper, a data driven approach is selected and a Nonlinear Autoregressive Network with Exogenous Inputs Neural Network (NARXNN) is explored to accurately estimate SOC. NARXNN has been shown to be superior to conventional Machine Learning techniques available in the literature. The NARXNN model is developed and tested on various EV Drive Cycles like LA92, US06, UDDS and HWFET to test its performance on real world scenarios. The model is shown to outperform conventional statistical machine learning methods and achieve a Mean Squared Error (MSE) in the 1e-5 range.