Exploring Different Time-series-Transformer (TST) Architectures: A Case Study in Battery Life Prediction for Electric Vehicles (EVs)

TL;DR

This paper evaluates various Time-series-Transformer architectures for predicting battery life parameters in electric vehicles, incorporating environmental and operational data to improve accuracy over traditional models.

Contribution

It introduces novel TST architectures, including encoder TST + decoder LSTM and hybrid TST-LSTM, tailored for EV battery parameter prediction, and compares them with existing models.

Findings

Hybrid TST-LSTM outperforms traditional models in accuracy.

Incorporating environmental factors improves prediction quality.

New TST architectures effectively model complex battery behavior.

Abstract

In recent years, battery technology for electric vehicles (EVs) has been a major focus, with a significant emphasis on developing new battery materials and chemistries. However, accurately predicting key battery parameters, such as state-of-charge (SOC) and temperature, remains a challenge for constructing advanced battery management systems (BMS). Existing battery models do not comprehensively cover all parameters affecting battery performance, including non-battery-related factors like ambient temperature, cabin temperature, elevation, and regenerative braking during EV operation. Due to the difficulty of incorporating these auxiliary parameters into traditional models, a data-driven approach is suggested. Time-series-transformers (TSTs), leveraging multiheaded attention and parallelization-friendly architecture, are explored alongside LSTM models. Novel TST architectures, including encoder TST + decoder LSTM and a hybrid TST-LSTM, are also developed and compared against existing models. A dataset comprising 72 driving trips in a BMW i3 (60 Ah) is used to address battery life prediction in EVs, aiming to create accurate TST models that incorporate environmental, battery, vehicle driving, and heating circuit data to predict SOC and battery temperature for future time steps.