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

arXiv: https://arxiv.org/abs/2308.03260 · HTML: https://mlsanity.com/paper/2308.03260/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers](https://mlsanity.com/paper/2306.03099/) (2023)
- [Require Process Control? LSTMc is all you need!](https://mlsanity.com/paper/2306.07510/) (2023)

## Built on by (most cited first, 1 of 1)

- [Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior](https://mlsanity.com/paper/2406.06602/) (2024)

## Abstract (arXiv)

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.