  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.
