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.

近年来，电动车辆 (EVs) 的电池技术一直备受关注，重点是开发新的电池材料和化学成分，然而准确预测电池参数，如充电状态 (SOC) 和温度，对于构建先进的电池管理系统 (BMS) 仍然是一项挑战。鉴于将这些辅助参数整合到传统模型中的困难，建议采用数据驱动的方法，通过利用多头注意力和并行化友好架构的时间序列转换器 (TSTs) 和 LSTM 模型研究。还开发了新颖的 TST 架构，包括编码器 TST + 解码器 LSTM 和混合式 TST-LSTM，并与现有模型进行比较。使用一组包含宝马 i3 (60 Ah) 的 72 次行驶记录的数据集来解决 EV 中的电池寿命预测问题，旨在创建准确的 TST 模型，将环境、电池、车辆驾驶和加热电路数据纳入，以预测未来时间步骤的 SOC 和电池温度。