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In recent years, battery technology for electric vehicles (EVs) has been amajor focus, with a significant emphasis on developing new battery materialsand chemistries. However, accurately predicting key battery parameters, such asstate-of-charge (SOC) and temperature, remains a challenge for constructingadvanced battery management systems (BMS). Existing battery models do notcomprehensively cover all parameters affecting battery performance, includingnon-battery-related factors like ambient temperature, cabin temperature,elevation, and regenerative braking during EV operation. Due to the difficultyof incorporating these auxiliary parameters into traditional models, adata-driven approach is suggested. Time-series-transformers (TSTs), leveragingmultiheaded attention and parallelization-friendly architecture, are exploredalongside LSTM models. Novel TST architectures, including encoder TST + decoderLSTM and a hybrid TST-LSTM, are also developed and compared against existingmodels. A dataset comprising 72 driving trips in a BMW i3 (60 Ah) is used toaddress battery life prediction in EVs, aiming to create accurate TST modelsthat incorporate environmental, battery, vehicle driving, and heating circuitdata to predict SOC and battery temperature for future time steps.

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