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Due to traffic congestion and environmental pollution, connected automated vehicle (CAV) technologies based on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure communication (V2I) have gained increasing attention from both academia and industry. Connected hybrid electric vehicles (CHEVs) offer great opportunities to reduce vehicular operating costs and emissions. However, in complex traffic scenarios, high-quality real-time energy management of CHEVs remains a technical challenge. To address the challenge, this paper proposes a hierarchical eco-driving strategy that consists of speed planning and energy management layers. At the upper layer, by leveraging the real-time traffic data provided by vehicle-to-everything (V2X) communication, dynamic traffic constraints are predicted by the traffic flow predictor developed based on the Hankel dynamic mode decomposition algorithm (H-DMD).

Accurate estimation of battery state of health (SOH) has become indispensable in ensuring the predictive maintenance and safety of electric vehicles (EVs). While supervised machine learning excels in laboratory settings with adequate SOH labels, field-based SOH data collection for supervised learning is hindered by EVs' complex conditions and prohibitive data collection costs. To overcome this challenge, a battery SOH estimation method based on semi-supervised regression is proposed and validated using field data in this paper. Initially, the Ampere integral formula is employed to calculate SOH labels from charging data, and the error of labeled SOH is reduced by the open-circuit voltage correction strategy. The calculation error of the SOH label is confirmed to be less than 1.2%, as validated by the full-charge test of the battery packs.