A framework integrates map data, Bidirectional LSTM driver behavior modeling, and quasi-steady physics-based energy consumption to generate personalized BEV velocity, power, and SOC profiles that capture individual patterns across urban, freeway, and hilly routes.
A Novel Energy-Efficiency Optimization Ap- proach Based on Driving Patterns Styles and Experimental Tests for Electric Vehicles,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data
A framework integrates map data, Bidirectional LSTM driver behavior modeling, and quasi-steady physics-based energy consumption to generate personalized BEV velocity, power, and SOC profiles that capture individual patterns across urban, freeway, and hilly routes.