Driving Condition-based Energy Management Strategy of Hybrid Vehicles
7 April, 2024
- Developed a TCN-based driving condition forecasting model, achieving a 23% improvement in forecast accuracy and enabling proactive energy allocation decisions.
- Designed a DDPG-based multi-agent energy management system for hybrid vehicles, with each agent optimised for specific driving conditions using continuous state spaces (battery SOC, vehicle speed) and continuous action outputs (engine/motor torque control).
- Developed a driving condition-aware agent adaptive switching mechanism that dynamically selects between specialised agents based on real-time driving scenarios (urban/highway/congested traffic), achieving a 29% reduction in vehicle energy consumption under real stochastic driving conditions.