Research on Path Planning and Energy Efficiency Optimization System for New Energy Vehicles in Telematics Environment

This study introduces an energy efficiency optimization system for new energy vehicles using deep learning and multi-objective decision-making algorithms. The system integrates real-time vehicle telemetry, environmental data, and traffic conditions through a four-layer architecture comprising perception, network, platform, and application layers. Testing with 385 electric vehicles over a six-month period showed a reduction in energy consumption from 15.8kWh to 13.2kWh per 100km, with 18.7% savings in high-speed conditions. The system achieved 95.8% prediction accuracy and 99.3% service stability. The optimization algorithms demonstrated significant improvements in route planning, charging efficiency, and driver behavior modification, resulting in a 42.3% reduction in aggressive driving patterns and 15.6% improvement in regenerative braking efficiency.