Page 831 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

• To make the system continuously learns from historical telemetry and energy
consumption data of driver behavior/profiles
• To compute recommendations based on factors such as live battery state (i.e, by reading
battery SoC, SoH, temperature, discharge rate — not just SoC, terrain complexity,
congestion patterns, and real-time charging slot availability. Transport 4.10: Intelligent
• The overarching aim is to create a robust, self-improving system that minimizes energy
wastage, reduces range anxiety, and enables autonomous vehicles to make context-
aware, data-driven navigation, proactive driving prediction, not just route display and
charging decisions in real time.

Sub-Scenarios (Expanded)

To give a more concrete idea of how the system functions in different contexts, we outline the
following sub-scenarios:

Sub-Scenario 1: Energy Optimization

• Uses LSTM/Transformer models to predict high-consumption zones (e.g., steep inclines
or traffic-heavy areas).
• Recommends alternate routes or adaptive cruise control strategies to minimize energy
drain.

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