Neural Network Energy Management Training via Simulated Driving Cycles

The patent replaces traditional rule-based mechanical control logic with machine learning algorithms and artificial intelligence models. The energy management system uses neural networks and reinforcement learning to automatically learn optimal control strategies from training data, eliminating the need for explicit programming of complex decision rules. This substitution enables the system to handle multi-voltage configurations, high-voltage systems, and numerous electronic components adaptively without requiring complex predefined logic for each scenario.

The patent implements a comprehensive training phase before actual operation, where the machine learning model is pre-trained using simulated driving cycles and training datasets that represent various driving scenarios, weather conditions, and system configurations. This preliminary action allows the model to learn optimal energy management strategies offline, so that during actual vehicle operation, the system can make rapid decisions without requiring additional training data. The training phase consolidates the data requirements into a one-time setup process rather than continuous data collection during operation.

The patent introduces a reflex module as an intermediary layer between the reinforcement learning agent and the final control decisions. The agent proposes control actions, which are then evaluated by the reflex module that checks whether these actions satisfy predefined safety constraints and operational rules. This intermediary structure separates the learning function from the safety verification function, allowing the system to maintain high safety standards through explicit reflex checks while keeping the learning process relatively simple. The reflex module acts as a gatekeeper that filters agent proposals without requiring the agent itself to understand complex safety constraints.