Vehicle Battery Load Cut-Off Based on Usage Pattern Learning

The patent applies dynamics by transitioning from fixed phased cut-off to dynamic adaptive cut-off. The system continuously monitors vehicle usage patterns through the network monitor and adjusts power cut-off timing based on learned patterns. This allows the power supply strategy to adapt dynamically to actual driver behavior, cutting power earlier when usage is unlikely and maintaining power longer when needed, thereby reducing unnecessary current drainage while preserving driver convenience.

The patent implements feedback through the network monitor that continuously observes vehicle network states and driver interactions. This feedback loop provides real-time information about actual vehicle usage, which is then processed by the usage pattern learner to adjust future power cut-off decisions. The feedback mechanism enables the system to learn from past behavior and optimize power management, reducing energy loss while maintaining convenience.

The system uses dynamics to adjust power cut-off timing based on learned usage patterns. Instead of fixed early cut-off, the system dynamically determines optimal cut-off points that balance battery protection with driver needs. By adapting cut-off timing to actual usage patterns, the system can cut power early when appropriate (improving battery performance) while maintaining power longer when driver interaction is expected (preserving convenience).

The patent applies parameter changes by modifying power cut-off timing parameters based on learned usage patterns. The system adjusts temporal parameters (when to cut power) and conditional parameters (which loads to cut) based on analyzed driver behavior. This parameter adaptation allows optimization of battery performance through earlier cut-off in low-usage scenarios while maintaining convenience in high-usage scenarios.

The system applies self-service through automated usage pattern learning and adaptive control. Rather than requiring complex manual configuration or intervention, the system automatically monitors network states, learns usage patterns, and adjusts power management strategies independently. This self-learning capability achieves improved energy efficiency without proportionally increasing system complexity, as the learning algorithms operate autonomously using existing sensor data.

The patent applies universality by using the existing vehicle network monitor and controller for dual purposes: original communication functions and usage pattern learning for power management. The same hardware infrastructure supports multiple functions, avoiding the need for separate dedicated sensors or complex additional systems. This multi-functionality approach improves energy efficiency while minimizing the increase in overall system complexity.