Vehicular Battery Discharge Scheduling for Premature Degradation
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Solution Overview
Problem
The frequent recharging of electric vehicle batteries before they reach depletion leads to expedited degradation, reducing their useful life.
Innovation Solution
A system utilizing machine learning models to analyze charging and driving histories, recommending a strategic discharge routine to counteract expedited degradation by periodically discharging the battery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the battery is recharged after minimal use, then the battery is frequently recharged, but the battery experiences expedited degradation
Solution Approach 1:
The system continuously monitors charging patterns and battery state, using machine learning models to analyze charging history and provide feedback recommendations. The system adjusts discharge routines based on real-time battery conditions and historical data, creating a closed-loop control system that optimizes battery maintenance dynamically
Solution Approach 2:
The system performs preliminary discharge actions before expedited degradation occurs by analyzing charging patterns and predicting potential degradation risks. Machine learning models forecast battery health trends and initiate preventive discharge routines in advance to counteract degradation before it becomes problematic
2Reliability
If a discharge routine is implemented to counteract degradation, then battery lifespan is prolonged, but the system complexity increases
Solution Approach 1:
The system monitors and manages its own battery health autonomously using onboard sensors and machine learning algorithms. The vehicle's computing systems automatically analyze charging patterns, predict degradation risks, and execute discharge routines without external intervention, making the complex system self-managing rather than requiring additional control infrastructure
Solution Approach 2:
The system dynamically adjusts discharge parameters such as discharge timing, duration, and intensity based on battery state and historical data. Machine learning models optimize these parameters in real-time, allowing the system to handle complexity through adaptive parameter tuning rather than fixed complex control logic
Data Source
AI summary
Systems/techniques that facilitate strategic discharging of vehicular batteries are provided. In various embodiments, a system can access a charging history of a battery of a vehicle. In various aspects, the system can determine, via execution of a first machine learning model on the charging history, whether the battery is likely to experience expedited degradation. In various instances, the system can recommend, in response to a determination that the battery is likely to experience expedited degradation and via execution of a second machine learning model on the charging history and on a driving history of the vehicle, a discharge routine that is likely to counteract such expedited degradation.