EV Charging Target State of Charge Based on Recharged Energy
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Solution Overview
Problem
Existing technologies for managing the electrical recharge of electrified vehicle traction batteries do not adequately account for the specific use patterns and habits of vehicle users, leading to suboptimal battery longevity.
Innovation Solution
A process for managing a recharge session of a traction battery that involves recording the amount of energy recharged, configuring the target charge level based on this data, and using a cumulative distribution function to determine an optimal energy need, thereby setting an optimal target load level for the battery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the target state of charge is set to 100% to ensure sufficient power for unexpected driving needs, then the user's driving security is improved, but the battery longevity deteriorates due to increased electrochemical stress
Solution Approach 1:
The patent implements a dynamic target state of charge adjustment mechanism that adapts the charging limit based on learned user driving patterns. The system transitions from a static 100% charge target to a dynamically optimized target that varies according to predicted driving needs, thereby reducing unnecessary time at maximum charge while maintaining driving security.
Solution Approach 2:
The system employs feedback through machine learning algorithms that continuously analyze historical charging and driving data. This feedback loop enables the system to learn user patterns and automatically adjust the target state of charge, balancing battery longevity with driving security without requiring manual user intervention.
2Duration of action of stationary object
If pre-programmed limiting functions are implemented to reduce target charge level and extend battery life, then battery longevity is improved, but user convenience deteriorates as users must manually configure and monitor charging parameters
Solution Approach 1:
The patent implements a self-service charging management system that automatically learns user patterns and adjusts charging parameters without manual intervention. The system autonomously monitors driving behavior, charging history, and battery status to dynamically optimize the target state of charge, eliminating the need for users to manually configure charging limits while extending battery life.
Solution Approach 2:
The system replaces manual user configuration with automated machine learning algorithms. Instead of requiring users to mechanically adjust charging parameters through interfaces and settings, the system uses computational algorithms to automatically determine optimal charging targets based on learned patterns, significantly improving ease of operation.
3Duration of action of stationary object
If intelligent charging monitoring functions are implemented to manage overnight charging based on alarm time, then battery longevity is improved by reducing maximum charge duration, but adaptability deteriorates as the system cannot account for varying user usage patterns
Solution Approach 1:
The patent transforms the static overnight charging monitoring function into a dynamic system that adapts to varying user patterns. The machine learning component continuously learns from historical data to adjust charging targets based on actual usage patterns, making the system versatile enough to handle different scenarios while maintaining the benefit of reduced maximum charge duration.
Solution Approach 2:
The system dynamically changes the target state of charge parameter based on learned usage patterns rather than using a fixed threshold. This parameter adaptation enables the system to maintain battery longevity benefits while becoming adaptable to diverse user behaviors and charging scenarios.
Data Source
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AI summary
The present invention relates to a method for managing an electric vehicle battery recharging session, wherein the method comprises determining a target state of charge level for commanding the stoppage of the recharging session. According to the invention, the method furthermore comprises the following steps: recording (33) an amount of recharged energy (QER) in a recharging session, and configuring (38) the target state of charge level (SOCc) as a function of said amount of recharged energy (QER). The invention is applicable in particular to electric motor vehicles able to be recharged from a charging terminal and makes it possible to control the end of recharging in a personalized manner according to the user's recharging habits and uses.