Battery Charging Setpoint Optimization via Probability Models
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Solution Overview
Problem
Existing electric vehicle charging management systems face inaccuracies and uncertainties in determining energy setpoints for batteries, leading to inappropriate recharging strategies and reduced system performance, especially when user energy requirements are unclear or statistical data is limited.
Innovation Solution
A method using a processing unit to determine energy setpoints for batteries by analyzing probability laws of energy requirements based on historical data, maximizing the probability that the setpoint meets or exceeds the actual energy needs while ensuring the total energy delivered by the charging system is efficiently utilized, employing binary search algorithms to adjust setpoints accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy setpoints are determined based on user input and forecast energy production, then the charging system can plan recharging to minimize cost and environmental impact, but inaccuracies and uncertainties in energy requirement determination lead to inappropriate recharging strategies
Solution Approach 1:
The system implements feedback by using actual charging data from multiple charging sessions to update and refine the probability distribution models of energy requirements. The processing unit continuously learns from historical data, adjusting the probability distributions to better reflect actual user needs, thereby improving the accuracy of energy setpoint determination over time while maintaining cost-effective charging strategies
Solution Approach 2:
The system changes the parameter representation from deterministic energy requirements to probabilistic distributions. By modeling energy requirements as probability distributions with evolving parameters (mean, standard deviation) based on historical data, the system can account for uncertainties and variations in user needs, leading to more robust charging strategies that are both efficient and reliable
2Ease of operation
If the charging system assigns fixed energy setpoints without considering probability distributions, then the system operation is simple, but the setpoints may not meet actual energy needs leading to reduced performance
Solution Approach 1:
The system performs preliminary actions by pre-calculating probability distribution parameters from historical data before actual charging decisions are made. The processing unit builds and maintains probability models in advance, allowing it to quickly determine appropriate energy setpoints during charging operations without complex real-time calculations, thus maintaining operational simplicity while achieving precise energy delivery
Solution Approach 2:
The probability distribution model serves as an intermediary between simple charging operations and precise energy delivery requirements. Instead of directly complex calculations during charging, the system uses the pre-computed probability distributions as a mediator to translate operational simplicity into precise energy setpoint determination that meets actual user needs
Data Source
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AI summary
The invention relates to a method for management, by at least one processing unit (107), of the collection of batteries (Bi) connected to the recharge terminals (101) of a recharge system, comprising the following steps: a) determining the total quantity of energy which can be delivered by the recharge system during a recharge period; b) for each battery (Bi), determining a probability relationship for the effective energy need of the battery; and c) for each battery (Bi), determining a value of energy to inject into the battery (Bi), the values been determined by taking account of the probability relationships in order to maximize, for each battery (Bi) the probability that the value assigned to the battery is greater than or equal to the effective energy need of the battery, and in such a way that the sum of the values is less than or equal to the total quantity of energy able to be delivered by the recharge system during said period.