Dynamic Battery Charging Schedule for Fleet Power Management
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Solution Overview
Problem
Current battery recharging management systems for electric vehicle fleets face challenges in adapting to varying numbers of vehicles, leading to overloading or underutilization of charging points, and fail to optimize recharging times based on user behavior and terminal power limitations, often requiring manual reservation and lacking compatibility across different vehicle manufacturers.
Innovation Solution
A dynamic and predictive management system that records and analyzes electrical consumption habits of each vehicle to automatically schedule recharging periods, optimizing the use of available power and adapting to changes in user behavior, without requiring specific vehicle or manufacturer data, by identifying users and calculating optimal recharging times based on past data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the power of the supply point is increased to accommodate more vehicles, then the number of vehicles that can be recharged simultaneously increases, but the installation and operating costs increase
Solution Approach 1:
The patent implements dynamic power allocation where the supply point's power distribution is not fixed but adapts in real-time based on vehicle priorities, state of charge levels, and predicted usage patterns. The system dynamically adjusts charging rates and timing to maximize fleet readiness without requiring oversized infrastructure capacity.
Solution Approach 2:
The system performs preliminary actions by predicting future vehicle usage patterns and proactively scheduling recharging operations before vehicles are actually needed. This allows the supply point to manage peak loads more efficiently by pre-charging vehicles during off-peak periods, reducing the need for excessive power capacity during peak times.
2Quantity of substance
If the power of the supply point is reduced to lower costs, then installation and operating costs decrease, but the system becomes overloaded or cuts out during peak recharging demand
Solution Approach 1:
The patent implements periodic charging cycles that distribute power demand over time rather than concentrating it all at once. By cycling through vehicles in optimized sequences and adjusting charging rates periodically based on system load and vehicle needs, the system maintains reliability with reduced power capacity.
Solution Approach 2:
The system continuously monitors power consumption, vehicle state of charge, and system load, using this feedback to dynamically adjust charging operations. This closed-loop control prevents overloading by detecting approaching capacity limits and redistributing power accordingly, ensuring system stability even with reduced power capacity.
3Adaptability or versatility
If recharging is scheduled based on current state of charge only, then the scheduling is simple to implement, but it cannot adapt to varying user behavior patterns and future consumption needs
Solution Approach 1:
The system performs preliminary analysis of historical usage data to predict future vehicle consumption patterns and required state of charge levels. By pre-calculating these requirements based on learned user behaviors, the system can adapt to varying usage patterns without requiring complex real-time decision-making, maintaining relative simplicity while improving adaptability.
Solution Approach 2:
The system automatically learns and adapts to each vehicle's usage patterns through self-service mechanisms, analyzing historical data and adjusting scheduling without external intervention. This automated learning process enables adaptation to varying user behaviors while keeping the system architecture relatively simple, as the adaptation occurs through data-driven algorithms rather than complex control logic.
4Duration of action of moving object
If successive recharging of all vehicles is implemented, then each vehicle receives adequate charging time, but the total recharging period becomes too long for operational requirements
Solution Approach 1:
The patent implements dynamic scheduling that adjusts the sequence and duration of charging for different vehicles based on their individual needs, predicted usage patterns, and current state of charge. This dynamic approach allows simultaneous or near-simultaneous charging of multiple vehicles with optimized power distribution, dramatically reducing total fleet recharging time compared to strict successive charging.
Solution Approach 2:
The system performs preliminary assessment of each vehicle's charging requirements and predicted usage timing to optimize the charging schedule in advance. By pre-planning which vehicles need charging and when they will be needed, the system can coordinate charging operations to minimize total downtime while ensuring each vehicle receives adequate charge, avoiding the need for lengthy successive charging sequences.
Data Source
Figure 1
AI summary
The present invention relates to a method for the dynamic and predictive management of the electrical recharging of batteries (2), in which several vehicles (A, B, C, D), each belonging to a user, are equipped with at least one rechargeable battery (2); at the time of recharging each battery (2), each user and/or each vehicle (3) is identified by means of identification (6) at a single power supply point (4), and the battery (2) of each vehicle is electrically connected to said power supply point (4) for the purpose of recharging each battery (2). The method is characterized in that it consists of: - retrieving at least one data record relating to a previous recharging of each battery (2) in the form of a charging time (7) extending from a start time of recharging (70) to a stop time of recharging (71);- from the processing of said recorded data, determine a predicted charging time for each battery (2) from a predicted start time of power supply until a predicted end time of power supply; and - to determine a charging schedule (11) for said vehicles (A,B,C,D) by ordering the predicted charging times determined for each battery (2), said times (7) following one another and at least partially overlapping in time, by applying each predicted time to the charging of each vehicle (A,B,C,D), from its predicted start time of power supply which then becomes its actual start time of power supply (70), until its predicted end time of power supply.