EV Charging Control via Cost Function Optimization
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Solution Overview
Problem
Existing methods for controlling the charging of electric vehicles do not effectively manage overcapacities in the electricity grid, as they do not consider time periods of overload or overcapacity, and can lead to significant degradation of vehicle energy stores during charging.
Innovation Solution
A method where a central control system communicates with vehicles and the electricity grid server to distribute charging operations based on a cost function that assigns different cost values to various time periods, prioritizing charging within the grid's charging time window and delaying it to avoid peak usage, thereby reducing overcapacities and minimizing energy store degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If charging operations are prioritized during periods of overcapacity in the electricity grid, then overcapacities can be efficiently reduced, but vehicle energy stores may be subjected to significant degradation
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting charging parameters (charging power, charging rate) based on the vehicle's state of charge, age, and environmental conditions. The control system modifies charging parameters in real-time to optimize the balance between utilizing grid overcapacity and minimizing energy store degradation, thereby resolving the contradiction between productivity and reliability.
Solution Approach 2:
The patent implements dynamics by making the charging control system adaptive and flexible. The control system continuously monitors grid conditions, vehicle state, and environmental factors, dynamically adjusting charging operations accordingly. This dynamic approach allows the system to respond to changing conditions and optimize the trade-off between overcapacity reduction and energy store protection.
2Reliability
If charging is delayed to avoid peak usage periods, then energy store degradation is minimized, but overcapacities in the electricity grid cannot be efficiently utilized
Solution Approach 1:
The patent applies feedback by implementing a closed-loop control system that continuously monitors grid conditions, vehicle state, and charging progress. The control system uses this feedback information to make real-time decisions about charging operations, adjusting charging power and timing based on current conditions. This feedback mechanism enables the system to efficiently utilize overcapacities while protecting energy stores from excessive degradation.
Solution Approach 2:
The patent implements preliminary action by performing predictive analysis of grid conditions and vehicle requirements. The control system forecasts future overcapacity periods and plans charging operations in advance, preparing optimal charging schedules that balance grid utilization with energy store protection. This preliminary planning allows the system to proactively capture overcapacities while minimizing degradation risks.
3Ease of operation
If charging time windows are strictly enforced for vehicles, then user requirements are met, but flexibility in responding to grid overcapacities is reduced
Solution Approach 1:
The patent applies dynamics by transforming static charging time windows into flexible, adaptive time ranges. The control system allows charging windows to be adjusted dynamically based on grid conditions, vehicle state, and user preferences. This dynamic flexibility enables the system to respond to grid overcapacities while still meeting user requirements, as charging can be scheduled within an adaptable time range rather than a fixed window.
Solution Approach 2:
The patent implements parameter changes by modifying charging window parameters (start time, end time, duration) based on real-time conditions. The control system adjusts these parameters to optimize the balance between user requirements and grid utilization. Users can specify preference ranges, and the system dynamically selects optimal charging times within these ranges, maintaining ease of operation while improving grid response flexibility.
Data Source
AI summary
A method controls electrical charging of a group of vehicles, wherein the respective vehicles are connected to a power supply system for charging a vehicle energy store for driving the particular vehicle, wherein a central control system communicates with the respective vehicles in the group and with a server belonging to the power supply system operator. On the basis of a received charging command from the server which specifies a charging period, the central control system selects a number of vehicles from the group which are intended to be charged. A charging time window, a departure time and a desired state of charge of the vehicle energy store at the departure time are each stipulated in advance for one or more specific vehicles of the number of vehicles, wherein the duration of the charging operation until the desired state of charge is reached is estimated for a particular specific vehicle and the particular specific vehicle distributes the charging operation, according to a cost function which stipulates different cost values for different time periods before the departure time, to the time periods in ascending order of the cost values. The cost function for a particular specific vehicle is stipulated by the central control system on the basis of first to seventh cost values such that charging is preferably carried out in the charging time window and the charging period and at a later time.