Vehicle Charging Synchronization System
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Solution Overview
Problem
Existing vehicle charging solutions fail to consider demand response events in the electricity grid and user historical driving patterns, leading to inefficient charging practices.
Innovation Solution
A system that determines synchronized arrival times and uses this data to estimate departure times, optimize charging schedules based on demand response requirements and user patterns, and assign charging time slots to minimize energy costs while ensuring vehicle readiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing charging solutions charge the vehicle as soon as it is connected to a power source, then the charging process is simple and quick to initiate, but it fails to consider demand response events and user driving patterns, leading to inefficient energy usage and suboptimal charging timing
Solution Approach 1:
The system performs preliminary actions by determining estimated departure times and analyzing demand response events before initiating charging. The server calculates when the vehicle will be needed and proactively schedules charging during optimal times that align with both user needs and grid conditions, rather than simply charging upon connection.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring demand response events, user driving patterns, and vehicle state of charge. This feedback loop enables the server to dynamically adjust charging schedules, selecting time slots that respond to real-time grid conditions while ensuring the vehicle is ready when needed.
2Loss of energy
If the system considers demand response events and user patterns to optimize charging schedules, then energy cost reduction and charging optimization are achieved, but the system complexity and computational requirements increase
Solution Approach 1:
The server acts as an intermediary between the vehicle charging system and the power grid. It receives demand response events from the grid operator, analyzes user driving patterns, and translates these inputs into optimized charging schedules. This intermediary role centralizes the computational complexity in a dedicated system rather than requiring complex logic in every charging device.
Solution Approach 2:
The system replaces manual or simple automatic charging decisions with an intelligent algorithmic approach. The server uses computational methods to analyze patterns, predict departure times, and optimize charging schedules based on demand response events, substituting complex mechanical or manual scheduling processes with software-based intelligence.
3Measurement precision
If the system determines synchronized arrival times and estimates departure times to optimize charging, then charging timing precision and user need satisfaction are improved, but the computational processing and data analysis requirements increase
Solution Approach 1:
The system performs preliminary analysis of user driving patterns and historical data to establish distribution patterns before actual charging events. By pre-processing this data and creating predictive models, the system reduces the computational burden during real-time charging decisions and can quickly determine optimal charging windows.
Data Source
AI summary
The disclosure includes a system and method for performing one or more vehicle functions associated with a vehicle. The system includes a processor and a memory storing instructions that, when executed, cause the system to: receive sensor data indicating that a vehicle has arrived at a destination location; determine a synchronized arrival time describing when the vehicle arrived at the destination location, the synchronized arrival time determined based in part on the sensor data; and determine one or more vehicle functions associated with the vehicle based in part on the synchronized arrival time.