EV Charging Planner Using Travel Routine Prediction
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Solution Overview
Problem
Electric vehicle operators face challenges in balancing charging efficiency and cost, as they often charge to maximum capacity without considering cheaper alternatives, and may encounter backlogs or inefficiencies at charging stations due to lack of awareness about available options.
Innovation Solution
A computer-implemented method and system that analyzes an electric vehicle operator's daily activities, travel routines, and geo-location to provide a user interface presenting charging options, including nearby charging stations and alternative electric vehicles, to optimize charging based on cost and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If operators charge to maximum state of charge at one or more charging speeds, then the electric vehicle battery is fully replenished, but the charging cost increases and charging efficiency decreases
Solution Approach 1:
The system performs preliminary actions by analyzing operator travel routines and predicting future travel plans before charging decisions are made. It proactively identifies optimal charging opportunities along predicted travel paths, allowing operators to charge at cost-effective times and locations before needing to travel, thus avoiding both high costs and inefficiency
Solution Approach 2:
The system introduces an intermediary intelligence layer between the operator and charging infrastructure. This intermediary analyzes multiple data sources (travel routines, charging station availability, cost structures) and mediates charging decisions by presenting optimized charging options that balance cost and efficiency, rather than leaving operators to make binary charging decisions
2Ease of operation
If operators arrive at a charging station, then charging can be performed, but wait time increases due to backlog of additional customers
Solution Approach 1:
The system performs preliminary actions by predicting operator travel plans and identifying optimal charging stops along the path before the operator arrives. By proactively scheduling charging at less congested stations based on predicted demand patterns, the system reduces wait times while ensuring charging accessibility when needed
Solution Approach 2:
The system incorporates feedback from real-time charging station status data and historical charging patterns to dynamically adjust charging recommendations. This feedback mechanism allows the system to identify stations with lower current demand and recommend them to operators, thereby reducing wait times while maintaining charging accessibility
3Reliability
If operators use charging equipment at residential home or public charging stations, then the battery is replenished, but awareness of cheaper alternate locations is lost
Solution Approach 1:
The system introduces an intermediary intelligence layer between the operator and charging infrastructure. This intermediary analyzes multiple data sources (travel routines, charging station availability, cost structures) and mediates charging decisions by presenting optimized charging options that balance cost and efficiency, rather than leaving operators to make binary charging decisions
Solution Approach 2:
The system replaces the manual information-gathering and decision-making process with an automated computational system. Instead of operators needing to manually research and compare charging locations, the system automatically processes travel data, charging infrastructure data, and cost information to substitute human decision-making with algorithmic optimization
Data Source
AI summary
A system and method for providing charging options based on electric vehicle operator daily activities that include analyzing data associated with daily activities of an operator of an electric vehicle and determining at least one travel routine and at least one prospective travel plan that is completed by the operator of the electric vehicle based on the daily activities. The system and method also include determining a current geo-location of the electric vehicle. The system and method further include presenting an electric vehicle charging planner user interface that presents at least one prospective travel path to the at least one predicted point of interest.