PredictEV Tool Optimizes EV Charging Station Deployment

Solution Overview

Problem

Current approaches for deploying electric vehicle charging station (EVCS) infrastructure do not address the growing demand for EVs in an integrated and holistic manner, failing to consider factors like EV adoption patterns, regional demographics, and point of interest visitation, leading to inefficient charging infrastructure planning.

Innovation Solution

A machine-learning based software tool, PredictEV, predicts EV adoption and demand, providing recommendations for EVCS deployment by using a Generalized Bass diffusion model, mobility simulation, and inference engine to determine optimal charger types and quantities, considering factors like driver classification, visitation patterns, and environmental impact.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If EV charging infrastructure is deployed without integrated demand estimation, then deployment can proceed quickly, but the infrastructure planning becomes inefficient and fails to meet actual charging needs

Engineering Contradiction:

Improvecharging infrastructure planning efficiencyVSAvoidEV adoption patterns, regional demographics, visitation data

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system performs preliminary demand estimation by integrating EV adoption patterns, regional demographics, and point of interest visitation data before deploying charging infrastructure. This advance planning ensures that infrastructure deployment is optimized to meet actual future charging needs rather than reacting to current demand alone.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously collects and analyzes data on EV adoption patterns, charging behavior, and point of interest visitation to refine demand estimates. This feedback loop enables dynamic adjustment of infrastructure deployment strategies to align with evolving charging needs and usage patterns.

Inventive Principle:

Principle #23Feedback

2Loss of time

If charging infrastructure is deployed without demand prediction, then deployment decisions can be made quickly, but waiting times for charging increase and driver satisfaction decreases

Engineering Contradiction:

Improvecharging waiting timeVSAvoiddemand prediction system complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The system performs preliminary demand estimation by integrating EV adoption patterns, regional demographics, and point of interest visitation data before deploying charging infrastructure. This advance planning ensures that infrastructure deployment is optimized to meet actual future charging needs rather than reacting to current demand alone.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses machine learning models to analyze multiple parameters including EV adoption rates, regional demographics, and point of interest visitation patterns to predict future charging demand. By changing and optimizing these input parameters, the system can accurately forecast demand and position charging stations to minimize waiting times.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If EV charging infrastructure is deployed without holistic planning, then individual charging stations can be installed quickly, but overall infrastructure efficiency and sustainability are compromised

Engineering Contradiction:

Improvecharging infrastructure sustainabilityVSAvoidinfrastructure planning complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system serves multiple functions simultaneously: it estimates EV adoption patterns, predicts charging demand, analyzes regional demographics, evaluates point of interest visitation data, and optimizes charging station placement. This multi-functional approach ensures comprehensive and sustainable infrastructure planning that considers all relevant factors.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system continuously collects and analyzes data on EV adoption patterns, charging behavior, and point of interest visitation to refine demand estimates. This feedback loop enables dynamic adjustment of infrastructure deployment strategies to align with evolving charging needs and usage patterns.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20220332209A1System and method for estimating the optimal number and mixture of types of electric vehicle charging stations at one or more points of interest

Publication Date: 2022.10.20 VOLTA CHARGING LLC

AI summary

An approach is provided for estimating the optimal number and mixture of types of electric vehicle (EV) charging stations (EVCS) at one or more points of interest (POIs). A method includes generating, based on an EV adoption model, an EV adoption prediction. The method includes generating, based on a mobility simulation model, a driver-type prediction that predicts percentages of EV drivers qualifying for various EV driver types. The method includes generating, based on the EV adoption prediction, the driver-type prediction, and a visitation model, a visitation prediction that predicts how many EV drivers of each type of EV driver will visit the POI. The method includes determining and displaying, based on how many EV drivers of each type of EV driver is predicted to visit the POI, for each type of EV charging station of a plurality of types of EV charging stations, an optimal number of EVCS to install.