EV Fast Charging Station Planning on Expressways

Solution Overview

Problem

Current charging station planning methods are incomplete and unsystematic, failing to accurately determine the locations and capacities of electric vehicle (EV) fast charging stations on expressways due to uncertainties in EV characteristics and traffic behavior, leading to inefficiencies in infrastructure costs and user waiting times.

Innovation Solution

A planning method that forecasts the spatial and temporal distribution of EV charging load, uses the nearest neighbor clustering algorithm to determine the locations of fast charging stations, and applies queuing theory to determine the number of chargers in each station, considering EV types, traffic patterns, and travel ranges.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If the number of charging stations is roughly estimated based on EV maximum travel range and traffic network, then the planning process is simple, but the location siting and capacity sizing are inaccurate

Engineering Contradiction:

Improveplanning process complexityVSAvoidlocation siting and capacity sizing accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent performs preliminary forecasting of spatial and temporal distribution of EV charging load before determining station locations and capacities. This preliminary analysis of EV travel behaviors, battery capacities, and charging demands enables more accurate location siting and capacity sizing decisions to be made in advance, rather than relying on rough estimates.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent incorporates feedback mechanisms by using forecasted charging load distribution to iteratively optimize station locations and capacities. The system continuously refines the planning based on predicted EV travel patterns, charging demands, and service coverage feedback, improving the accuracy of location siting and capacity sizing.

Inventive Principle:

Principle #23Feedback

2Device complexity

If fast charging stations are planned without considering EV characteristics and traffic behavior uncertainty, then the planning is straightforward, but the total cost including infrastructure and user waiting cost is not minimized

Engineering Contradiction:

Improveplanning model complexityVSAvoidtotal cost optimization

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent changes key parameters by incorporating EV-specific characteristics (battery capacity, charging power requirements, travel range) and traffic behavior parameters (origin-destination patterns, travel times, charging demands) into the planning model. This enables the system to optimize station locations and capacities to minimize total cost while accounting for the uncertainty and variability in EV usage patterns.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent performs preliminary forecasting of spatial and temporal distribution of EV charging load before determining station locations and capacities. This preliminary analysis of EV travel behaviors, battery capacities, and charging demands enables more accurate location siting and capacity sizing decisions to be made in advance, rather than relying on rough estimates.

Inventive Principle:

Principle #10Preliminary action

3Device complexity

If charging station locations are determined without forecasting spatial and temporal distribution of charging load, then the planning is simpler, but the service coverage and user satisfaction are reduced

Engineering Contradiction:

Improveforecasting model complexityVSAvoidcharging service accessibility

Core Design Contradiction:

Device complexityVSEase of operation

Solution Approach 1:

The patent performs preliminary forecasting of spatial and temporal distribution of EV charging load before determining station locations and capacities. This preliminary analysis of EV travel behaviors, battery capacities, and charging demands enables more accurate location siting and capacity sizing decisions to be made in advance, rather than relying on rough estimates.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies local quality by determining charging station locations and capacities based on local charging demand characteristics. The forecasted spatial and temporal distribution of charging load enables the system to tailor station configurations to specific locations with high charging demands, improving service coverage and user satisfaction in areas where EV users need charging services most.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS10360519B2Planning method of electric vehicle fast charging stations on the expressway

Publication Date: 2019.07.23 TIANJIN UNIV

AI summary

A planning method of EV fast charging stations on the expressway, comprising the following steps: Step 1: forecasting the spatial and temporal distribution of EV charging load, that is to determine the time and location of each EV needing charging on the expressway; Step 2: based on the forecast result achieved by Step 1, determining the locations of the fast charging stations on the expressway by the nearest neighbor clustering algorithm; Step 3: according to the spatial and temporal forecast result of the EV charging load and the locations of the fast charging stations, determining the number c of the chargers in each fast charging station by queuing theory. Due to battery characteristics, traditional gas stations do not completely match the fast charging stations. In the planning method, the locations and times for charging of the EVs are considered to determine the capacities and locations of the fast charging stations, which can meet the charging needs of the EVs more than the traditional gas station, and thus promote the development of EVs.