EV Charging Station Congestion Estimation via Fleet Data
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Solution Overview
Problem
Conventional electric vehicle charging systems do not account for the movement of other vehicles, leading to potential waiting times when a charge station is occupied by another electric vehicle, thus failing to facilitate rapid charging.
Innovation Solution
An information providing system that includes an in-vehicle device and an information center, which estimate the congested state of a charge station based on the position and state-of-charge of both the own electric vehicle and other vehicles around the station, providing this information to the driver to select less congested stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the path search is implemented based on reachable charge stations using own vehicle's state-of-charge, then the navigation capability is improved, but the waiting time at charge station increases due to occupation by other vehicles
Solution Approach 1:
The system performs preliminary estimation of charge station congestion by analyzing other vehicles' movement patterns and charging behaviors before the own vehicle arrives. This advance information allows the driver to select charge stations with lower expected waiting times, thereby resolving the contradiction between navigation capability and waiting time.
Solution Approach 2:
The system collects position information and state-of-charge data from other vehicles, processes this feedback information to estimate congestion levels, and provides this estimation to the driver for route planning. This feedback loop enables dynamic route optimization that accounts for real-time charge station occupancy patterns.
2Device complexity
If the charge station selection is made without considering other vehicles' movement, then the system complexity is reduced, but the charging efficiency deteriorates due to unexpected waiting
Solution Approach 1:
Other vehicles effectively provide the service of congestion information by transmitting their position and state-of-charge data. The system utilizes this self-generated data from the vehicle fleet to estimate charge station congestion, achieving improved charging efficiency without requiring complex external monitoring infrastructure.
Solution Approach 2:
The system uses an intermediary estimation model that processes position information and state-of-charge data from multiple vehicles to predict charge station congestion. This intermediary layer translates raw vehicle data into actionable congestion estimates, enabling efficient charge station selection with moderate system complexity.
Data Source
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AI summary
An information providing system (1) for providing a passenger with information includes an in-vehicle device (30) installed in an electric vehicle (EVn), wherein the passenger is provided with the information via the in-vehicle device (30). The in-vehicle device (30) includes a notifier (38) for notifying the passenger of a congested state of a charge station candidate (CS1). The notifying is implemented by estimating (42) of the congested state of the charge station candidate (CS1) at a time point when an own electric vehicle (EV1) reaches the charge station candidate (CS1) which is a charge station (CS) reachable with a state-of-charge (SOC) of the own electric vehicle (EV1), and the estimating (42) is implemented based on: position information and the state-of-charge (SOC) of the own electric vehicle (EV1), and position information and a state-of-charge (SOC) of another electric vehicle (EV2-EVn) which is present around the charge station candidate (CS1).