EV Charging Control With Dynamic Module Sharing and Vehicle ID
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Solution Overview
Problem
Existing electric vehicle charging systems lack flexibility in power sharing, leading to inefficient charging due to static allocation of charging modules, and fail to analyze charging data for personalized charging methods.
Innovation Solution
A big data-based central control charging system that collects and analyzes state-of-charge data to dynamically allocate charging modules and identify vehicle models, enabling flexible power sharing and customized charging methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individual dispensers determine charging modules based on immediate usage state, then local autonomy is maintained, but charging efficiency deteriorates due to lack of flexible power sharing
Solution Approach 1:
The server acts as an intermediary between multiple dispensers, collecting charging information from each dispenser and electric vehicles, analyzing the data to determine optimal charging module allocation, and distributing control decisions back to dispensers. This mediator approach enables centralized optimization of power sharing while maintaining the operational structure of individual dispensers.
2Device complexity
If charging modules are allocated statically to dispensers, then device complexity is reduced, but adaptability deteriorates when charging states change
Solution Approach 1:
The system implements dynamic allocation of charging modules where the server continuously monitors charging states of dispensers and electric vehicles, and reassigns charging modules in real-time based on current needs. This dynamic approach allows the system to adapt to changing charging states without requiring complex manual reconfiguration at each dispenser.
Solution Approach 2:
The server analyzes charging parameters such as state of charge, charging speed requirements, and dispenser capacity to dynamically adjust the allocation of charging modules. By changing these operational parameters centrally, the system achieves flexible power sharing while maintaining relatively simple individual dispenser structures.
3Device complexity
If server only collects charging information without analysis, then device complexity is minimized, but loss of information occurs regarding charging patterns and vehicle characteristics
Solution Approach 1:
The server collects charging information from dispensers and electric vehicles, analyzes this data to identify charging patterns and vehicle characteristics, and uses this feedback to optimize future charging module allocations. The analyzed information feeds back into the allocation decisions, creating a continuous improvement loop that enhances charging efficiency while maintaining manageable server complexity.
Data Source
AI summary
A big data based central control charging system according to the present includes an electric vehicle charging unit which charges an electric vehicle using a plurality of charging modules; and a central control unit which collects state-of-charge data by performing the communication with the electric vehicle, by means of the electric vehicle charging unit, the central control unit converts state-of-charge data received from the electric vehicle, by means of the electric vehicle charging unit into big data, extracts a feature item for every electric vehicle model and a data value of the feature item by analyzing and learning the big data of the state-of-charge data, builds or updates a vehicle model identification model for identifying the electric vehicle model based on the extracted feature item and the feature item data value.