EV Charging Power Scheduling With Temperature-Responsive Ventilation
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Solution Overview
Problem
Existing charging systems for electric vehicles struggle to accurately predict and schedule charging demand, leading to congestion and long waiting times due to limitations in charging station capacity and distribution.
Innovation Solution
A charging scheduling system that utilizes a transmission module to obtain historical usage features from vehicles, a monitoring module for ambient temperature, and a processor to generate scheduling instructions adjusting charging power and ventilation based on these features, ensuring safe and efficient charging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If charging power is increased to meet growing demand, then charging capacity is improved, but heat accumulation and safety risks worsen
Solution Approach 1:
The charging system dynamically adjusts charging power based on real-time temperature monitoring and battery state of charge. The processor modulates power delivery between series and parallel configurations, reducing power when temperature thresholds are approached while maintaining optimal charging efficiency during cooler periods.
Solution Approach 2:
The system implements continuous feedback loops where temperature sensors monitor battery and ambient conditions, and this data feeds back to the processor which adjusts charging power accordingly. The ventilation system also responds to temperature feedback by activating fans or HVAC when thresholds are exceeded.
2Loss of time
If more charging stations are deployed to reduce waiting times, then charging accessibility is improved, but system complexity and cost worsen
Solution Approach 1:
The charging station is designed as a multi-functional integrated system that combines charging, temperature monitoring, ventilation, and scheduling capabilities in one unit. This universal design allows a single station to serve multiple functions rather than requiring separate systems for each function.
Solution Approach 2:
The system automatically schedules and manages charging tasks without requiring external complex control systems. The processor autonomously optimizes charging sequences, monitors conditions, and adjusts power delivery based on real-time data, making the station self-managing and reducing overall system complexity.
3Productivity
If charging power is optimized based on battery capacity, then charging efficiency is improved, but system complexity worsens
Solution Approach 1:
The system performs preliminary actions by obtaining battery basic information and historical usage features before initiating charging. The processor pre-calculates optimal charging parameters based on battery capacity, age, and usage patterns, preparing charging schedules in advance to maximize efficiency while simplifying real-time control.
Solution Approach 2:
The system optimizes charging by dynamically changing key parameters such as charging current, voltage, and power levels based on battery state. The processor adjusts these parameters according to battery capacity, temperature, and charge level, enabling efficient charging through parameter optimization rather than complex hardware modifications.
Data Source
AI summary
The present disclosure provides a charging scheduling method based on a usage feature of an electric vehicle, comprising obtaining a charging feature of a vehicle to be charged based on historical usage features; generating a scheduling instruction based on an ambient temperature and the charging feature, and sending the scheduling instruction to a charging module; the scheduling instruction being configured to adjust a series-parallel state of a pulse transformer in the charging module, to adjust charging power of the charging module; and generating a heat dissipation instruction and sending the heat dissipation instruction to a ventilation module in response to the ambient temperature satisfying a preset temperature condition.