Vehicle Battery Charging Time Estimation Using Big Data Corrections
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Solution Overview
Problem
Existing methods for estimating vehicle battery charging time are inaccurate due to external factors such as charger power supply deviations and regional power supply and demand variations, leading to significant errors in estimated charging times.
Innovation Solution
A system and method utilizing a big data server to calculate an estimated charging time correction value based on charger information, location, and charging power type, which is applied to refine the initial estimated charging time using a charging controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple calculation method using battery parameters is used to estimate charging time, then the estimation process is fast and simple, but the accuracy of the estimated charging time deteriorates due to external factors
Solution Approach 1:
The patent introduces a big data server as an intermediary between the charging controller and the external charger. This server collects actual charging data from multiple sources, processes it to generate correction values, and provides these corrections to improve estimation accuracy without complicating the local estimation logic.
Solution Approach 2:
The system performs preliminary data collection and correction value calculation in advance through the big data server. By pre-processing charging data from multiple chargers and locations, the system prepares correction values before they are needed for actual charging time estimation, enabling accurate real-time predictions.
2Device complexity
If charging time estimation does not consider external factors, then the estimation system remains simple, but the reliability of the estimation deteriorates due to charger variations and regional power dynamics
Solution Approach 1:
The system implements a feedback mechanism where actual charging time data from multiple sources is continuously collected, compared with estimated values, and used to generate correction factors. These corrections are fed back into the estimation algorithm, creating a self-improving system that adapts to external variations.
Solution Approach 2:
The big data server performs multiple functions: collecting charging data, analyzing charger characteristics, determining regional power dynamics, calculating correction values, and providing recommendations. This multi-functional approach handles various external factors through a single centralized system.
3Measurement precision
If correction values from big data are applied to refine charging time estimates, then the accuracy of estimated charging time improves, but the system complexity increases due to data collection and processing requirements
Solution Approach 1:
The patent extracts the complex data processing functions from the local charging controller and places them in a centralized big data server. This separation allows the local device to remain simple while the server handles the computationally intensive tasks of data collection, analysis, and correction value generation.
Data Source
AI summary
A system for estimating a charging time of a battery includes: a big data server that receives charger information that is identification information of a connected external charger, information on an area where the external charger is located, or information on a type of charging power supplied from the external charger, along with a first estimated charging time and an actual charging time of the battery, calculates an error between the first estimated charging time and the actual charging time, and calculates an estimated charging time correction value according to the charger information and the error; and a charging controller that calculates a second estimated charging time based on a state of the battery, and receives the estimated charging time correction value from the big data server and applies the received estimated charging time correction value to the second estimated charging time to calculate the first estimated charging time.