EV State-of-Charge Prediction for V2L Power Usage
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Solution Overview
Problem
Electric vehicles face challenges in accurately estimating real-time discharging amounts and their impact on battery health when used for external power supply functions like V2L, making it difficult for users to predict when the battery will reach a minimum charging level, especially during camping or other power-intensive activities.
Innovation Solution
An electric vehicle state of charge control system and method that calculates predicted power consumption based on general and real consumption data of external devices, allowing users to estimate the time to reach a minimum charging level by setting available power information, sensing external temperature, and matching usage data with preset load power data to provide accurate predictions and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If V2L technology is introduced to display real-time discharging amount, then power supply capability to external devices is improved, but ability to estimate remaining usage time and battery impact is deteriorated
Solution Approach 1:
The system implements feedback by continuously monitoring actual power consumption of external devices and comparing it with predicted values. The controller adjusts future predictions based on the difference between actual and predicted consumption, creating a closed-loop system that improves estimation accuracy over time while maintaining real-time power supply capability.
Solution Approach 2:
The system performs preliminary action by predicting power consumption and remaining usage time before the battery actually reaches minimum charge level. The controller calculates predicted power consumption amounts and remaining usage times in advance, allowing users to plan their external device usage before power depletion occurs.
2Ease of operation
If battery power is used to drive external devices in V2L mode, then usability of external power supply is improved, but accuracy of predicting time to reach minimum charging amount is deteriorated
Solution Approach 1:
The system uses feedback by continuously monitoring actual power consumption and using this information to refine future predictions. The controller compares actual consumption with predicted consumption and adjusts subsequent predictions accordingly, improving measurement precision while maintaining ease of operation.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting prediction parameters based on actual operating conditions. The controller modifies power consumption predictions according to actual device usage patterns, temperature conditions, and battery state changes, thereby improving prediction accuracy without compromising usability.
3Measurement precision
If power consumption data of multiple external devices is collected and analyzed, then prediction accuracy is improved, but system complexity is increased
Solution Approach 1:
The system implements universality by using a single controller that performs multiple functions: monitoring power consumption, predicting remaining usage time, calculating battery impact, and providing user notifications. This multi-functional approach improves prediction accuracy without proportionally increasing system complexity.
Solution Approach 2:
The system applies self-service by automatically collecting, analyzing, and processing power consumption data without requiring user intervention. The controller autonomously monitors external device usage, calculates predictions, and provides notifications, reducing the operational complexity despite the sophisticated data processing required.
Data Source
AI summary
An embodiment state of charge (SOC) control method for an electric vehicle includes setting available power amount information based on power of a battery, receiving usage information on a usage device selected by a user from an external device and matching the usage information to preset load power data of a preset load power database, calculating predicted power amount information of the usage device based on the preset load power data and the available power amount information, and providing the predicted power amount to a user's device.