EV Battery Charging Management Using Travel Data
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Solution Overview
Problem
The high cost and short lifespan of batteries in two-wheeled electric vehicles lead to frequent replacements, resulting in elevated operational expenses due to inadequate charging management.
Innovation Solution
A method and apparatus for charging management that estimates the depth of discharge and degree of ageing of a battery using historical travel data, determining an optimal state of charge range and charging the battery to a maximum state of charge, thereby reducing battery ageing and prolonging its lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the battery is charged to full capacity frequently, then the energy availability for travel is improved, but the battery ageing accelerates and lifespan decreases
Solution Approach 1:
The system performs preliminary analysis of historical travel data and future travel plans to determine the optimal charging strategy in advance. By predicting future energy needs before charging occurs, the system can charge to the minimum necessary capacity rather than full capacity, thereby reducing unnecessary charging cycles and extending battery lifespan while ensuring sufficient energy availability when needed.
Solution Approach 2:
The charging strategy is made dynamic by continuously adjusting the target SOC based on real-time factors including historical travel patterns, predicted future trips, current battery health status, and environmental conditions. This dynamic adjustment allows the system to optimize the balance between energy availability and battery preservation on each charging occasion, avoiding both overcharging and unnecessary full charges.
2Reliability
If the battery is charged to maximum SOC always, then the energy sufficiency is improved, but the battery ageing increases and replacement frequency increases
Solution Approach 1:
The system analyzes historical travel data and predicts future travel requirements before charging occurs. This preliminary analysis enables the system to determine the precise charging level needed for upcoming trips, avoiding unnecessary charging to maximum SOC and thereby reducing battery ageing and replacement frequency while maintaining energy sufficiency for predicted travel needs.
Solution Approach 2:
The system continuously monitors actual travel patterns against predictions and uses this feedback to refine future charging decisions. By comparing predicted versus actual energy consumption and adjusting the charging strategy accordingly, the system maintains reliable energy sufficiency while optimizing charging levels to minimize battery ageing and operational costs over time.
3Ease of operation
If simple charging strategies are used, then the ease of operation is improved, but the battery lifespan is not optimized
Solution Approach 1:
The system enables the battery management to serve itself by automatically analyzing travel data, predicting energy needs, and determining optimal charging strategies without requiring user intervention. The user simply connects the battery for charging, and the system autonomously manages the charging process based on learned travel patterns and battery health status, thereby maintaining ease of operation while optimizing battery lifespan.
Solution Approach 2:
The system replaces manual charging decision-making with an automated intelligent algorithm that processes travel data and battery status information. This substitution of mechanical/user-based charging decisions with an electronic intelligent system maintains simplicity for the user while implementing complex optimization logic that extends battery lifespan through scientifically determined charging strategies.
Data Source
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AI summary
The present invention relates to a method and apparatus for charging management, the method comprising: using travel data of an electric vehicle of all historical days, including a current day, prior to a second day to estimate a possible depth of discharge (DOD) of a battery supplying power to the electric vehicle on the second day and a degree of ageing of the battery as of the current day, wherein the travel data indicates the variation with time of a travel speed of the electric vehicle on all the historical days; determining an optimal state of charge (SOC) range of the battery corresponding to the estimated degree of ageing, on the basis of information indicating optimal SOC ranges of the battery corresponding to different degrees of ageing of the battery; determining a maximum SOC of the battery when used on the second day, on the basis of the estimated possible DOD and the determined optimal SOC range; and charging the battery to the maximum SOC, when the battery can be charged for use on the second day. The method and apparatus can prolong the life of the battery supplying power to the electric vehicle.