EV Battery Preconditioning Using Destination and Charging Propensity
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Solution Overview
Problem
Existing EV charging systems face inefficiencies in determining when to initiate preconditioning of the battery pack for fast charging, leading to suboptimal charging times and potential battery damage due to incorrect predictions.
Innovation Solution
A system that uses a processor and memory to predict the vehicle's destination and user's charging behavior, determining a confidence score to decide whether to schedule battery pack preconditioning, ensuring the battery is at an optimal temperature for fast charging, based on location data, user propensity, and charging station availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system initiates battery pack preconditioning based on simple triggers (e.g., low battery capacity), then the battery can be prepared for fast charging, but the system may incorrectly predict charging needs leading to suboptimal charging times and potential battery damage
Solution Approach 1:
The system performs preliminary actions by collecting location data samples and predicting destination before the actual charging event occurs. The machine learning model analyzes travel patterns and destination predictions in advance to determine whether preconditioning should be initiated, allowing the system to prepare the battery optimally before fast charging begins.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring user charging behavior, location data, and charging station availability to refine its predictions. The machine learning model learns from historical charging patterns and adjusts its predictions accordingly, improving accuracy over time while adapting to changing user preferences and travel patterns.
2Measurement precision
If the system collects and analyzes multiple data points (location data, user behavior, charging station availability), then the accuracy of preconditioning prediction improves, but the processing time and computational resources increase
Solution Approach 1:
The system collects location data samples continuously during travel and performs preliminary analysis to predict destination before the charging decision needs to be made. By gathering and processing data in advance, the system reduces the time pressure during the actual charging event while maintaining high prediction accuracy through multiple data points.
3Productivity
If the system schedules preconditioning based on predicted destination and user propensity, then recharging time is reduced, but the system may unnecessarily heat the battery when charging is not needed
Solution Approach 1:
The system uses feedback from user charging behavior, charging station availability, and destination prediction confidence scores to make informed decisions about preconditioning. By continuously learning from actual charging events and comparing predictions with real outcomes, the system refines its model to reduce false positives and avoid unnecessary preconditioning while maintaining high accuracy for genuine charging opportunities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces recharging time, maximizes energy throughput during fast charging, and prolongs battery life by accurately anticipating and optimizing the preconditioning process.
Implementation Method 1
The preconditioning of the battery pack of the vehicle may deliberately increases the temperature of the battery pack
Data Source
AI summary
A system preconditions a battery pack of a vehicle to support fast charging. The system detects a trigger that that indicates the vehicle will be traveling or the battery pack of the vehicle has been reduced to a predetermined capacity. The system collects a plurality of samples of location data of the vehicle. The system predicts a destination of the vehicle based on the samples. The system determines a propensity of a user to charge the vehicle based on previous charging behavior of the user. The system determines a confidence score of the predicted destination and the determined propensity. The system determines whether to schedule preconditioning of the battery pack based on the confidence score meeting a threshold.