Vehicle Battery Selection Using Longevity Prediction and Load Simulation
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Solution Overview
Problem
Current systems fail to adequately predict battery life and identify suitable batteries for evolving vehicle technologies, which are strained by advanced functionalities and environmental factors, leading to inconsistent performance and reduced battery life.
Innovation Solution
A system and method that evaluates and recommends batteries based on intended usage, environmental factors, and vehicle loads, using a battery longevity predictor and simulator to select the appropriate battery for a vehicle, considering factors like driving patterns, environmental conditions, and electrical load demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advanced vehicle functionalities (start-stop technology, autonomous steering, etc.) are added to increase vehicle capability, then vehicle functionality is improved, but battery strain increases and battery life decreases
Solution Approach 1:
The system performs preliminary assessment of battery health state and predicts remaining useful life before failure occurs. By evaluating multiple battery factors (temperature, voltage, current, age) and electrical load factors in advance, the system can proactively manage battery usage and recommend replacement timing, preventing unexpected failures while supporting advanced vehicle functionalities.
Solution Approach 2:
The system continuously monitors battery performance and provides feedback through longevity predictions and replacement recommendations. This closed-loop feedback mechanism allows the system to adapt battery management strategies based on actual usage patterns and environmental conditions, optimizing battery life while supporting evolving vehicle functionalities.
2Measurement precision
If comprehensive battery monitoring and prediction systems are implemented to improve battery life prediction, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments battery assessment into distinct components: battery factors (temperature, voltage, current, age) and electrical load factors (accessory usage, driving patterns). By dividing the complex prediction task into manageable segments, the system achieves high prediction accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system transforms complex battery degradation phenomena into quantifiable parameters and predictive metrics. By changing the representation of battery health from qualitative assessments to quantitative longevity predictions based on multiple measured parameters, the system achieves precise measurements without proportionally increasing system complexity.
Data Source
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AI summary
Disclosed is a vehicle comprising a vehicle system having a system having a number of loads defining a load profile; a validated battery comprising one or more batteries which can fulfill the load profile; an integrated battery selected from the validated battery, the integrated battery selected for longevity relative to other batteries; wherein the validated battery is provided within the vehicle. Further disclosed is a battery longevity predictor comprising a plurality of battery factors; a plurality of electrical load factors; a plurality of cycling or crank data; an output; wherein the output comprises a battery longevity predictor based on the plurality of battery factors, plurality of vehicle loads, and the plurality of cycling or crank data.