Vehicle Battery Selection Using Load Profiles and Longevity Prediction
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Solution Overview
Problem
Current systems are inadequate in predicting battery life and identifying suitable batteries for evolving vehicle technologies and functionalities, which can lead to strain on batteries and impact their longevity.
Innovation Solution
A system and method for evaluating and recommending the best battery for a particular vehicle based on intended usage, environmental factors, and quantitative data from a model, considering factors such as battery factors, vehicle loads, and cycling data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current battery selection systems are used, then battery selection is simple and quick, but battery life prediction accuracy is insufficient and suitable battery identification is inadequate
Solution Approach 1:
The battery selection system is divided into multiple independent modules: vehicle usage analysis module, environmental factor module, battery simulation module, and recommendation module. Each module processes specific aspects independently, allowing the system to achieve high prediction accuracy through comprehensive multi-factor analysis without becoming unmanageably complex as a whole.
Solution Approach 2:
The system performs preliminary battery simulation and performance evaluation before actual battery selection. By pre-calculating battery life predictions based on various usage scenarios and environmental factors, the system prepares comprehensive data in advance, enabling accurate battery identification when needed without requiring complex real-time analysis.
2Reliability
If comprehensive usage cases and environmental factors are considered, then battery selection accuracy improves, but evaluation time and computational resources increase
Solution Approach 1:
The system pre-processes and stores usage case templates and environmental factor data before actual battery selection. By preparing reference data and simulation models in advance, the system can quickly evaluate batteries by comparing against pre-established criteria rather than performing complete simulations from scratch, reducing evaluation time while maintaining comprehensive analysis.
Solution Approach 2:
The system dynamically adjusts the level of analysis depth based on available time and computational resources. For routine selections, it uses simplified parameter comparisons, while for critical applications, it performs comprehensive multi-factor simulations. This allows the system to balance accuracy requirements with time constraints by adapting the evaluation thoroughness to specific needs.
Data Source
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.