Vehicle Battery Selection Based on Load and Longevity Prediction
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Solution Overview
Problem
Current systems fail to adequately predict battery life and identify suitable batteries for vehicles with evolving functionalities and strain requirements, leading to potential battery life impacts.
Innovation Solution
A system and method for evaluating and recommending batteries based on intended usage cases, environmental factors, and quantitative data, using a battery longevity predictor that considers battery factors, vehicle loads, and driver patterns.
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 segmented into multiple independent modules: vehicle functionality analysis module, electrical load calculation module, environment factor assessment module, driver behavior analysis module, battery simulation module, and recommendation module. Each module processes specific aspects independently, allowing complex predictions to be broken down into manageable components that can be developed, tested, and maintained separately while contributing to overall prediction accuracy.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing vehicle functionality data, electrical load patterns, environment conditions, and driver behavior before battery selection or replacement. Battery simulation is conducted in advance to predict longevity under specific usage conditions, allowing the system to recommend batteries optimized for the vehicle's actual operational profile rather than using generic selection criteria.
2Reliability
If a comprehensive battery evaluation system is implemented, then battery recommendation accuracy improves, but computational time and data processing requirements increase
Solution Approach 1:
The system performs preliminary battery simulation and longevity prediction before final recommendation, using the collected vehicle data to pre-assess battery performance under expected usage conditions. This advance computation allows the system to have prediction results ready when battery selection or replacement is needed, reducing actual decision-making time while maintaining comprehensive analysis.
Solution Approach 2:
The system implements a tiered evaluation approach where essential factors (vehicle functionality, electrical loads) are always analyzed, while additional factors (detailed driver behavior patterns, specific environment conditions) are processed based on data availability and prediction confidence requirements. This allows the system to balance computational thoroughness with practical time constraints.
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.