EV Track Usage Monitoring for Range and Charge Planning
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Solution Overview
Problem
Battery electric vehicles face challenges in range limitations and charging time constraints, especially in remote areas and performance driving environments, due to limited charging options and thermal limitations of components, which affect their performance and efficiency during racing and off-road activities.
Innovation Solution
A system and method for adaptive prediction of electrified vehicle performance, which includes receiving goal parameters, past energy consumption data, and vehicle specifications to generate a future state of charge prediction, providing dynamic control alterations to optimize energy usage, recommend charging locations, and adjust vehicle settings for efficient trajectory and regenerative energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If battery electric vehicles are used in remote areas and performance driving environments, then vehicle performance and driving capability are improved, but range limitations and charging time constraints worsen due to limited charging options
Solution Approach 1:
The system performs preliminary analysis of the driving route to identify charging locations and calculate required charge times before the vehicle departs. This allows the driver to plan the journey with knowledge of available charging infrastructure, ensuring the vehicle can complete the trip within its range limitations while maintaining performance driving capability.
Solution Approach 2:
The system continuously monitors vehicle energy consumption, state of charge, and compares it against the planned route requirements. This feedback mechanism allows real-time adjustments to driving behavior or charging plans to ensure the vehicle maintains adequate range for performance driving while accounting for actual energy usage patterns.
2Duration of action of moving object
If charging time is extended to ensure adequate charge in remote areas, then range and driving capability are improved, but time constraints for performance driving and racing worsen
Solution Approach 1:
The system identifies optimal charging locations and calculates minimum required charge times before the vehicle arrives at the track or racing venue. This preliminary planning ensures that charging is performed efficiently during setup time rather than during the racing event, minimizing time loss to charging while ensuring adequate range for performance driving.
Solution Approach 2:
The system analyzes historical energy consumption data and vehicle performance parameters to optimize charging duration. By adjusting charging parameters based on actual usage patterns, the system minimizes required charge time while ensuring sufficient range for the intended performance driving activity.
3Reliability
If thermal limitations of battery and motor components are considered, then component reliability is improved, but performance and efficiency worsen due to derating
Solution Approach 1:
The system performs preliminary thermal analysis of the driving route and environmental conditions to predict battery and motor temperature behavior before the vehicle arrives. This allows the driver to adjust driving strategy or cooling system operation in advance to prevent thermal derating during performance driving, maintaining both reliability and performance.
Solution Approach 2:
The system continuously monitors actual battery and motor temperatures and compares them against thermal limits. This feedback enables real-time adjustments to driving behavior or thermal management system operation to maintain components within safe temperature ranges while maximizing performance output and avoiding unnecessary derating.
Data Source
AI summary
The disclosure is generally directed to systems and methods for adaptive prediction of electrified vehicle performance including receiving a set of goal parameters identifying a drivers performance requirements, receiving a set of fixed parameters related to course, vehicle and passenger status, receiving past energy consumption data for the electrified vehicle and the driver, generating an adaptive prediction of a future state of charge (SOC) of one or more electricity sources, and providing a dynamic control alteration based on the adaptive prediction, the dynamic control alteration as a function of the set of goal parameters. The adaptive prediction is based on the set of goal parameters, the set of fixed parameters and the past energy consumption data. The adaptive prediction includes updated parameters based on performance of the electrified vehicle.