Vehicle Computing System for Energy Conservation Actions
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Solution Overview
Problem
Battery electric vehicles (BEVs) face challenges due to the time-consuming nature of recharging and limited recharging infrastructure, leading to concerns about running out of power, especially in situations where drivers are away from charging stations.
Innovation Solution
A computer-implemented method that utilizes a vehicle computing system to analyze and provide personalized energy conservation actions based on received data, including driver-specific profiles and environmental factors, to optimize energy usage and extend the vehicle's range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If battery electric vehicles are used, then environmental friendliness and operating costs are improved, but recharging time and infrastructure availability worsen
Solution Approach 1:
The system performs preliminary actions by predicting future energy requirements based on historical driving patterns, weather forecasts, and scheduled events. It proactively schedules charging during off-peak hours or when renewable energy availability is high, before the vehicle actually needs the energy, thereby avoiding last-minute charging delays.
Solution Approach 2:
The system continuously monitors actual energy consumption against predictions and uses this feedback to refine future forecasts. It also monitors charging infrastructure availability and adjusts charging schedules based on real-time conditions, creating a closed-loop system that adapts to changing circumstances and improves accuracy over time.
2Loss of energy
If battery electric vehicles are used, then operating costs are reduced, but range anxiety and infrastructure limitations worsen
Solution Approach 1:
The system enables the vehicle to serve itself by automatically making intelligent charging decisions without requiring constant user intervention. It autonomously monitors energy levels, predicts consumption patterns, and schedules charging operations based on user preferences and environmental conditions, freeing the user from manual management while ensuring reliable power availability.
Solution Approach 2:
The system dynamically adjusts charging parameters such as charging rate, timing, and duration based on real-time conditions including battery state of charge, grid electricity prices, renewable energy availability, and predicted driving needs. This flexible parameter adjustment optimizes both cost efficiency and reliability adaptively.
3Use of energy by moving object
If personalized energy conservation actions are implemented, then energy efficiency is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system segments the complex energy optimization problem into distinct functional modules: data collection from multiple sensors, historical pattern analysis, predictive modeling, charging schedule generation, and user interface management. Each module handles a specific aspect of the problem independently, making the overall system more manageable and maintainable despite its comprehensive capabilities.
Data Source
AI summary
A computer-implemented method includes receiving a request for improving vehicular energy conservation. The method further includes responsively providing at least one action to a vehicle computing system, based at least in part on received vehicle data. Also, the method includes receiving data relating to a change in vehicle energy efficiency resulting from implementation of the action. The method additionally includes analyzing the received data and action along with data resulting from similar actions to determine a generalized effect of the action.