Eco-Routing Controller for Electric Vehicle Range Extension
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Solution Overview
Problem
Conventional route planning systems for vehicles do not account for energy efficiency, leading to suboptimal routes that may result in insufficient range or increased fuel consumption, especially in electric and hybrid vehicles.
Innovation Solution
Intelligent vehicle systems with real-time eco-routing and adaptive driving control capabilities that use predictive algorithms to optimize energy usage by suggesting driver behavior changes and on-demand charging, extending the driving range and improving fuel economy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional route planning systems are used, then route determination is simple, but energy efficiency is poor and driving range is insufficient
Solution Approach 1:
The system performs preliminary evaluation of multiple candidate routes before vehicle traversal, calculating energy consumption characteristics in advance using map data, vehicle state, and environmental conditions. This allows the selection of an energy-optimal route before the journey begins, improving energy efficiency without adding complex real-time computation during driving.
Solution Approach 2:
The system continuously monitors actual vehicle state, environmental conditions, and energy consumption during traversal, comparing against predicted values. This feedback loop allows dynamic adjustment of route selection and driving parameters to maintain optimal energy efficiency, resolving the contradiction between simple routing and energy optimization.
2Duration of action of moving object
If real-time eco-routing and adaptive driving control are implemented, then driving range is extended and fuel economy is improved, but system complexity increases
Solution Approach 1:
The powertrain control module performs multiple functions: traditional engine/motor control, route evaluation, energy consumption prediction, and adaptive driving control. By consolidating these functions into a single controller, the system extends driving range through intelligent control without proportionally increasing overall system complexity.
Solution Approach 2:
The system uses the vehicle's existing sensors, processors, and communication systems to perform eco-routing and adaptive control functions. By leveraging already-present components for multiple purposes, the system extends driving range through software intelligence rather than adding extensive new hardware.
3Loss of energy
If multiple candidate routes are evaluated for energy characteristics, then energy optimization is improved, but computational time and processing requirements increase
Solution Approach 1:
The system evaluates multiple candidate routes but focuses detailed energy analysis only on the most promising options identified by preliminary filtering. This partial evaluation approach achieves sufficient energy optimization without the computational burden of exhaustive analysis of all possible routes.
Solution Approach 2:
The system performs preliminary route filtering and energy characteristic evaluation before final route selection, using map data and vehicle state to pre-assess candidate routes. This preliminary action reduces the computational load during real-time decision-making, balancing energy optimization with acceptable planning time.
Data Source
AI summary
Presented are intelligent vehicle systems and control logic for driver coaching and on-demand vehicle charging, methods for making/using such systems, and motor vehicles with real-time eco-routing and automated driving capabilities. A method for controlling operation of a vehicle includes: determining an origin and destination for the vehicle; conducting a geospatial query to identify a candidate route for traversing from the origin to the destination; determining, based on current electrical characteristics of the vehicle's battery pack, an estimated driving range for the vehicle; responsive to the estimated driving range being less than the candidate route's distance, evaluating energy characteristics of the candidate route to derive an estimated energy expenditure to reach the destination; using the estimated energy expenditure, generating an action plan with vehicle maneuvering and/or accessory usage actions that extend the estimated driving range; and commanding a resident vehicle subsystem to execute a control operation based on the action plan.