Off-Road EV Energy Control for Trail Range and Battery Reserve
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Off-road electric vehicles face challenges in managing energy consumption due to limited charging stations and varying terrain conditions, which can lead to a low battery state in remote areas without communication capabilities.
Innovation Solution
A control system that uses a navigation system, sensor data, and a controller to dynamically manage the electric vehicle's drivetrain and accessories, estimating energy consumption based on trail characteristics, ambient conditions, and vehicle parameters to avoid low battery states by suggesting route changes and controlling power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If the EV operates in terrain mode or uses high-power accessories to maintain performance in off-road conditions, then the vehicle capability and drivetrain performance are improved, but the electric energy consumption increases substantially
Solution Approach 1:
The system dynamically adjusts drivetrain operating modes (2WD/4WD, high/low range, differential lock states) and accessory power levels based on real-time terrain conditions, vehicle state, and remaining energy reserves. The controller continuously monitors GPS location, trail characteristics, and battery charge state to adapt power distribution, ensuring optimal performance while preventing energy depletion in remote areas.
2Length of moving object
If the EV travels to distal off-road locations without charging stations, then the exploration capability and operational range are improved, but the risk of low battery state increases
Solution Approach 1:
The system performs preliminary energy assessment before departure and continuously monitors projected energy consumption along the planned route. The controller calculates required energy reserves based on trail difficulty, vehicle mass, ambient conditions, and distance to charging stations. If the projected charge state falls below safety thresholds, the system proactively suggests route modifications or power management adjustments before the low-battery condition occurs.
Solution Approach 2:
The system continuously monitors actual energy consumption versus predicted consumption, comparing real-time battery charge state with projected levels. GPS location and trail condition feedback enable the controller to update energy requirements dynamically. When discrepancies indicate risk of depletion, the system provides feedback to the operator through warnings and suggested actions to maintain safe charge levels.
3Speed
If the EV uses aggressive driving style or high-power accessories to overcome difficult terrain, then the traversal capability is improved, but the energy consumption rate increases
Solution Approach 1:
The system dynamically changes operational parameters including drivetrain gear selection (high/low range), wheel slip control thresholds, accessory power levels, and differential lock engagement based on real-time conditions. The controller adjusts these parameters to balance traversal capability with energy conservation, modifying driving characteristics according to trail difficulty and remaining energy reserves.
4Measurement precision
If the EV control system monitors and manages multiple parameters (battery charge state, trail conditions, ambient weather, vehicle mass) to optimize energy consumption, then the energy management accuracy is improved, but the system complexity increases
Solution Approach 1:
The controller integrates multiple functions into a single centralized system: monitoring battery charge state, tracking GPS location, characterizing trail conditions, measuring ambient temperature and precipitation, calculating vehicle mass effects, and controlling drivetrain and accessories. This multi-functional integration reduces overall system complexity compared to separate dedicated systems for each function while maintaining comprehensive energy management capability.
Data Source
AI summary
An operating system for a vehicle having an electric vehicle (EV) drivetrain and a plurality of electrically-powered accessories is described. A controller determines, via a navigation system, a target off-road trail segment, and characterizes the subject vehicle, ambient conditions, and the target off-road trail segment to determine an estimated consumption of electric energy for the vehicle to operate over the target off-road trail segment. The EV drivetrain and the electrically-powered accessories are controlled during operation of the vehicle on the off-road trail segment based upon the estimated consumption of electric energy for the subject vehicle. This is done to minimize a likelihood of a low SOC event for the DC power source for the trail segment and to avoid a low battery state at a location that is distal from a charging station.