Multi e-Axle Traction Control Using Look-Ahead Torque Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Range anxiety remains a significant barrier for heavy-duty electric trucks due to high energy consumption, large batteries, sparse charging infrastructure, and operational demands, necessitating efficient powertrain management to extend driving range and reduce energy losses.
Innovation Solution
A system for optimizing traction control in multi-e-axle electric vehicles using look-ahead information to generate optimal torque profiles and distribute torque between e-axles, minimizing energy losses through a three-step control strategy involving torque profile generation, allocation, and dynamic drive control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If large batteries are used to increase driving range, then energy capacity is improved, but vehicle weight increases
Solution Approach 1:
The system performs preliminary actions by using look-ahead information about upcoming road conditions (grades, curves, traffic) to pre-calculate optimal torque profiles before reaching those conditions. This allows the vehicle to anticipate energy demands and optimize power delivery in advance, maximizing range without requiring additional battery capacity.
2Loss of energy
If conventional torque distribution is used, then system simplicity is maintained, but energy losses increase
Solution Approach 1:
The system implements dynamic torque distribution that continuously adapts to real-time operating conditions. The supervisory controller dynamically adjusts torque allocation between e-axles based on calculated optimal profiles, allowing each electric machine to operate in its most efficient range. This dynamic approach minimizes powertrain losses while managing the increased control complexity through automated algorithms.
Solution Approach 2:
The system changes operational parameters by continuously varying torque distribution ratios between e-axles based on optimal torque profiles. By adjusting these parameters dynamically according to road conditions and vehicle state, the system optimizes energy efficiency across different operating scenarios without requiring hardware modifications.
3Use of energy by moving object
If optimal torque profile optimization is implemented, then energy efficiency is improved, but control complexity increases
Solution Approach 1:
The supervisory controller performs preliminary optimization by calculating optimal torque profiles in advance using look-ahead road information. This pre-computation allows the system to prepare efficient torque distribution strategies before executing them, improving energy efficiency while managing control complexity through proactive rather than reactive control.
Data Source
AI summary
Systems and methods for range extension with optimal traction control for multi e-axle based electrified vehicles are disclosed. The systems and methods optimize the operation of electric vehicles with multiple e-axles by generating optimal torque profiles based on look-ahead information about road conditions and optimally distributing torque between e-axles to minimize energy losses and extend driving range. The optimal traction control system operates in three main steps: first, generating an optimal torque profile based on look-ahead information about road grade and speed limits; and second, optimally allocating the requested torque between multiple e-axles to minimize energy losses. Third, dynamic drive control for electric machine to track optimal torque with minimized current. The optimal torque profile follows the trend of the road grade, providing more torque on uphill sections and less torque or even negative torque on downhill sections.