Multi-Electric Axle Power Distribution for Traction and Energy Control
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Solution Overview
Problem
Existing systems for controlling multiple axles in electric vehicles fail to optimize traction efforts by distributing vehicle mass effectively, leading to reduced wheel pressure and inefficiencies in power distribution.
Innovation Solution
A multi-axle controller that determines power distribution among electric axles by optimizing a performance cost function, considering vehicle parameters, road grade, and load distribution, and adjusts motor generator loads to maximize efficiency and safety margins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power is distributed among multiple electric axles without optimization, then the vehicle can operate with multiple driven axles, but energy consumption increases and traction efficiency decreases
Solution Approach 1:
The system dynamically changes operating parameters including power distribution ratios among axles, motor generator loads, and torque vectors based on real-time conditions (road grade, vehicle speed, axle slip) to optimize energy consumption while maintaining traction control. The multi-axle controller adjusts these parameters continuously to find the optimal balance between energy efficiency and traction performance.
Solution Approach 2:
The power distribution system transitions from static to dynamic control, where the multi-axle controller continuously monitors axle performance, road conditions, and vehicle state to adjust power allocation in real-time. This dynamic adaptation allows the system to respond to changing traction conditions and optimize energy usage throughout the vehicle operation cycle.
2Stability of the object's composition
If mass is distributed to multiple axles to reduce ground contact pressure, then vehicle stability improves, but wheel pressure and traction effort are reduced
Solution Approach 1:
The system applies different torque and power levels to individual axles based on their specific traction conditions, load distribution, and road surface characteristics. Each axle receives optimized local control rather than uniform power distribution, allowing maximum traction effort at each contact point while maintaining overall vehicle stability through coordinated multi-axle control.
Solution Approach 2:
The multi-axle controller predicts future traction requirements based on road grade data, vehicle acceleration demands, and current axle performance, then pre-adjusts power distribution and torque vectors to optimize both stability and traction effort before critical moments occur. This anticipatory control ensures optimal traction is available when needed.
3Productivity
If real-time control of multiple axles is implemented to optimize traction, then traction efficiency improves, but system complexity increases
Solution Approach 1:
The multi-axle controller performs multiple functions including power distribution optimization, traction control, stability management, and energy efficiency optimization through a single integrated system. This universal controller consolidates what could be multiple separate control systems, reducing overall complexity while maintaining comprehensive traction optimization across all axles.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor axle slip, motor generator performance, vehicle acceleration, and road conditions, then the multi-axle controller uses this feedback to automatically adjust power distribution and torque vectors. This closed-loop control optimizes traction efficiency automatically without requiring complex manual intervention or overly sophisticated control algorithms.
Data Source
AI summary
A system for controlling a plurality of electric axles of a vehicle is disclosed, comprising: the plurality of electric axles, one or more motor generators associated with one or more of the plurality of electric axles, and a multi-axle controller communicatively coupled to the one or more motor generators and configured to execute software to cause the multi-axle controller to: determine a power demand of the vehicle based at least in part on one or more parameters of each electric axle and current and/or future road grade; generate a performance cost function associated with one or more performance metrics, the power demand, and/or the vehicle information; determine a power distribution among the plurality of electric axles by optimizing the performance cost function; and transmit a traction command to the one or more motor generators to control the operation of the plurality of electric axles.