Multi-Motor Torque Allocation Across the Full Driving Cycle
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-motor systems lack global optimization across an entire trip, leading to inefficiencies such as overheating and mechanical losses due to transmissions, and do not integrate energy regeneration effectively.
Innovation Solution
A multi-component optimization control system that globally optimizes torque load distribution across motors and generators, considering the entire trip and constraints like motor temperatures, using a controller with sensors to dynamically allocate driving tasks and regenerate energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential local optimization is used to determine motor load allocation, then the system can make decisions based on current conditions, but it cannot achieve overall efficient allocation of loads across motors for the entire trip
Solution Approach 1:
The system receives trip information in advance and performs global optimization calculations before the trip begins, determining the optimal motor load allocation strategy for the entire journey. This preliminary action allows the system to achieve overall efficient load allocation rather than making sequential local decisions, directly resolving the contradiction between immediate responsiveness and overall trip efficiency.
2Adaptability or versatility
If transmissions are used to transfer torque load between motors, then torque can be redistributed, but mechanical losses are introduced into the system
Solution Approach 1:
The system replaces mechanical torque transfer mechanisms (transmissions) with independent motor control. Each motor is controlled independently to provide the required torque distribution, eliminating the need for mechanical power transfer between motors. This substitution removes mechanical losses while maintaining torque distribution capability, directly resolving the energy loss issue.
3Reliability
If existing systems consider motor temperatures, then they can prevent overheating, but the consideration is limited to binary decisions within pre-defined ranges rather than optimized allocation
Solution Approach 1:
The system transforms temperature management from binary on/off decisions to continuous parameter optimization. The global optimization algorithm treats motor temperatures as continuous variables and dynamically adjusts motor load allocation to optimize performance while maintaining temperatures within safe ranges. This allows for nuanced, optimized temperature management rather than crude binary switching, resolving the contradiction between reliability and optimization capability.
Data Source
AI summary
A multi-motor switching system and method for obtaining a global optimization of performance criteria that takes into account variables and conditions across an entire driving cycle. The controller of the system is adapted to conduct a global optimization in that it determines the most optimal distribution of motor loads over an entire trip or driving cycle, as opposed to sequentially determining the optimized solution for a given point of time and localized set of current condition. In one embodiment where the control system provides for global optimization, the controller receives trip information through a trip planning tool. The controller utilizes the trip information to generate a driving cycle and further incorporates this information into the optimization process performed by the controller to determine the optimal solution for the entire trip.