EV Powertrain Torque Control Under Battery Thermal Constraints
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Solution Overview
Problem
Current powertrain control systems for electric vehicles fail to optimize performance and energy usage, particularly in high-performance applications like racing, due to limitations in torque distribution and thermal management, leading to suboptimal lap times and battery degradation.
Innovation Solution
A control system that continuously adjusts torque distribution between electric machines and an internal combustion engine, or between front and rear axles, using real-time data from sensors and navigation, to minimize travel time while adhering to constraints such as battery state of charge and temperature, incorporating AI/ML for adaptive optimization and data-driven heuristic relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If torque distribution is optimized for maximum power output, then vehicle performance and acceleration are improved, but battery thermal degradation increases and state of charge depletes too quickly
Solution Approach 1:
The control system dynamically adjusts torque distribution between front and rear axles based on real-time battery state of charge and temperature conditions. The system transitions from static torque vectors to dynamic torque vectors that adapt to changing battery conditions, allowing maximum performance when battery capacity is sufficient and reducing torque when thermal or charge limits are approached.
Solution Approach 2:
The system changes operational parameters (torque distribution ratios, power split between electric machines) based on battery state of charge and temperature thresholds. When battery temperature or SOC enters critical ranges, the control parameters are automatically adjusted to reduce thermal load and preserve battery capacity, thereby resolving the contradiction between performance and thermal management.
2Loss of time
If aggressive torque distribution is used to minimize lap time, then racing performance is improved, but battery state of charge depletes faster leading to performance degradation later in the lap
Solution Approach 1:
The control system performs preliminary optimization by calculating the optimal torque distribution strategy before the vehicle completes a lap, taking into account the entire track profile and battery state. This allows the system to plan power usage in advance, ensuring sufficient state of charge is maintained throughout the lap to sustain high performance without sudden degradation.
Solution Approach 2:
The system continuously monitors battery state of charge and adjusts torque distribution in real-time based on actual battery performance feedback. When SOC depletion is detected, the control system automatically modifies torque vectors to preserve battery capacity, creating a closed-loop control that balances lap time optimization with sustained battery reliability throughout the racing sequence.
3Reliability
If thermal management constraints are strictly enforced to protect the battery, then battery health is maintained, but vehicle performance and acceleration capability are reduced
Solution Approach 1:
The control system applies thermal management constraints selectively rather than continuously. During periods when battery temperature and charge state are within safe margins, the system relaxes thermal constraints to allow maximum acceleration capability. Thermal management actions are applied partially - only when and where needed - rather than as continuous limiting constraints, thereby maintaining both battery health and performance.
4Use of energy by moving object
If real-time adaptive control is implemented to optimize torque distribution, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical torque management systems with electronic control and software-based optimization algorithms. The real-time adaptive control is implemented through programmable control units that calculate optimal torque distribution based on sensor inputs, substituting physical complexity with computational intelligence. This allows sophisticated energy optimization without proportionally increasing mechanical or hardware complexity.
Data Source
AI summary
A method and control system for performance-optimized control of a powertrain of an electric vehicle are disclosed. The method includes acquiring vehicle status data along a specified drive route for the electric vehicle. The vehicle status data includes a state of charge of a traction battery and a battery temperature of the traction battery. The method also includes determining a current position of the electric vehicle along the specified drive route. The method includes receiving driver requests and continuously controlling, based on a real-time control function using the vehicle status data, the current position, and the driver requests as input data, at least one control parameter along the specified drive route. The real-time control function is adapted to minimize a travel time along at least a portion of the specified drive route while conforming to pre-defined constraints on the vehicle status data.