Vehicle Thermal Management via Predictive Drive Train Temperature Control
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Solution Overview
Problem
Existing predictive thermal management systems for vehicles require complex algorithms and significant computer power, and fail to fully utilize their potential due to the need to account for changes in driving routes and styles, leading to potential overheating of components and reduced energy efficiency.
Innovation Solution
A method for operating a motor vehicle that simplifies temperature regulation by allowing users to input and adjust peripheral conditions such as driving style, vehicle load, and electrical system usage, enabling the determination and optimization of drive train temperature for reduced consumption, with feedback on fuel economy and suggestions for optimizing behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex algorithms are used for predictive thermal management, then temperature regulation accuracy is improved, but device complexity and computing power requirements increase
Solution Approach 1:
The patent segments the thermal management system into multiple independent control zones (engine cooling, battery cooling, cabin climate control) that can be managed separately. Each zone has its own temperature sensors and control algorithms, allowing simplified local control rather than one complex global algorithm. This segmentation reduces overall system complexity while maintaining temperature regulation accuracy in each specific zone.
Solution Approach 2:
The system performs preliminary thermal management actions based on predicted future conditions. By using navigation data to anticipate upcoming terrain, traffic, and environmental conditions, the system pre-adjusts temperatures before they are needed. This allows simpler real-time algorithms because the heavy computational work is done in advance based on route information, reducing the complexity of moment-to-moment control decisions.
2Use of energy by moving object
If comprehensive predictive algorithms are implemented, then energy efficiency is improved, but computing power requirements and system complexity increase
Solution Approach 1:
The system uses navigation data and route information to perform preliminary thermal management calculations before the vehicle reaches specific conditions. By predicting future thermal requirements based on upcoming terrain, traffic patterns, and environmental conditions, the system pre-adjusts temperatures and reduces the need for intensive real-time computing. This shifts computational load to advance planning, improving energy efficiency while reducing instantaneous computing power requirements.
Solution Approach 2:
The thermal management system utilizes readily available vehicle data (engine temperature, battery state, ambient conditions) and navigation information that is already being processed for other purposes. By integrating thermal management controls with existing vehicle systems and data streams, the system achieves energy-efficient operation without requiring separate dedicated computing resources, thus improving energy efficiency while minimizing additional computing power requirements.
3Reliability
If the system accounts for all possible driving route changes, then reliability is improved, but device complexity and adaptability requirements increase
Solution Approach 1:
The system uses navigation data to identify potential thermal stress conditions ahead of time, such as steep climbs, heavy traffic congestion, or hot ambient conditions. By predicting these conditions in advance, the system proactively adjusts temperatures and activates cooling systems before overheating occurs. This preliminary action approach improves reliability by preventing overheating events while keeping the control algorithm relatively simple, as it relies on basic predictive logic rather than complex real-time optimization.
4Adaptability or versatility
If manual user input is required for route changes, then adaptability is improved, but ease of operation decreases
Solution Approach 1:
The system automatically detects and adapts to the driver's actual behavior patterns by monitoring acceleration, braking, and steering inputs. Instead of requiring manual user input to specify driving style, the system self-adjusts thermal management parameters based on observed driving patterns. This maintains adaptability to individual driving styles while significantly improving ease of operation, as the system learns and adapts automatically without burdening the driver with additional controls or inputs.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces energy consumption and emissions by actively engaging users in predictive thermal management, simplifying algorithms and reducing computing power, while providing direct feedback on fuel-saving opportunities and promoting eco-friendly driving habits.
Implementation Method 1
a coolant flow through a drive train of the vehicle is to be regulated
Implementation Method 2
a device for regulating the temperature of the drive train
Data Source
AI summary
Methods and systems are provided for estimating a drive train temperature for a journey. In one example, a method comprises requesting a vehicle operator to input one or more travel parameters for the journey, predicting travel parameters the vehicle operator omitted to input, and displaying an estimated fuel economy for the journey, where the estimated fuel economy is based on the estimated drive train temperature, which is based on the travel parameters.