Vehicle Propulsion Energy Management Using Thermal Current Limiting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current energy management systems for electric propulsion in vehicles face inefficiencies and potential damage due to overheating, particularly with high currents, and existing solutions like finite state automations are complex and costly, while oversizing components is not practical.
Innovation Solution
A method that determines parameters of electrical components in operation, uses a thermal model to predict real-time temperatures, and automatically reduces current to prevent overheating, eliminating the need for temperature sensors and allowing for optimized energy management without entering an idle state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If finite state automations are used for energy management, then discrete switching states can be controlled, but the system complexity increases and limit cycles occur causing component damage
Solution Approach 1:
The patent replaces the mechanical finite state automation control system with a neural network-based intelligent control system. The neural network continuously processes input parameters (temperature, current, voltage) and generates smooth control signals, eliminating the discrete switching states and limit cycles inherent in finite state automations. This substitution resolves the contradiction by maintaining operational control while dramatically reducing system complexity and eliminating harmful oscillations.
Solution Approach 2:
The patent introduces dynamic, continuous control through neural network output signals that adapt in real-time based on system conditions. Instead of fixed discrete states, the system dynamically adjusts control parameters smoothly, preventing limit cycles and component damage while maintaining ease of operation through adaptive intelligence.
2Productivity
If finite state automations are used for energy management, then switching control is achieved, but frequently switching causes limit cycles and component damage
Solution Approach 1:
The neural network control system replaces the频繁 switching finite state automation with continuous smooth control signals. This substitution maintains productivity by efficiently managing power distribution while eliminating the frequent on-off switching that causes limit cycles and component damage, thereby improving reliability.
Solution Approach 2:
The patent implements continuous control through the neural network's real-time output signals, replacing the discontinuous switching action of finite state automations. This continuity eliminates harmful limit cycles and component stress from frequent switching, maintaining productivity while significantly improving component reliability and system stability.
3Reliability
If temperature sensors are installed to prevent overheating, then temperature monitoring is achieved, but system costs and complexity increase
Solution Approach 1:
The patent introduces an intermediary thermal model that acts as a virtual temperature sensor. This model predicts component temperatures by processing readily available electrical parameters (current, voltage, time) through thermal resistance and capacitance relationships. This intermediary approach achieves reliable overheating prevention without installing physical temperature sensors, reducing system complexity and cost.
Solution Approach 2:
The patent substitutes physical temperature sensors with a software-based thermal prediction model. The model calculates temperatures in real-time using electrical parameters and thermal characteristics, achieving the same protective function without the hardware complexity, cost, and installation requirements of actual temperature sensors.
4Reliability
If components are oversized to handle high currents, then overheating is prevented, but system cost and weight increase
Solution Approach 1:
The patent implements a feedback control system using the thermal model to continuously monitor predicted temperatures and adjust current distribution accordingly. This feedback mechanism allows the use of smaller, lighter components by dynamically controlling their operation to prevent overheating, rather than relying on oversized components designed for worst-case continuous operation.
Solution Approach 2:
The patent changes the operational parameters of electrical components dynamically through neural network control and thermal model monitoring. By adjusting current magnitudes and duty cycles based on real-time thermal predictions, the system enables the use of smaller, lighter components that operate within safe thermal limits through parameter optimization rather than physical oversizing.
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 effectively prevents overheating in electrical components, reduces costs by using standard-sized components, and optimizes energy management, ensuring efficient and safe operation of electric propulsion systems.
Implementation Method 1
inputting the parameter into a thermal model of the electrical component; predicting the temperature Tp of the electrical component being in operation state in real time
Implementation Method 2
during the operation of electrical machines and other consumer loads an energy loss is generated. This so-called power dissipation PDiss is converted into heat
Data Source
AI summary
The invention relates to a method for optimizing energy management of an electrical propulsion system of a vehicle, wherein the electrical propulsion system of the vehicle comprises an energy storage system and an electric machine. The electrical propulsion system further comprises at least one electrical component, wherein the electrical component has an idle state and an operation state. The method comprising the steps of: a) determining at least one parameter of the electrical component being in the operation state; b) inputting the parameter into a thermal model of the electrical component; c) predicting the temperature Tp of the electrical component being in operation state in real time on board the vehicle; d) comparing the predicted temperature value Tp with a predefined threshold value Tmax, and, e) automatically reducing the magnitude of the electrical current through the electrical component to a safe level if the predicted temperature value Tp exceeds the predefined threshold value Tmax.