Predictive EV Drive Cooling for Motor and Power Electronics Efficiency
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Solution Overview
Problem
Current temperature regulation systems in electric vehicles (EVs) fail to effectively manage thermal conditions of power electronics and electric motors, leading to reduced efficiency and vehicle range due to inefficient operation in temperature-dependent regions.
Innovation Solution
An advanced driver assistance system (ADAS) with imaging sensors and navigation data is used to predict road conditions and adjust the temperature of power electronics and electric motors through a predictive controller, employing coolant pumps, oil pumps, cooling fans, and active air flaps to maintain optimal operating temperatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current temperature regulation systems lower coolant or oil temperature before high load demand, then overheating is prevented, but energy efficiency and vehicle range are reduced due to inability to optimize for efficiency regions
Solution Approach 1:
The system performs preliminary cooling actions before high load demand occurs by using navigation data to predict upcoming high load conditions. The predictive controller activates cooling fans and adjusts coolant flow in advance, allowing the system to prevent overheating while optimizing for energy efficiency during the predicted high load period, rather than continuously cooling or reacting after temperature rise.
Solution Approach 2:
The system dynamically adjusts cooling strategies based on predicted driving conditions. The predictive controller modifies coolant flow rates, cooling fan speeds, and active air flap positions in real-time according to the predicted temperature profile and power loss minimization requirements, transitioning between different cooling modes to optimize both reliability and energy efficiency.
2Productivity
If electric motors and power electronics operate in less efficient regions (low-speed/high-torque or high-speed/low-torque), then vehicle performance is maintained, but temperature-dependent efficiency decreases and vehicle range is reduced
Solution Approach 1:
The system changes operating parameters by adjusting the temperature of electric motors and power electronics through predictive thermal management. By controlling coolant temperature and flow rate based on predicted operating conditions, the system maintains components within optimal temperature ranges that minimize power losses, even when operating in less efficient torque-speed regions.
Solution Approach 2:
The predictive controller uses feedback from navigation data about upcoming road conditions, speed limits, and gradients to continuously adjust cooling system parameters. This closed-loop control optimizes the temperature of power electronics and electric motors to minimize power losses while maintaining the ability to deliver required performance.
3Use of energy by moving object
If navigation data is used for predictive control, then energy efficiency is enhanced through optimized temperature management, but system complexity increases due to integration of ADAS and predictive algorithms
Solution Approach 1:
The predictive controller integrates multiple functions into a single system: it processes navigation data, predicts road conditions, estimates motor speed and torque, forecasts temperature profiles, and controls cooling system components. This multi-functional approach enhances energy efficiency while managing complexity by consolidating control logic rather than adding separate independent systems.
Solution Approach 2:
The system uses existing navigation data from ADAS that is already being collected for other purposes (route planning, speed limit detection). By repurposing this existing data for thermal management predictions, the system enhances energy efficiency without requiring additional sensors or data collection infrastructure, thereby limiting the increase in system complexity.
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 enhances energy efficiency by minimizing power losses and thermal stresses, increasing the electric driving range and reducing the lifetime and reliability of power electronics and electric motors, while also decreasing overall power consumption and replacement costs.
Implementation Method 1
The system may comprise one or more cooling fans... determining a control input to activate the cooling fan and the AAF to regulate coolant temperature of a coolant
Implementation Method 2
allow coolant to pass through and into a radiator, of the one or more radiators, lowering the coolant temperature
Data Source
AI summary
Systems and methods for controlling vehicle energy efficiency are provided. The system may comprise an ADAS, a navigation sensor configured to collect navigation data, and an electric drive cooling system, comprising a powertrain controller, comprising a processor and a memory, one or more electric motors (EMs), one or more power electronics (PEs), and a temperature control system. The processor may be configured to determine a vehicle route, dissect the route into a plurality of segments, calculate a travel time for each segment, divide the travel time into a number of prediction steps, forming a prediction horizon, establish one or more road conditions, estimate EM motor speed and torque, and estimate a temperature profile for the EMs and PEs across the prediction horizon, and determine one or more control inputs to cause the one or more EMs and the one or more PEs to function within a desired temperature range.