Vehicle Cooling Pump MPC for Temperature-Energy Tradeoffs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cooling systems for motor vehicles are inefficient due to suboptimal control of cooling pumps, which leads to energy loss and varying optimal component temperatures depending on operating conditions.
Innovation Solution
Integration of a cooling pump control as a degree of freedom into an efficiency optimization framework using Model Predictive Control (MPC) to operate cooling pumps based on a predictive longitudinal driving strategy, minimizing energy loss and optimizing component temperatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If cooling pump control is implemented using conventional closed-loop control to maintain optimal component temperature, then component temperature control is improved, but energy loss increases due to pump output being considered a loss
Solution Approach 1:
The MPC controller predicts future component temperatures and pump power consumption over a prediction horizon, allowing the system to plan cooling actions in advance rather than reacting to current temperature deviations. This predictive approach enables optimization of the trade-off between temperature control and energy consumption by considering future operating conditions.
Solution Approach 2:
The system dynamically adjusts pump operating points based on predicted future conditions rather than maintaining fixed control parameters. The MPC controller continuously optimizes pump power consumption and component temperature trajectories, adapting to changing driving conditions and thermal loads in real-time.
2Ease of operation
If cooling pump control is implemented using rules-based logic or simple closed-loop control, then control implementation is simplified, but efficiency optimization is limited because optimal component temperature varies with operating situation
Solution Approach 1:
The MPC controller uses feedback from temperature sensors and power consumption measurements to continuously update predictions and adjust control actions. The controller minimizes a cost function that includes both temperature deviations and pump power consumption, creating a closed-loop system that optimizes efficiency while maintaining temperature control.
Solution Approach 2:
The system changes control parameters dynamically by optimizing pump power consumption and component temperature trajectories based on predicted future operating conditions. The MPC controller adjusts these parameters continuously to achieve optimal efficiency across varying driving situations rather than relying on fixed rules.
Data Source
AI summary
A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, a first term of which represents an output of the cooling pump (28). In addition, the processor unit (3) is configured for, by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14), ascertaining a speed trajectory of the motor vehicle (1) situated within a prediction horizon and simultaneously ascertaining a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function (15) is minimized.