EV Subsystem Preconditioning via Weather Forecast Scheduling
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Solution Overview
Problem
Electrified vehicles face unique thermal management challenges in achieving desired comfort levels while balancing fuel economy and electric range, as conventional methods lack efficient preconditioning strategies based on weather forecasts and user inputs.
Innovation Solution
A method and system for preconditioning electrified vehicle subsystems, including the battery pack, transmission, engine, and interior cabin, by scheduling thermal conditioning based on weather forecasts and user inputs, using sensors and cloud communication to determine the next expected usage time and prioritize thermal management of these components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thermal conditioning is applied to all subsystems continuously, then comfort and performance are improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary thermal conditioning of subsystems based on predicted future usage times and weather forecasts. By pre-heating or pre-cooling components before they are needed, the system ensures optimal performance and comfort when the vehicle is used, while avoiding continuous operation that would waste energy. The control system schedules thermal management activities in advance based on learned usage patterns and forecasted conditions.
Solution Approach 2:
The thermal management system dynamically adjusts its operation based on real-time conditions including actual weather data, vehicle state, and predicted usage patterns. The system transitions from static continuous conditioning to dynamic event-driven conditioning, activating thermal management only when and where needed based on changing conditions and predictions.
2Use of energy by moving object
If thermal conditioning is delayed until usage time, then energy consumption is reduced, but comfort and performance deteriorate
Solution Approach 1:
The system predicts future usage times based on historical data and user patterns, then initiates thermal conditioning in advance of the actual usage time. This preliminary action ensures that when the vehicle is needed, the subsystems are already at optimal temperatures, providing immediate comfort and performance without requiring intensive last-minute heating or cooling.
Solution Approach 2:
The system continuously monitors actual usage patterns and compares them with predictions, using this feedback to refine future predictions and adjust thermal management scheduling. The system also monitors weather forecast accuracy and adjusts conditioning schedules based on actual versus predicted conditions, optimizing the balance between energy consumption and comfort over time.
3Productivity
If weather forecast data is integrated into thermal management, then thermal optimization is improved, but system complexity increases
Solution Approach 1:
The system uses weather forecast data as an intermediary input to inform thermal management decisions. Rather than directly controlling thermal systems based on complex multi-parameter analysis, the system incorporates forecasted weather conditions as a key input variable that drives scheduling decisions, simplifying the control logic while improving thermal optimization.
Solution Approach 2:
The control system performs multiple functions using the same data infrastructure: it processes weather forecasts, learns usage patterns, predicts future usage times, and schedules thermal management activities. By consolidating these functions into a unified control architecture that handles diverse inputs and outputs, the system reduces overall complexity despite the advanced capabilities it provides.
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 comfort, fuel economy, and electric range by optimizing thermal conditions of vehicle subsystems before use, improving overall performance and efficiency by leveraging weather forecasts and user data for proactive thermal management.
Implementation Method 1
scheduling preconditioning of a battery pack, an interior cabin, a transmission and an engine
Implementation Method 2
adjusting a temperature of the battery pack such that the temperature is within a desired operating range
Implementation Method 3
scheduling preconditioning of a battery pack, an interior cabin, a transmission and an engine
Implementation Method 4
adjusting a temperature of the battery pack such that the temperature is within a desired operating range
Implementation Method 5
scheduling preconditioning of a battery pack, an interior cabin, a transmission and an engine
Implementation Method 6
adjusting a temperature of the transmission fluid such that the temperature is within a desired operating range
Implementation Method 7
scheduling preconditioning of a battery pack, an interior cabin, a transmission and an engine
Implementation Method 8
adjusting a temperature of an engine coolant such that the temperature is within a desired operating range
Data Source
AI summary
A method for preconditioning various subsystems of an electrified vehicle according to an exemplary aspect of the present disclosure includes, among other things, scheduling preconditioning of a battery pack, an interior cabin, a transmission and an engine of the electrified vehicle prior to a next expected usage time based at least on a weather forecast.