EV Pre-Departure Charging for Cabin and Battery Preconditioning
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Solution Overview
Problem
Electric vehicles face reduced battery range due to climate control usage during rides, and existing charging systems do not optimize charging times based on user habits and charging station attributes, leading to inefficient energy use.
Innovation Solution
A system utilizing machine learning prediction models to determine optimal pre-departure charging times by predicting user-departure times, cabin temperatures, and battery temperatures, taking into account charging station attributes, to precondition the vehicle before departure, thus conserving energy and improving battery range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If climate control is used during rides to maintain user comfort, then user comfort is improved, but battery range is reduced due to increased energy consumption
Solution Approach 1:
The system preconditions the cabin temperature before the user departs by scheduling charging sessions to complete before the predicted departure time. This allows the cabin to be heated or cooled in advance using charging power rather than depleting battery power during the ride, thereby maintaining user comfort while preserving battery range.
2Use of energy by moving object
If charging is performed during idle time to optimize battery range, then battery range is improved, but user comfort deteriorates due to insufficient energy for climate control
Solution Approach 1:
The system schedules charging sessions to complete before the user's predicted departure time, ensuring that sufficient energy is stored in the battery by then. This preliminary charging action guarantees both optimal battery range for the trip and sufficient energy availability for climate control during the ride, thus improving both battery range and user comfort.
3Device complexity
If existing charging systems operate without optimization, then system complexity is reduced, but energy efficiency deteriorates due to lack of charging time optimization
Solution Approach 1:
The system employs machine learning models that analyze user departure patterns, charging station attributes, and weather conditions to predict optimal charging start times. This feedback mechanism continuously learns from historical data and adjusts charging schedules to maximize energy efficiency, significantly reducing energy loss while maintaining manageable system complexity through automated decision-making.
4Loss of energy
If machine learning models are implemented to optimize charging schedules, then energy efficiency is improved, but device complexity increases due to multiple prediction models
Solution Approach 1:
The system uses machine learning models to autonomously predict user departure times, determine optimal charging schedules, and select appropriate charging stations without requiring complex user input or manual configuration. The models self-adjust based on learned patterns, reducing the need for complex control logic and interface complexity while maximizing energy efficiency through intelligent, automated decision-making.
Data Source
AI summary
A computer-implemented method for scheduling pre-departure charging for electric vehicles includes predicting a user-departure time based on a first machine learning prediction model. The method further includes determining a cabin temperature to be set for the user at the user-departure time based on a second machine learning prediction model. The method further includes determining a battery-temperature to be set at the user-departure time based on a third machine learning prediction model. The method further includes determining a present charge level of a battery of the electric vehicle. The method further includes computing a charging start-time to start charging the battery based on one or more attributes of a charging station to which the electric vehicle is coupled, and based on the user-departure time, the cabin temperature, and the battery-temperature. The method further includes initiating charging the battery at the charging start-time.