Fleet Vehicle Preconditioning Using Historical Demand Forecasts
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Solution Overview
Problem
Existing vehicle fleet preconditioning methods result in high energy consumption and environmental impact due to optimizing vehicle temperature based on customer demands, which is inefficient and costly.
Innovation Solution
A method and control unit that precondition vehicles based on historical data to adapt the number of preconditioned vehicles to demand, using climate control and engine heaters to minimize unnecessary heating/cooling, and a control unit to manage this process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If all vehicles of the fleet are preconditioned before use, then customer comfort demand is satisfied, but energy consumption increases significantly
Solution Approach 1:
The system applies partial action by preconditioning only a selected subset of vehicles rather than the entire fleet. The control unit determines which specific vehicles require preconditioning based on predicted demand, thereby providing sufficient comfort assurance while avoiding unnecessary energy expenditure on vehicles that will not be used.
Solution Approach 2:
The system performs preliminary action by predicting future vehicle demand and preconditioning vehicles in advance of actual use. Historical data and algorithms are used to forecast which vehicles will be needed, allowing the system to prepare them beforehand without knowing the exact timing of customer requests.
2Reliability
If a high number of vehicles are preconditioned simultaneously, then availability of preconditioned vehicles is ensured, but operational costs increase
Solution Approach 1:
The system uses partial action by preconditioning only the necessary number of vehicles to meet predicted demand rather than preconditioning all vehicles. This selective approach maintains sufficient availability while reducing operational costs associated with heating or cooling unnecessary vehicles.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual vehicle usage patterns and comparing them with predictions. Historical data is updated with real-world outcomes, allowing the algorithm to learn and improve its demand forecasting accuracy over time, thereby optimizing the number of preconditioned vehicles.
3Loss of time
If vehicles are preconditioned without advance ordering, then immediate availability is achieved, but unnecessary heating/cooling occurs
Solution Approach 1:
The system performs preliminary action by predicting which vehicles will be demanded and preconditioning them in advance without requiring customers to place advance orders. This eliminates customer waiting time while avoiding unnecessary preconditioning of vehicles that are not predicted to be used.
Solution Approach 2:
The system applies dynamics by continuously adapting the preconditioning strategy based on changing conditions. The algorithm adjusts which vehicles are preconditioned based on real-time factors such as current temperature, predicted demand patterns, and vehicle location, allowing flexible optimization rather than a static approach.
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
Reduces energy consumption and environmental impact by providing preconditioned vehicles when needed without over-preconditioning, ensuring efficient use of resources and lower fuel consumption.
Implementation Method 1
by heating or cooling a passenger compartment of the vehicle by means of the climate control system, a desired temperature can be achieved
Implementation Method 2
by heating or cooling a passenger compartment of the vehicle by means of the climate control system, a desired temperature can be achieved
Implementation Method 3
by means of an engine heater of each vehicle, for reaching a desired engine temperature of the vehicle before the vehicle is to be used
Data Source
AI summary
A method for preconditioning vehicles of a vehicle fleet, wherein vehicles of the vehicle fleet are provided at a vehicle fleet parking area when not used. The method includes the step of preconditioning a number of the vehicles of the vehicle fleet parked at the vehicle fleet parking area before such a vehicle is to be used, and the step of selecting the number of vehicles to be preconditioned based on historical data with respect to demand for using vehicles of the vehicle fleet.