EV Battery Pack Sizing for Route-Specific Parcel Delivery
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Solution Overview
Problem
Current electric vehicle (EV) battery pack configurations are inefficient as they often exceed the required capacity for parcel delivery, leading to increased carbon emissions due to unnecessary battery weight and inefficient energy usage, which is not optimized for specific delivery routes and conditions.
Innovation Solution
A method using trained artificial intelligence models to dynamically configure the battery pack size and service schedule based on parcel load, route characteristics, and contextual conditions such as road profile, weather, and traffic, to minimize carbon footprint per unit weight of parcels by selecting the most energy-efficient route and battery service options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If a larger battery pack is used to ensure sufficient range for parcel delivery, then the vehicle can complete more deliveries, but the carbon footprint per parcel increases due to unnecessary battery weight
Solution Approach 1:
The battery pack configuration is made dynamic and adaptable rather than fixed. The system uses AI models to determine the optimal battery size for each specific delivery route based on real-time factors such as parcel weight, route characteristics, traffic conditions, and weather. This allows the battery capacity to be optimized for each mission, avoiding the carbon footprint penalty of carrying excessive battery weight while ensuring sufficient range for the planned deliveries.
Solution Approach 2:
The patent changes the parameter of battery pack size from a fixed value to a variable that is optimized for each delivery route. By adjusting battery capacity parameters based on specific delivery requirements, the system achieves the minimum necessary battery weight for each mission, thereby reducing unnecessary carbon emissions while maintaining adequate delivery range.
2Object-generated harmful factors
If battery pack size is reduced to minimize carbon footprint, then emissions per parcel decrease, but the vehicle may not have sufficient range to complete deliveries
Solution Approach 1:
The system performs preliminary analysis using AI models to calculate the exact battery capacity needed for each specific delivery route before the vehicle departs. By pre-determining the optimal battery configuration based on route characteristics, parcel weight, and environmental conditions, the system ensures that the battery is sized precisely for the mission at hand, avoiding both excessive weight and insufficient range.
Solution Approach 2:
The patent implements a feedback mechanism where actual delivery data, battery consumption patterns, and route conditions are fed back into the AI models to continuously improve future battery sizing recommendations. This learning system enhances the accuracy of battery configuration predictions, ensuring reliable delivery completion while minimizing carbon footprint over time.
3Ease of operation
If fixed battery configuration is used for all deliveries, then operational simplicity is maintained, but energy efficiency decreases due to mismatched battery capacity for specific routes
Solution Approach 1:
The system enables the vehicle to self-determine its optimal battery configuration for each delivery route through integrated AI models that automatically analyze route data, parcel information, and environmental conditions. This self-service capability eliminates the need for manual battery configuration decisions while achieving superior energy efficiency compared to fixed configurations.
Solution Approach 2:
The patent creates a universal battery management system that can handle diverse delivery scenarios with a single adaptable platform. The AI-driven configuration system serves multiple functions: optimizing battery size, selecting service locations, planning routes, and adjusting to various environmental conditions, thereby maintaining operational simplicity while achieving route-specific energy efficiency.
Data Source
AI summary
A method, computer program product, and computer system for configuring a battery pack. Parcel delivery instructions including parcel level information, at least one electric vehicle property, and a first location and one or more destinations are received. A trained artificial intelligence model is used to extract an expected battery consumption of the electric vehicle for each of the plurality of potential routes, and to identify a delivery route that has a lowest expected battery consumption. Battery service options are mapped along the delivery route. Simulations of the electric vehicle completing the delivery are performed. A size of a battery pack to be used with the electric vehicle at a start of the delivery route at the first location is configured, and a battery pack service schedule for servicing the battery pack between the first location and the one or more destinations is configured, as a function of the multiple simulations.