Fleet EV Ownership Cost Optimization via Multi-Agent Simulation

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Solution Overview

Problem

Optimizing the total ownership cost of a fleet of electric vehicles is challenging due to factors like battery charging, available range, and perceived higher costs compared to internal combustion engine fleets.

Innovation Solution

A system and method utilizing a command unit with a processor and memory to execute a simulation module, sampling module, and optimization module. This system constructs a multi-agent model based on historical fleet trip data and mobility patterns, simulates different electric vehicle configurations, estimates expected costs, and determines an optimal configuration that minimizes investment and operational costs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If electric vehicles are used to replace internal combustion engine vehicles, then environmental benefits and operational efficiency are improved, but perceived higher costs and charging infrastructure requirements worsen the ownership cost optimization

Engineering Contradiction:

Improveoperational efficiencyVSAvoidcharging infrastructure

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The charging infrastructure is segmented into multiple types of charging stations (Level 1, Level 2, DC fast charging) distributed across different locations. The system optimizes by assigning different charging levels to different vehicles based on their specific needs, rather than requiring uniform infrastructure across the entire fleet

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts charging strategies based on real-time factors including battery state of charge, vehicle location, charging station availability, and time of day. The optimization module continuously adapts charging schedules and vehicle assignments to minimize infrastructure requirements while maintaining operational efficiency

Inventive Principle:

Principle #15Dynamics

2Device complexity

If multiple factors are considered in fleet optimization, then ownership cost optimization is improved, but the complexity of analysis and decision-making increases

Engineering Contradiction:

Improveoptimization analysisVSAvoiddecision-making efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The system automatically performs comprehensive optimization analysis by executing the simulation module, sampling module, and optimization module without requiring manual intervention. The command unit autonomously processes multiple factors including vehicle costs, charging infrastructure, operational constraints, and environmental conditions to generate optimized fleet configurations

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual decision-making processes are replaced with computational algorithms including Monte Carlo simulations and optimization algorithms that automatically process complex data relationships. The system substitutes human analytical processes with automated computational methods that can handle multiple variables simultaneously

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If battery charging and range constraints are enforced, then operational reliability is improved, but fleet flexibility and routing options are reduced

Engineering Contradiction:

Improveoperational reliabilityVSAvoidrouting flexibility

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary charging actions by scheduling vehicles to charge during off-peak hours or at designated locations before trips are needed. The optimization module pre-calculates charging schedules that ensure batteries reach required state of charge levels, allowing vehicles to undertake diverse routes without last-minute constraints

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Charging stations serve as intermediaries between the battery energy storage system and the external power source. The system optimizes by strategically positioning and utilizing these intermediary charging points along routing paths, enabling vehicles to maintain reliability requirements while accessing flexible routing options through coordinated charging-transportation planning

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12242984B2Ownership cost optimization for fleet with electric vehicles

Publication Date: 2025.03.04 GM GLOBAL TECHNOLOGY OPERATIONS LLC

AI summary

A system for optimizing ownership cost of a fleet having electric vehicles includes a command unit adapted to selectively execute a simulation module, a sampling module and an optimization module. The command unit is configured to construct a multi-agent model based at least partially on historical fleet trip data and mobility pattern data of the fleet. Route data for a set of fleet tasks is obtained, including charging infrastructure data. The command unit is configured to simulate different configurations of the electric vehicles carrying out the set of fleet tasks over a predefined period, via the simulation module, based in part on the multi-agent model and the route data. The command unit is configured to determine an optimal configuration from the different configurations of the electric vehicles, via the optimization module. The optimal configuration minimizes investment and operational costs of the fleet.