Electric Vehicle Fleet Optimization for Cost Minimization
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Solution Overview
Problem
Existing charge and dispatch planning systems for shared electric vehicles often result in unnecessary costs due to shortages or surpluses of vehicles, leading to increased maintenance and replacement costs, as they do not effectively minimize long-term total costs.
Innovation Solution
An information processing method that determines an optimal number of movable bodies and their planned movement distances based on cost information varying with the number of vehicles and battery deterioration, using movement plan information, relationship information between distance and battery deterioration, and cost information to minimize long-term total costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a predetermined number of electric vehicles are used in the charge and dispatch planning system, then the vehicle dispatch control can be executed, but a shortage or surplus of electric vehicles occurs depending on demand changes, leading to unnecessary costs
Solution Approach 1:
The patent applies dynamics by making the number of electric vehicles adjustable rather than fixed. The system dynamically determines the optimal number of vehicles based on real-time demand predictions, battery states, and cost parameters. This allows the fleet size to adapt to changing dispatch requirements, preventing both shortages and surpluses of vehicles.
Solution Approach 2:
The patent changes the parameter of vehicle quantity from a static predetermined value to a dynamic optimized value. By introducing cost parameters (acquisition cost, maintenance cost, depreciation) and demand parameters, the system calculates the optimal number of vehicles that minimizes total costs while meeting dispatch requirements. This parameter optimization resolves the contradiction between having enough vehicles for productivity and avoiding unnecessary vehicle accumulation.
2Reliability
If the number of electric vehicles is increased to prevent shortage, then dispatch demand can be met, but maintenance costs and replacement costs increase due to battery deterioration
Solution Approach 1:
The patent implements feedback by continuously monitoring battery deterioration degrees and using this information to adjust vehicle deployment strategies. The system feeds back battery health data to the optimization algorithm, which then determines optimal dispatch plans that balance reliability requirements with cost constraints. This feedback loop prevents excessive vehicle acquisition by using actual battery condition data to make informed decisions.
Solution Approach 2:
The patent applies preliminary action by predicting future battery deterioration and planning vehicle replacement or maintenance in advance. The optimization algorithm considers projected battery degradation over time and schedules vehicle maintenance or replacement before critical failures occur. This preliminary planning reduces emergency replacement costs and extends vehicle utilization while maintaining dispatch reliability.
3Loss of energy
If the number of electric vehicles is decreased to reduce costs, then maintenance and replacement costs are reduced, but shortage of vehicles occurs when dispatch demand increases
Solution Approach 1:
The patent optimizes the vehicle quantity parameter by introducing cost functions that balance acquisition/maintenance costs against dispatch capability. The system calculates the marginal cost of adding versus removing vehicles based on demand elasticity, battery deterioration rates, and operational constraints. This parameter optimization identifies the precise number of vehicles needed to maintain productivity while minimizing costs, resolving the contradiction between cost reduction and dispatch capability.
Solution Approach 2:
The patent makes the vehicle fleet size dynamic rather than static. The optimization algorithm continuously adjusts the recommended number of vehicles based on changing demand patterns, battery health data, and cost parameters. This dynamic adjustment ensures the fleet size remains adequate for productivity requirements while avoiding unnecessary vehicle accumulation that would increase costs.
Data Source
AI summary
An information processing device acquires movement plan information including a total moving distance per unit period of a plurality of movable bodies; acquires relationship information indicating a relationship between a moving distance per the unit period of the movable body and a deterioration degree of the battery; acquires first cost information for calculating a first cost varying with the number of the movable bodies and second cost information for calculating a second cost varying with the deterioration degree of the battery; and determines a planned number of movable bodies, the planned number being the number of the movable bodies for use in a movement plan and being the number of the movable bodies having a total of the first cost and the second cost satisfying a predetermined requirement, on the basis of the movement plan information, the relationship information, the first cost information, and the second cost information.