Mobile EV Charging Scheduling for Fleet Downtime Reduction
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Solution Overview
Problem
The transition to electric machines presents challenges in managing battery life, optimizing charging infrastructure, and ensuring operational efficiency, leading to increased downtime and operational costs due to inefficient charging strategies and inadequate battery management, compounded by limited high-fidelity battery data availability.
Innovation Solution
An EV management system that utilizes predictive models and charge scheduling algorithms to optimize charging operations by determining battery state of charge, generating real-time notifications, and simulating charge scheduling to minimize downtime and reduce costs through smart scheduling and resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional charging infrastructure is used, then charging capacity is limited, but operational downtime increases
Solution Approach 1:
The patent implements dynamic charge scheduling that adapts to real-time battery state of charge predictions and operational needs. The system continuously updates charging schedules based on predicted battery degradation and operational requirements, transforming static charging infrastructure into a dynamic system that optimizes charging timing and duration to minimize downtime while protecting battery life.
Solution Approach 2:
The system performs preliminary actions by predicting future battery state of charge levels and scheduling charging operations in advance. The charge scheduling algorithm anticipates when batteries will need charging based on historical data and operational patterns, arranging charging during optimal times before critical low-battery situations occur, thereby preventing operational downtime.
2Reliability
If frequent charging is performed, then battery life is extended, but operational time decreases
Solution Approach 1:
The patent changes the parameter of charging frequency from fixed to variable based on predicted battery degradation rates. The system monitors and analyzes battery state of charge patterns, operational intensity, and environmental conditions to dynamically adjust charging parameters. This allows the system to extend battery life by optimizing when and how much to charge, rather than following a fixed charging schedule that would reduce operational time.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring battery state of charge, operational patterns, and charging effectiveness. This feedback loop allows the charge scheduling algorithm to learn from actual battery behavior and adjust future charging schedules to maximize battery life while minimizing impact on operational time, creating a self-optimizing system.
3Adaptability or versatility
If more mobile charging stations are deployed, then charging availability improves, but system complexity increases
Solution Approach 1:
The patent creates a universal charge scheduling system that can manage multiple mobile charging stations across diverse locations and equipment types through a single platform. The system provides multi-functional capabilities including predictive analytics, schedule optimization, battery health monitoring, and real-time coordination, allowing one system to handle various charging scenarios without proportionally increasing complexity.
Solution Approach 2:
The charge scheduling system acts as an intermediary layer between mobile charging stations and battery-powered equipment. This intermediary coordinates charging requests, optimizes station utilization, and manages scheduling logic centrally, thereby improving charging availability across the fleet without requiring each individual station to have complex autonomous decision-making capabilities.
4Productivity
If predictive models are implemented, then charging efficiency improves, but data processing requirements increase
Solution Approach 1:
The patent applies partial action by implementing predictive models that focus on the most critical features and patterns relevant to charge scheduling decisions. Rather than processing all possible battery data comprehensively, the system identifies and analyzes key predictors of battery degradation and operational needs, achieving sufficient charging efficiency improvement without the computational burden of exhaustive data processing.
Data Source
AI summary
A technique is directed to methods and systems for managing electric vehicle charging. The electric vehicle management system can determine a battery state of charge using a data driven model and send geolocation push notifications regarding battery charging states to the electric vehicle, operators, and/or fleet managers. The electric vehicle management system can determine the routes for available chargers, the transit time, battery charging time and rate, and peak load costs for a charging an electric vehicle. A user can access the electric vehicle management system via an application on a user device.