Mobile Charging Bot Deployment Using IoT EV Battery Data
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Solution Overview
Problem
The challenges of EV range anxiety and lack of accessible charging infrastructure create uncertainty for long-distance travel, necessitating a proactive mobile charging system that dynamically monitors and deploys charging units based on real-time vehicle data.
Innovation Solution
A proactive mobile charging deployment (PMCD) system that monitors IoT-enabled electric vehicles, selects and deploys mobile charging units to predicted or current charging needs, and notifies users of available units, using dynamic IoT data analysis and navigation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a proactive mobile charging system is deployed to reduce range anxiety and ensure timely charging availability, then the charging experience and user convenience are improved, but the system complexity and infrastructure requirements increase
Solution Approach 1:
The system performs preliminary actions by proactively monitoring EV battery status and deploying mobile charging units before the vehicles reach critical low-battery states. The PMCD bot continuously analyzes IoT data from EVs and preemptively dispatches MCUs to predicted or current charging needs, preventing range anxiety rather than reacting after depletion occurs.
Solution Approach 2:
The proactive mobile charging deployment system acts as an intermediary between EVs and fixed charging infrastructure. The PMCD bot serves as a mediator that receives real-time battery status data from IoT-enabled EVs, processes this information, and coordinates the deployment of mobile charging units as a flexible intermediate charging solution between the vehicle and permanent charging stations.
2Reliability
If mobile charging units are deployed based on real-time IoT data monitoring, then charging availability is improved, but the loss of time for data processing and deployment coordination increases
Solution Approach 1:
The system implements continuous feedback loops by monitoring real-time battery status data from IoT-enabled EVs and using this feedback to dynamically adjust mobile charging unit deployment. The PMCD bot receives ongoing updates on battery charge levels, geographic location, and charging needs, continuously refining deployment decisions based on the latest data to optimize charging availability while minimizing response time.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for deploying mobile charging units for electric vehicles. The embodiment may include receiving data from a set of Internet-of-Things (IoT) enabled electric vehicles (EVs). The data includes a current percentage of remaining battery power for an IoT enabled EV of the set of IoT enabled EVs. In response to determining that the current percentage of remaining battery power for the IoT enabled EV falls below a specified EV battery threshold, the embodiment may include selecting at least one IoT enabled mobile charging unit (MCU) from a set of IoT enabled MCUs. The embodiment may include deploying the selected at least one IoT enabled MCU to a determined geographic location. The embodiment may include notifying a user of the IoT enabled EV of availability of the selected at least one IoT enabled MCU at the determined geographic location.