EV Charging Recommendations for Avoiding Unplanned Trip Stops
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Solution Overview
Problem
Electric vehicles often require unplanned charging stoppages during trips due to insufficient battery charge, causing inconvenience to users and disrupting the driving schedules of commercial vehicle drivers, leading to time and resource losses.
Innovation Solution
An electric vehicle charging recommendation system that provides proactive charging recommendations based on vehicle, driver, and trip information, optimizing energy transfer and timing to ensure uninterrupted travel without unplanned stops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle battery is charged multiple times during a long trip, then the vehicle can maintain operation, but unplanned charging stoppages occur causing inconvenience and time loss
Solution Approach 1:
The system performs preliminary action by determining and recommending charging locations and energy amounts before the vehicle battery becomes depleted. The server calculates optimal charging parameters in advance based on trip information, vehicle battery capacity, and energy consumption patterns, enabling drivers to plan charging stops proactively rather than reactively when battery level drops
Solution Approach 2:
The system implements feedback by continuously monitoring vehicle battery state of charge levels and comparing them against predicted consumption rates. The server receives real-time battery status updates and adjusts charging recommendations dynamically, providing closed-loop control that adapts to actual driving conditions and battery performance
2Loss of time
If charging is scheduled during trip planning, then unplanned stoppages are reduced, but trip flexibility and driver autonomy are limited
Solution Approach 1:
The system enables self-service by empowering drivers with personalized charging recommendations rather than imposing rigid schedules. The driver receives suggested charging locations and energy amounts but maintains full control to accept, modify, or ignore recommendations based on personal preferences, urgent destinations, or changing trip conditions
Solution Approach 2:
The system applies dynamics by providing adaptive, real-time charging recommendations that adjust to changing trip conditions. Rather than static pre-planned charging schedules, the server continuously updates recommendations based on current battery state, actual energy consumption, traffic conditions, and driver behavior patterns, allowing flexible adaptation throughout the trip
3Productivity
If optimal charging parameters are calculated and provided, then charging efficiency is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system uses an intermediary approach by introducing a server as a centralized computational hub that handles complex calculations. Rather than requiring sophisticated onboard vehicle computers, the server performs energy consumption analysis, charging parameter optimization, and route planning, reducing the computational burden on vehicle systems while maintaining high charging efficiency
Solution Approach 2:
The system applies segmentation by dividing the charging optimization task into distinct functional modules: trip information collection, battery state monitoring, energy consumption calculation, charging parameter determination, and recommendation delivery. This modular architecture simplifies system implementation and maintenance while enabling efficient charging optimization
Data Source
AI summary
An electric vehicle charging recommendation method is disclosed. The method may include obtaining a vehicle information associated with a vehicle, a driver information, and a vehicle trip information. The vehicle information may include a current state of charge (SOC) of a vehicle battery. The method may further include determining a distance to be travelled by the vehicle from a vehicle current location based on the vehicle trip information. The method may further include determining an expected SOC required to travel the distance from the vehicle current location based on the vehicle information, the driver information and the vehicle trip information. The method may additionally include transmitting a recommendation notification to a user device to charge the vehicle battery when the expected SOC may be greater than the current SOC.