Battery Reservation System with Predicted Return Charge
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Solution Overview
Problem
Conventional battery reservation systems cannot accept reservations for battery packs that are scheduled to be returned, limiting flexibility and options for users.
Innovation Solution
A battery reservation device that includes a battery state acquisition component, scheduled return time acquisition component, remaining battery charge prediction component, charging speed acquisition component, and reservation acceptance component, allowing reservations for battery packs scheduled to be returned based on their predicted remaining charge and charging speed at the time of return.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the conventional reservation system only accepts reservations for battery packs already at the battery service location, then the system complexity remains simple, but the reservation flexibility and available options for users are limited
Solution Approach 1:
The system performs preliminary calculations of remaining battery charge and estimated return times before reservations are made. By pre-computing these parameters for battery packs in use, the system can proactively identify available batteries and notify users in advance, enabling reservations without requiring complex real-time tracking during the reservation moment
Solution Approach 2:
The system continuously monitors battery pack status (charge levels, location, usage patterns) and feeds this information back to update reservation availability. This feedback mechanism allows the system to dynamically adjust which batteries can be reserved based on their projected return status, increasing flexibility while using automated data collection rather than manual complexity
2Quantity of substance
If the system accepts reservations for battery packs scheduled to be returned, then the number of available reservation options increases, but the accuracy of predicting remaining battery charge becomes more critical
Solution Approach 1:
The system applies conservative estimation by calculating remaining battery charge based on minimum expected charge depletion rather than exact usage. This partial action approach ensures that even if predictions are slightly off, the system maintains enough buffer to fulfill reservations, prioritizing availability over precise prediction
Solution Approach 2:
The system monitors multiple parameters (current charge level, historical discharge rates, seasonal variations, usage patterns) and adjusts predictions based on changing conditions. By tracking how these parameters evolve over time, the system improves prediction accuracy for returning battery packs without requiring perfect single-point measurements
3Reliability
If the system processes reservations for returning battery packs in real-time, then reservation accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The system pre-calculates estimated return times and projected remaining charge for all battery packs in use before reservation requests arrive. This preliminary processing creates a ready pool of predictable battery availability information, allowing fast reservation decisions without real-time computation delays
Solution Approach 2:
The system uses dynamic time windows for reservation acceptance, adjusting processing intensity based on demand. During peak reservation periods, it relies on pre-computed estimates; during off-peak times, it performs more detailed real-time calculations, balancing accuracy requirements with processing time constraints
Data Source
AI summary
A battery reservation device (10) comprises a battery state acquisition component (12), a scheduled return time acquisition component (13c), a remaining battery charge prediction component (14), a battery station information acquisition component (15), and a reservation acceptance component (17). The battery state acquisition component (12) acquires the remaining battery charge of a battery pack (1) scheduled to be returned to a battery station (30a). The scheduled return time acquisition component (13c) acquires the scheduled return time of the battery pack (1). The remaining battery charge prediction component (14) predicts the remaining battery charge at the time of return on the basis of the current remaining battery charge of the battery pack (1) and the scheduled return time. The battery station information acquisition component (15) acquires the charging speed of a charger (31). The reservation acceptance component (17) accepts a rental reservation for the battery pack (1) scheduled to be returned on the basis of the remaining battery charge at the time of return and the charging speed of the charger (31).