Electric Work Machine Allocation Using Battery Degradation Prediction
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Solution Overview
Problem
Conventional power supply systems for construction machinery do not consider battery degradation variation, leading to inefficient battery operation and reduced lifespan.
Innovation Solution
An operation assistance device comprising a server that allocates electric work machines to work sites based on workload and battery degradation states, predicting and minimizing battery degradation by optimizing work site assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If batteries are allocated to work sites based on workload plan alone, then operation efficiency and usage efficiency are improved, but battery degradation increases and life decreases
Solution Approach 1:
The system changes the allocation parameter from workload alone to a composite parameter combining workload and battery degradation state. The server calculates predicted degradation states for different work sites and uses these predictions to make allocation decisions, thereby changing the decision-making parameter to include battery health projections.
Solution Approach 2:
The system implements feedback by continuously monitoring actual battery degradation states and comparing them with predicted degradation states. The server uses this feedback information to adjust future allocations, creating a closed-loop system that learns from actual battery performance to optimize future work site assignments.
2Device complexity
If batteries are allocated without considering degradation state, then allocation simplicity is maintained, but battery degradation suppression is insufficient
Solution Approach 1:
The system performs preliminary action by calculating predicted degradation states before making allocation decisions. The server proactively computes how battery degradation will progress under different work site conditions and uses these predictions to pre-determine optimal allocations, preventing degradation rather than reacting to it.
Solution Approach 2:
The server acts as an intermediary between workload requirements and battery allocation decisions. It mediates by processing both workload information and predicted degradation data to create optimized allocation plans, serving as an intermediate calculation layer that balances operational needs with battery health preservation.
Data Source
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AI summary
This disclosure provides an operation assistance device capable of suppressing degradation and increasing a life of battery of an electric work machine. An operation assistance device 1 allocates a plurality of electric work machines EWM1, EWM2, ..., EWMn to a respective plurality of work sites WS1, WS2, ..., WSn. The operation assistance device 1 includes an information storing section 11 that stores each piece of information on a workload of the plurality of work sites WS, a state acquiring section 12 that acquires a degradation state of a battery B mounted in each of the electric work machines EWM, a state predicting section 13 that calculates a predicted degradation state of the battery B when each of the electric work machines EWM works at each of the work sites WS, and an operation planning section 14. The operation planning section 14 selects the work sites WS in a descending order of the workload based on the information stored by the information storing section 11, and allocates a specific electric work machine EWM among the plurality of electric work machines EWM1, EWM2, ..., EWMn to the selected work site WS based on the predicted degradation state.