Battery Prognostics Using Route-Segment Energy Feedback
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Solution Overview
Problem
Battery electric machines (BEMs) face challenges in predicting energy usage and monitoring battery health due to unpredictable road conditions, which can lead to energy depletion, machine disablement, and increased costs or time due to re-routing, especially in remote job sites where maintenance access is limited.
Innovation Solution
A control system that monitors battery health and energy consumption by comparing present performance data with historical data from similar travel route segments, using sensors to gather real-time and historical information on battery state-of-charge, state-of-health, and terrain conditions, and adjusts maintenance schedules based on threshold differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If battery electric machines operate in remote job sites with unpredictable road conditions, then operational flexibility and task completion capability are improved, but energy consumption increases and battery health monitoring becomes more difficult
Solution Approach 1:
The system performs preliminary actions by comparing historical energy consumption data with real-time sensor data before the machine actually depletes energy or encounters critical battery issues. The control system continuously monitors battery state-of-charge and state-of-health, and compares current travel route segments with historical data to predict energy requirements in advance, allowing proactive route adjustments or maintenance scheduling.
Solution Approach 2:
The system implements feedback by continuously monitoring battery parameters (state-of-charge, state-of-health, temperature) and comparing actual energy consumption against historical data and predictions. The control system uses this feedback loop to adjust operational parameters, alert operators to potential energy depletion risks, and optimize future route planning based on learned patterns from previous trips.
2Productivity
If the machine traverses unpredictable road segments with soft underfoot conditions, then task completion capability is maintained, but energy depletion and machine disablement risk increases
Solution Approach 1:
The system performs preliminary risk assessment by comparing current travel route conditions with historical data before the machine encounters problematic segments. The control system identifies patterns in historical energy consumption during similar road conditions and alerts operators in advance, allowing them to adjust routes, reduce speed, or prepare for energy depletion scenarios before they occur.
Solution Approach 2:
The system provides beforehand cushioning by maintaining a safety margin in battery state-of-charge based on historical variability in energy consumption for similar routes. The control system calculates predicted energy requirements with confidence intervals and ensures the machine operates within safe energy thresholds, cushioning against unexpected energy depletion from unpredictable road conditions.
3Reliability
If real-time monitoring of battery health and energy consumption is implemented, then predictive maintenance and operational optimization are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The control system performs multiple functions using a single integrated platform: it monitors battery state-of-charge, state-of-health, and temperature; collects sensor data from the machine and environment; compares historical and real-time data; predicts energy consumption; and generates maintenance alerts. This multi-functional approach reduces overall system complexity compared to separate specialized systems for each function.
Solution Approach 2:
The system provides self-service by automatically comparing real-time battery and energy consumption data with historical data, identifying anomalies, and generating maintenance predictions without requiring external intervention. The control system autonomously processes sensor data, performs data matching across trips and routes, and alerts operators only when predictive maintenance actions are needed, reducing the burden on operators and external maintenance teams.
4Measurement precision
If historical data from multiple trips and routes is collected and compared, then accuracy of energy consumption prediction is improved, but data management complexity and processing time increase
Solution Approach 1:
The system segments historical data by travel route segments, machine load conditions, and environmental factors to enable targeted comparisons. Instead of processing all historical data uniformly, the control system divides data into relevant segments matching current trip conditions, significantly reducing processing time while maintaining prediction accuracy by comparing only analogous historical scenarios.
Solution Approach 2:
The system applies local quality by focusing data collection and comparison on specific relevant parameters and route segments rather than uniformly processing all data. The control system identifies and prioritizes critical data points (such as energy consumption during soft underfoot conditions or battery temperature trends) that have the greatest impact on prediction accuracy, reducing overall data processing requirements.
Data Source
AI summary
A control system is programmed for monitoring the health and charge of batteries used to power a battery electric machine (BEM) or other heavy equipment and determining when maintenance, service, or replacement should be performed for the batteries as a function of data related to travel route segments over which the BEM or other heavy equipment is operated. The control system is programmed to receive historical information mapping the performance and energy consumption of a BEM or other heavy equipment, such as battery state-of-charge, power usage, battery state-of-health, and number of charge cycles for a battery supplying power to the BEM or other heavy equipment operating over a travel route segment, and instruct an operator to replace or perform maintenance on the batteries if the difference between present and historical performance exceeds a threshold level.