ToolFusion
📋 Case Study

Coal Mine Battery Thermal Management at BHP Mt Arthur

Battery pack thermal runaway risk during 12hr shifts; SOC estimation drift >8% after 4 hours

🏗️ Project Overview

Deployment of 32 electric autonomous haul trucks in subtropical climate (42°C peak ambient)

🎯 Challenge

Battery pack thermal runaway risk during 12hr shifts; SOC estimation drift >8% after 4 hours

🔧 Design Approach

Active liquid-cooled battery packs with predictive thermal model fed by pit microclimate sensors and duty-cycle AI

📐 Design Diagram

BHP Mt Arthur — Battery Thermal Management SystemSensors(microclimate)AI Engine(duty-cycle)Model(predictive thermal)CoolantQ = 18.4 L/minActive Liquid-Cooled Battery PackSOC correction: ±0.7% (Kalman-filtered f(T, current, age))Thermal Runaway RiskSOC Drift >8% (4h)

AI-generated project design illustration

📐 Key Calculations

Coolant Flow Rate Requirement

Q = m·c·ΔT / t
Result: 18.4 L/min
Maintains cell delta-T <2.1°C across pack

SOC Estimation Correction Factor

f(T, current, age)
Result: Kalman-filtered → ±0.7% error
Enables accurate state-of-charge handover between shifts

📊 Results

Battery degradation rate reduced by 41%; unplanned thermal shutdowns eliminated; shift handover SOC accuracy improved from 82% to 99.3%

💡 Lessons Learned

  • •Thermal models must be calibrated per battery batch, not per model
  • •Microclimate sensors must be co-located with truck charging bays and idle zones

✅ Key Takeaways

  • 1Thermal models must be calibrated per battery batch, not per model
  • 2Microclimate sensors must be co-located with truck charging bays and idle zones