🎓 Lesson 16
D5
SoH Monitoring Algorithms for Underground LHDs
SoH monitoring algorithms are software tools that estimate how much battery life remains in an underground LHD’s battery by analyzing real-time voltage, temperature, and current data.
🎯 Learning Objectives
- ✓ Calculate SoH using coulombic counting with drift correction
- ✓ Analyze voltage relaxation curves to estimate capacity fade in LHD duty cycles
- ✓ Explain how temperature-dependent aging models improve SoH accuracy for underground thermal profiles
- ✓ Apply IEEE 1188–2023 validation protocols to verify SoH algorithm performance
📖 Why This Matters
In underground mines, Battery-Electric Load-Haul-Dump (LHD) machines operate in confined, high-temperature, dusty environments with limited charging windows. A sudden 20% SoH drop can cause mid-shift battery failure—stranding equipment 2 km from the portal, halting production, and risking safety. Unlike surface EVs, underground LHDs cannot rely on GPS or cloud-based AI; SoH algorithms must run onboard, in real time, with <5% error—even after 1,500 charge cycles. Mastering these algorithms directly impacts fleet uptime, battery replacement budgets, and decarbonization timelines.
📘 Core Principles
SoH estimation rests on three interdependent pillars: (1) Capacity-based SoH, defined as SoH(%) = (Q_actual / Q_nominal) × 100, where Q_actual is the battery’s present usable capacity; (2) Impedance-based SoH, tracking internal resistance growth correlated with electrode degradation; and (3) Model fidelity—physics-informed models (e.g., single-particle + SEI growth) suit controlled lab conditions, while adaptive extended Kalman filters (EKF) dominate field-deployed LHD systems due to robustness against sensor noise and dynamic load transients. Crucially, underground LHDs experience unique stressors: sustained 45°C ambient temperatures, frequent 0.5–2C discharge pulses during mucking, and regenerative braking energy absorption variability—each demanding SoH algorithms calibrated to mine-specific duty cycles, not automotive profiles.
📐 Coulombic Counting with Voltage-Based Drift Correction
Coulombic counting estimates capacity change by integrating current over time—but drifts due to sensor offset and self-discharge. Voltage relaxation correction anchors the estimate at known open-circuit voltage (OCV) points (e.g., post-idle periods), mapping OCV to SoC via a validated lookup table, then back-calculating true capacity. This hybrid method achieves <3% SoH error over 500 cycles in LHD applications per NORAM Mining’s 2023 fleet study.
💡 Worked Example
Problem: An underground LHD battery (nominal capacity = 360 Ah) completes a shift. Coulombic integration reports 342 Ah discharged, but post-shift 30-min idle shows OCV = 3.245 V. Per the validated OCV-SoC table, this corresponds to SoC = 18.2%. The battery was charged to 100% pre-shift. Calculate corrected SoH.
1.
Step 1: Determine expected discharge depth: 100% − 18.2% = 81.8% of nominal capacity → 0.818 × 360 Ah = 294.5 Ah
2.
Step 2: Coulombic count reported 342 Ah discharged — indicating integrator drift: 342 − 294.5 = +47.5 Ah overestimation
3.
Step 3: Correct actual delivered capacity: 342 Ah − 47.5 Ah = 294.5 Ah → SoH = (294.5 Ah / 360 Ah) × 100 = 81.8%
Answer:
The corrected SoH is 81.8%, confirming 18.2% capacity loss—within acceptable range for 750 cycles (typical range: 80–85% SoH).
🏗️ Real-World Application
At Newmont’s Boddington Mine (WA), LHDs equipped with EKF-based SoH algorithms (developed with EnerSys and integrated into Sandvik LH517i BMS firmware) reduced unplanned battery replacements by 37% over 18 months. The algorithm fused cell-level voltage, pack temperature gradients (measured at 6 zones), and duty-cycle-specific discharge rate coefficients. When SoH dropped below 75%, the system triggered automated low-power mode and scheduled depot charging—preventing thermal runaway events linked to >20% capacity loss in prior NiMH fleets. Field validation confirmed ±2.3% median absolute error vs. lab reference discharge tests.
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