Theiss battery storage facility in Austria; licensed photograph

INDUSTRYNORTH / Battery storage

Protect the health behind every cycle.

Understand which racks behave differently, where efficiency is slipping, and how operating choices affect long-term battery value.

REAL PHOTOGRAPH · THEISS, AUSTRIA · BP 95 / CC BY 4.0Credits ↗

01 / THE CORE PACKAGE

A clear foundation. Value you can assess.

Start with the assets and data that matter. We agree on scope and success criteria before building.

INCLUDED WITHIN THE AGREED SCOPE

Battery storage
Core analytics

  • 01Thermal behavior adjusted for operating state
  • 02Voltage imbalance and peer analysis
  • 03Efficiency and auxiliary-load models
  • 04Ranked findings for engineering review

Data assessment, model validation, and delivery into your tools: Power BI, Tableau, custom applications or CMMS.

Extend where it makes sense.

Optional modules selected for value and data readiness.

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Validated state-of-health estimation

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Degradation-aware dispatch scenarios

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Warranty evidence and contract interpretation

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Capacity and augmentation planning

Data and technical scope

BMS cell/rack voltage and temperatures, current, state of charge, PCS/EMS data and auxiliary power. Health estimation also needs adequate operating history and capacity-test references.

Analytics supports engineering review and complements the BMS and site protections. A thermal anomaly is not a prediction of a fire. Dispatch scenarios require explicit market and operating assumptions.

02 / WHAT A FINDING LOOKS LIKE

Find the rack that behaves differently

R-08 reaches a +6.4°C temperature residual after adjusting for load, SOC and ambient conditions.

ILLUSTRATIVE EXAMPLE · NOT A CLIENT RESULT
Rack R-08 heats differently at comparable loadILLUSTRATIVE DATA
Rack R-08 heats differently at comparable load09.118.327.436.5°CD1D6D12Time
ExpectedObserved
D12 · Expected: 26.2 °C · Observed: 32.6 °C

Synthetic measurements and an illustrative expected profile. Select a point to inspect its values.

View the data
TimeExpected (°C)Observed (°C)
D12727.2
D227.727.9
D327.727.9
D427.127.3
D526.427.2
D626.227.8
D726.829.2
D827.530.7
D927.831.8
D1027.332.1
D1126.632.2
D1226.232.6
Locate the rack that needs investigationILLUSTRATIVE DATA
Locate the rack that needs investigation°CR-010.2R-020.4R-03-0.3R-040.1R-050.5R-060.1R-07-0.2R-086.4R-090.1R-100.3R-11-0.1R-120.2Rack
ExpectedObserved
R-12 · Expected: 0 °C · Observed: 0.2 °C

Temperature residual: observed minus expected after adjusting for current, SOC and ambient conditions.

View the data
RackExpected (°C)Observed (°C)
R-0100.2
R-0200.4
R-030-0.3
R-0400.1
R-0500.5
R-0600.1
R-070-0.2
R-0806.4
R-0900.1
R-1000.3
R-110-0.1
R-1200.2

03 / FROM EVIDENCE TO DECISION

Watch the AI workflow unfold.

Explore each stage. See the sources, the proposal, and the point where your team decides.

OPERATIONS INTELLIGENCESIMULATION
YOUR DATA. YOUR TEAM IN CONTROL.
R-08

Find the rack that behaves differently

+6.4°Csample rack residual
01

A signal worth investigating.

R-08 reaches a +6.4°C temperature residual after adjusting for load, SOC and ambient conditions.

Rack R-08 heats differently at comparable loadILLUSTRATIVE DATA
Rack R-08 heats differently at comparable load09.118.327.436.5°CD1D6D12Time
ExpectedObserved
D12 · Expected: 26.2 °C · Observed: 32.6 °C

Synthetic measurements and an illustrative expected profile. Select a point to inspect its values.

0:00 / 0:40

Illustrative data · sample documents · simulated workflow. These are not client results.

Read the complete story

Find the rack that behaves differently

R-08 reaches a +6.4°C temperature residual after adjusting for load, SOC and ambient conditions.

The persistent residual and peer comparison warrant cooling-system review [1][2]. Keep BMS protections in force. This signal does not establish a fire prediction or a confirmed cell defect.

  1. Attach normalized thermal and peer evidence
  2. Check service records with the responsible engineer
  3. Follow site procedures for any equipment intervention

A focused thermal investigation is ready for review.

Explore all 10 AI workflows ↗

04 / MANAGED ANALYTICS

A useful model needs care.

Equipment changes. Sensors drift. Operations evolve. We keep models aligned with that reality.

See model care in action ↗
  1. 01Monitor data quality and model drift
  2. 02Investigate changes before retraining
  3. 03Validate on held-out periods and known events
  4. 04Review versions, deployment and rollback
  5. 05Incorporate engineering feedback