Cell-level trend warnings
Analyze voltage, temperature and current measurements over time to investigate abnormal heating and potential risks.
DDS
Understand health.Anticipate risk.Protect value.
Intelligent battery health diagnostics
From cell state estimation and health assessment to trend warnings, turn distributed battery data into informed maintenance decisions.
HEALTH INSIGHTCell-level health insightSOC · SOH · RUL
EARLY RISK INSIGHTRisk trendsVoltage · Temperature · Resistance
Physics models × AI analysisCell · Pack · RackLifecycle assessment
Power Anywhere.
Always On.
INSIGHT BEFORE ACTION
Every decisionstarts with the cell.
Start with reliable data. Understand the cell, step by step.
Cell consistency analysis
Compare voltage, temperature and SOC across cells to identify outliers and follow consistency changes and capacity-loss risks.
A longer-term asset perspective
Review changes in DC internal resistance and ageing patterns to assess remaining-life trends and support maintenance and replacement plans.
PHYSICS × INTELLIGENCE
Two perspectives.One cell.
Physics explains behavior. Data reveals change. Together, they give health and safety assessments greater context.
PHYSICS
Second-order RC · Resistance estimation · Electrochemical featuresPhysics-based models
Use equivalent circuits and parameter estimation to understand changes inside the battery.
AI
Time series · Feature clustering · Outlier detectionIntelligent analysis
Combine time-series analysis, feature clustering and anomaly detection to find deviations in operating data.
Physics-based models
- Second-order RC model
- A two-RC network captures ohmic resistance, electrochemical polarization and concentration polarization to reconstruct voltage response under changing operating conditions.
- Resistance identification
- Identify DC resistance (DCR) online from charge and discharge data, tracking changes in resistance and battery aging over time.
- Electrochemical signatures
- Extract electrochemical fingerprints from incremental capacity (IC) curves and voltage relaxation to relate changing signatures to aging mechanisms.
Intelligent analysis
- Time-series analysis
- Model trends and cycles in long-term voltage, temperature and resistance records to reveal gradual changes in performance.
- Feature clustering
- Cluster cells by multiple features to identify differences in consistency and group behavior, providing a basis for comparing similar cells.
- Outlier detection
- Combine statistical and density-based analysis to flag cells that deviate from their peers, supporting anomaly screening and early warning.
One diagnostic approach across three levels
- Cell
- Pack
- Rack
BATTERY HEALTH
Understand today. Plan for what comes next.
Connect state estimation, parameter analysis and trend assessment to support battery lifecycle management.
Estimate SOC for every individual cell using an extended Kalman filter (EKF), continuously refining charge estimates with operating data.
Combine AI with physics-based models to estimate SOH for every individual cell and track differences in health and degradation.
Combine AI with physics-based models to estimate RUL for every individual cell, using aging trajectories to assess remaining useful life.
Combine battery models and operating data to assess SOC and SOH, informing charge and discharge strategies and maintenance planning.
HEALTH TRAJECTORY
Illustrative trendSOHBattery health and lifetime trends
Observed historyCurrentLifetime outlook
Available capacityCapacity change and lossDynamically estimate the available capacity of every cell under its current state and operating conditions to inform charge and discharge planning.Stored energyCharge, discharge and energy statisticsDynamically estimate the available energy of every cell, assessing deliverable energy from its operating state to support energy management.DC internal resistanceCell parameters and lifetimeIdentify resistance parameters online for every cell, tracking aging and differences across the population to inform health diagnostics.
EARLY RISK INTELLIGENCE
Follow the direction of change.
Review historical trends and graded warnings to prioritize further investigation and maintenance.
Risk trends
- NormalMonitor
- AbnormalCell consistency
- WarningAssess
- CriticalRespond
Lithium-plating indicators
Assess lithium-plating risk during cold or high-rate charging. Voltage-plateau distortion, new dV/dQ peaks and abnormal relaxation help evaluate plating and inform charging strategies.
Internal short-circuit indicators
Investigate short-circuit risks associated with separator defects, lithium dendrites or contamination. Combine voltage-drop rate, open-circuit voltage (OCV) deviation and temperature rise to track progression and support graded thermal-runaway warnings.
Self-discharge indicators
Analyze voltage relaxation and voltage-drop rate (K value) during extended rest to distinguish normal self-discharge from abnormal losses and screen for cells with possible micro-shorts.
Electrical abuse analysis
Use shifts in the upper voltage limit, resistance growth, increased polarization and coulombic-efficiency deviations to assess past overcharge or overdischarge stress and identify cells needing attention.
Thermal abuse analysis
Relate temperature-rise rate, cell-to-cell temperature differences, local hot spots and temperature–resistance coupling to detect early thermal anomalies and inform thermal management.
Voltage and temperature prediction
Use second-order RC and temperature-rise models to project voltage and temperature under future operating conditions, informing power limits, current reduction, balancing and cooling.
Solution concept. Data access, diagnostic functions and deployment scope require project confirmation. Diagnostics assist qualified personnel and do not replace BMS protection or site safety procedures.
CELL CONSISTENCY
Spot cells drifting from the group.
Compare cells within a group to identify outliers and weak performers. Temporal consistency, group safety entropy and feature clustering inform balancing and maintenance plans.
Expert analysis↗Reference cellsFocus cell
Maximum / MinimumRange / Standard deviationTrends / Clustering
Expert analysis
Cell signal map
From parameters to physical behavior
ONE CONNECTED WORKSPACE
Every cell. One connected view.
Connect distributed storage sites. Bring monitoring, analysis, reports and warnings into one workspace.
DDSBattery health management
Illustrative interfaceMicrogrid demonstration site / Rack 01 / Pack 03
Example dataCell health overview
Reference cellsFocus cell
From the system to a single cell.Select a cell to explore its illustrative status
Locate equipment and cells
Cell 24
Review voltage outliers- SOC
- State of charge
- SOH
- Assess state and ageing together
- RUL
- Lifetime outlook
Trace the asset path, then review the alert, related measurements and historical curves.
Turn diagnostic evidence into action.Traceable diagnostic resultsConcept illustration · Not live operating data
Alert records and threshold settingsData export and analysis archiveRoles, permissions and activity logs
FROM INSIGHT TO ACTION
Put diagnosis into daily operations.
From continuous monitoring to verification, give every assessment a next step.
- 01
Monitor
Bring measurements together and track cell status.
- 02
Risk grading
Review anomalies and trends. Set investigation priorities.
- 03
Assess
Locate equipment and cells. Arrange inspection and maintenance.
- 04
Verify
Check results and retain diagnosis and maintenance records.
DDS FOR OFF-GRID & MICROGRIDS
Edge and cloud, connected.For off-grid systems and microgrids.
Continuous diagnosis on site, centralized analysis in the cloud. Battery health management for off-grid systems and microgrids.
Site data
BMS / EMS
Voltage · Current · TemperatureData access
Edge diagnostics
Data processing · Model analysis
Local diagnostics · Model updates · Resume transfer after outagesShared diagnostic data
Cloud platform
Unified multi-site management
SaaS service / Private deploymentProtocol compatibility assessed per siteModbus TCPIEC 104IEC 61850
BATTERY HEALTH, THROUGHOUT LIFE
Turn insight into action.
Discuss data availability, deployment boundaries and your diagnostic priorities.
Discuss a DDS project