IEEE Power & Energy Magazine - November/December 2020 - 96

Case Study
Predictive Maintenance Approaches for BESSs
This is an example application of predictive analytics on
real-time and historical BMS data to identify indicators
of unusual behavior. These indicators prompt alerts,
which ultimately lead to predictive maintenance.

Action Sequence
Process Site Data Trends:
Aggregated Cell Voltage, Battery
Current, and So on

Information Flow

No

Predictive Analysis

Yes
Run Predictive-Analytics Algorithm
On Site Data to Get Misbehavior/Weak
Battery Indicators

Misbehavior/Weak Indicator Detected?

Site Data Sent Over Secure
Internet Connections
Predictive-Analytics Engine

User Interface
With Periodic
Data Update

The service team
was alerted to this
pattern, and they
reviewed the
data closely,
subsequently
replacing a battery
module in this rack.

Rack Current (A)

150

Is Battery Module Suspected to Be
Misbehaving?
Take Actions Such as
* Put the Rack or System Out of Service
Until On-Site Inspection
* Schedule Site Visit to Check on the
Misbehaving Module

100
50
0

0

20,000

40,000

60,000

-50
Time (s)

-100

Cell Voltage (V)

In this example,
predictive analytics
identified a pattern
where the cell
voltage of a module
in rack number 1
was abnormally
deviating from the
other cell voltages.

Email Alerts

Daily and
On-Demand
Report
Generation

On Alert, Review the Asset's Data
and System-Usage Profile to Remove
False Positive and, If Required, Update
Predictive-Analytics Algorithm

Predictive Maintenance

Database
With Site
Data

Battery
Misbehavior
Indicator
Warning

Assert an Alarm Against Specific Asset

* Weak Module Reporting
* Temperature Analysis
* Capacity Fade Estimate

3.8
3.7
3.6
3.5
3.4
3.3
3.2
3.1
3

Results

VcellMax_Rack1
VminCell_Rack1
VcellMax_Rack2
VminCell_Rack2

Cell-Voltage Deviation Pattern
Triggered as Battery Misbehavior

0

20,000
40,000
Time (s)

Misbehaving battery modules were identified
using predictive analytics algorithms before
they could trigger safety hazards, negatively
impact system availability, or reduce the system
capacity. Furthermore, this approach helped
the service team plan maintenance visits more
efficiently in each geographic region, reducing
the warranty costs.

60,000

figure 4. A predictive maintenance case study from NEC Energy Solutions.

96	

ieee power & energy magazine	

november/december 2020



IEEE Power & Energy Magazine - November/December 2020

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - November/December 2020

Contents
IEEE Power & Energy Magazine - November/December 2020 - Cover1
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IEEE Power & Energy Magazine - November/December 2020 - Cover3
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