CellPassport: Cloud-based EIS battery diagnostic system for Predictive Battery cell state and Smart Energy management Integration
Tarek Mahmoud
Samy Ibrahim
Tarekm@ieee.org
Keywords: CellPassport; Predictive
Maintenance; Smart EMS; Dynamic Pricing; Real-Time EIS; State of Health (SoH); State
of X (SoX); Battery Cells; Cell Reuse;
Second-Life Applications; Cloud Analytics; Digital Twin; Failure Prediction
Introduction
The
CellPassport concept is closely aligned with the European Union’s Battery 2030+
Roadmap [1], which advocates for digital twins, cell-level sensing, and
intelligent data-driven maintenance strategies to support a circular battery
value chain.
Battery health and reliability are critical to applications like EVs,
residential, and utility-scale energy storage systems (BESS). The CellPassport
solution integrates real-time, non-invasive Electrochemical Impedance
Spectroscopy (EIS) [2] with cloud analytics and digital twin modeling. It also
enables cell-level assessment for reuse and second-life applications—key for
circular economy strategies.
Smart
EMS systems require a continuous stream of real-time battery condition data,
alongside weather forecasts and load predictions. Relying solely on static
battery models is insufficient for dynamic optimization [3], [4]. By
integrating CellPassport, the EMS adapts charge cycles to current battery
health and electricity prices, shifting operations to cheaper periods when
possible and reducing lifecycle costs.
This
approach supports predictive diagnostics, enhances maintenance planning, and
reduces lifecycle costs. The CellPassport solution integrates real-time,
non-invasive Electrochemical Impedance Spectroscopy (EIS) with cloud analytics
and digital twin modeling. This enables predictive battery diagnostics at the
individual cell level, significantly enhancing maintenance planning, cost
efficiency, and overall reliability.
Real-Time EIS and
CellPassport Methodology
CellPassport
uses the available measurements of operational voltage and current data through
Fast Fourier Transform (FFT)-based EIS analysis, with novel approach
eliminating the need for external frequency perturbation and relaying only on
the battery management systems (BMS) sensors, and make computational overhead
on the cloud through the diginal twin and making a full database for individual
cells including all SoX of the cell, facilitating more data analysis and
recording the history of the cell, as shown in Figure1.
CellPassport data can be used to have failure
prediction of the battery cell, as well as integration into existing systems
such as EMS systems.
Figure 1 CellPassport diginal twin
Cloud-Based
Predictive Maintenance
The
impedance data from each battery cell is securely transmitted to a cloud
platform where advanced analytics predict battery degradation and potential
failures. By forecasting the State of Health (SOH) accurately, operators can
schedule proactive maintenance, avoiding costly downtime and unplanned outages.
Smart EMS
Integration and Cost Optimization
CellDigital
twin’s real-time impedance monitoring data is seamlessly integrated into a
Smart Energy Management System (EMS). The system dynamically adjusts charging
cycles based on battery condition and real-time electricity pricing. For
example, if the impedance analysis indicates accelerated degradation and the
current electricity price is lower, the EMS can strategically avoid spending a
charging cycle of the BESS and have the energy from the grid during the
lower-cost periods, significantly reducing Capital expenditure (CapEx).
The EMS is a finite state machine the controls the
transition between different states based on the AI model that takes real time
data from the plant or from the Celldigital twin, as shown in Figure 2. This
accurate
Figure 2 Smart finite state machine EMS
Key Applications
The CellPassport solution benefits various battery-dependent
applications, including operational use and post-use recovery strategies:
·
EVs: SoX monitoring of
battery cells is vital for both the end user and car manufacturers. The
collected data during the lifetime of the battery is also needed for second
life and reperposing of the cells after reaching the end of its EV application
life.
·
Residential Energy Storage:
Optimizing home battery lifespan and energy costs with predictive analytics.
·
Utility-Scale BESS:
Improving grid stability, frequency regulation efficiency, and cost-effective
operation by integrating failure predictions and maintenance planning into EMS
strategies. Aiding smart EMS systems to exploit variable electricity prices
through cost tradeoffs and reduce the
CapEx.
·
Cell Repurposing &
Second-Life Use: Identifies viable used cells for reuse, optimizing lifecycle
value and sustainability.
Benefits of
CellPassport Integration
·
Predictive Maintenance:
Reduces unexpected failures, enhancing reliability.
·
Cost Efficiency: Optimizes
operational decisions based on dynamic pricing and battery health.
·
Scalable and Secure: Cloud
infrastructure ensures scalability, data security, and interoperability.
Conclusion
This aligns with
European Union goals in the Battery 2030+ Roadmap, which emphasize smart
diagnostics, second-life enablement, and lifecycle-aware battery operation.
CellPassport represents a transformative approach to battery health management,
utilizing cloud-analyzed EIS data to enable predictive maintenance,
cost-effective operational planning, and enhanced reliability across diverse
battery applications. Its integration with Smart EMS systems allows operators
to make informed decisions based on real-time battery condition and dynamic
electricity pricing, ultimately extending battery life and reducing operational
costs.
About Us
CellPassport
is developed by Dr. Tarek Mahmoud Samy Ibrahim and colleague, researchers and
engineers from AAU Energy and Enginius.org, with a focus on battery health
diagnostics, EMS strategies, and digital twin systems. The solution builds on
years of academic and industrial research to deliver real-time, scalable
battery intelligence solutions that empower predictive maintenance, lifecycle
extension, and smart energy integration.
References
[1] Battery 2030+ Roadmap. European Commission. Available: https://battery2030.eu/wp-content/uploads/2021/08/c_860904-l_1-k_roadmap-27-march.pdf
[2] T. Ibrahim, M. U.
Tahir, V. Knap, and D. I. Stroe, "Degradation Analysis of Lithium-ion
Capacitors based on Electrochemical Impedance Spectroscopy," 2024 IEEE
ECCE.
[3] T. Ibrahim, T.
Kerekes, D. Sera, A. Lashab, and D. I. Stroe, "Lithium-ion supercapacitors
and batteries for off-grid PV applications: lifetime and sizing,"
Batteries, vol. 10, no. 2, p. 42, 2024.
[4] T. Ibrahim, T. Kerekes, D. Sera, S. S. Mohammadshahi, and
D. I. Stroe, "Sizing of hybrid supercapacitors and lithium-ion batteries
for green hydrogen production from PV in the Australian climate,"
Energies, vol. 16, no. 5, p. 2122, 2023.