IEEE Power & Energy Magazine - Grid Edge 2023 - 59

table 4. Representative installations of VRB.
Company
Prudent Energy
Rongke Power
UniEnergy
Technologies
Sumitomo Electric
Industries
Location
Zhangbei, China
Liaoning, China
Washington,
United States
Hokkaido, Japan
Basic Specification
2-MW/4-MWh VRB
5-MW/10-MWh VRB
1-MW/3.2-MWh VRB
15-MW/60-MWh VRB
Application
Balancing wind and solar power
Coupling with wind power (smoothing/
ramping, transient active support, etc.)
Load shifting, frequency regulation,
voltage regulation
Coupling with wind and solar power
(e.g., frequency fluctuation suppression)
EVs (e.g., Tesla's Model S); for home and utility energy storage,
Tesla's Powerwall and Powerpack adopt Li-ion battery
technology as well. The Tesla Gigafactory plans annual Li-ion
battery production capacity of 35 GWh and anticipates driving
down the cost of an Li-ion battery pack by more than 30%.
It is clear that the Li-ion battery is among the most promising
technologies in electrical energy storage, and its installed capacity
is well poised to increase.
Battery Management
Notwithstanding significant progress in battery chemistries
and materials, effective and dependable battery management
systems (BMSs) are still needed for the condition monitoring,
charge/discharge regulation, thermal control, cell balancing,
health prognosis, and safety protection of large-scale battery
energy storage. The absence of such systems is most likely
the reason for the conservative use of batteries (e.g., 20-50%
excess energy capacity, evoking undesirable weight, volume,
and costs). Without appropriate BMSs, catastrophic hazards and
premature failure, such as thermal runaway, may occur owing
to poor electrical and thermal operating and maintenance practices.
Battery management thus plays a critical role in the integration
of battery energy storage into the electric grid in terms
of performance, safety, reliability, and economy. As shown in
Figure 6, the main functions of a BMS include the following:
✔ data acquisition: the measurement and collection of
data on current, voltage, temperature, etc.
✔ state estimation: high-accuracy gauging of SOC, state
of power (SOP), state of health (SOH), state of temperature,
etc.
✔ charge/discharge control: charge current/voltage regulation,
power electronics interface, etc.
✔ cell balancing: passive or active state-of-charge and
voltage equalization
✔ thermal management: control of the maximum temperature
and temperature deviation among cells inside
a battery pack
✔ safety protection: hardware setup for avoiding overcharge/
overdischarge and overheating, as well as hardware/
software redundancy for proactive fault diagnosis/isolation
and alarming.
september/october 2017
Year of
Installation
2011
2012
2015
2016
The key enabling technologies for these functions in BMSs
are dedicated to battery modeling, SOC/SOP/SOH estimation,
cell balancing, charging control, and fault diagnostics. Because
batteries' internal states are generally inaccessible through present
in situ sensing techniques, models are established with the
aim of mimicking their dynamics and constituting a duplicate of
a real battery. Then, based on such models, battery state/parameter
behavior can be probed and manipulated by sophisticated
estimation and control approaches. Additionally, large-scale
battery storage invariably consists of thousands of cells in series
and parallel connections to satisfy power and energy requirements.
Challenging but critical BMS functions maximize the
potential of a whole battery system by balancing the charge on
cells and avoiding the bucket effect caused by the weakest cell.
The BMS also realizes system supervision by conducting fault
diagnosis and prognosis of the state of the cells.
Battery Modeling
Mathematical modeling of battery dynamics is fundamental
and is also an efficient tool for advanced battery management.
The related work can be categorized into three types:
white-, black-, and gray-box models. The white-box models
are developed from first principles and accurately capture
internal battery dynamics such as ion diffusion, intercalation
kinetics, and electric potentials. Mathematically, these models
are initially subject to partial-differential equations and
can be computationally intractable for online implementation.
Consequently, model-based algorithms in this field are often
fused with reduced-order modeling, resulting in minimal sacrifice
of precision and physical interpretations. Alternatively,
data-driven or so-called model-free approaches can be used
to model battery characteristics, such as neural-network and
machine-learning approaches. The foregoing two methods
represent end points on a spectrum. A scheme in between is
a gray-box model (also referred to as an equivalent circuit
model) that approximates internal battery dynamics with some
voltage sources, resistors, and capacitors. These models are
relatively easy to implement but suffer from the lack of physically
meaningful parameters and insights. As a result, precise
constraints that achieve safe and optimal operations are difficult
to impose with gray-/black-model-based algorithms.
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