Battery Power - Fall 2015 - (Page 6)
Feature
Real-Time Battery Modeling in HiL Testing of Battery Management Systems
Paul Goossens, Vice President, Engineering Solutions
Maplesoft
Demand growth for rechargeable batteries in products such
as portable electronic devices, electrified vehicles (EVs) and
decentralized power utilities (smart grids) is nothing short of
robust. With forecasted annual growth pegged at 8 percent by
the Freedonia Group, the global battery market will reach almost
$74 billion this year. Rechargeable batteries will account for
about 82 percent of that, or $60 billion, according to market
researcher Frost & Sullivan.
This strong growth has brought numerous major electronics
companies into the market, offering products that target a range
of applications: from small and light-weight power for hand-held
devices to batteries the size of shipping containers for utilities. It
has also led to a significant increase in research investment into
battery technologies that address many of the technical challenges facing the industry, ranging from increasing specific energy
(capacity of a cell in watt-hours per kilogram, W/Kg), to thermal
stability, battery life extension through Battery Management Systems (BMS), and on to final disposal of spent cells.
Technical Challenges in Large Systems
As battery systems get larger, monitoring and controlling
them becomes increasingly complex. Issues such as charging
and discharging the cell array in a way that minimizes charge
times while maximizing energy efficiency and battery life must
all be considered when developing Battery Management Systems (BMS) for these products.
In particular, testing the BMS poses major challenges: they
are costly to put together, any faults in the BMS design can cause
catastrophic damage to very expensive test units, and often it may
not be possible to test for all scenarios, such as balancing across
the cells, leading to uncertainty around the test results.
An attractive solution to these challenges is to use virtual
batteries, mathematical models of battery cells that are capable
of displaying the same dynamic behavior as real ones, for earlystage testing of the BMS. Not only have these models proven
to be highly accurate, they are computationally efficient and are
able to achieve the execution implementation required to deliver
real-time performance for batteries containing hundreds of cells
on real-time platforms.
Maplesoft, a developer of battery modeling technology,
recently partnered with ControlWorks, Inc. of South Korea to develop a turn-key BMS system for one of the largest producers of
consumer electronics products in the world. This project focused
on a large electrical storage system for the Smart Grid and UPS
markets. The end result was a battery model capable of being
configured to represent a stack of up to 144 cells that can be connected in any combination of parallel and series networks. Fault
modes were also required, such as individual cells shorting or
opening, as well incorporating variations in charge capacity from
cell to cell, and degradation of capacity over the life of the cells.
6
Battery Power * Fall 2015
Approaches to Developing High-Fidelity Battery Models
There are two main approaches to battery modeling. The first
is to use equivalent electrical circuits that reflect charge capacity and internal resistance through the use of standard electrical
components to represent these properties. These equivalent-circuit models are conceptually simple and computationally light,
while capable of capturing many of the non-linear behaviors
within the cell. However, their scope of operation is somewhat
limited and it is not easy to map the components of the model to
the physical aspects of the real cell.
The other method is to use electrochemistry-based battery
models, those that include the details of the underlying physics
within the reaction between the electrodes and electrolytes. These
have been shown to be highly accurate predictors of the overall
charge/discharge characteristics of a cell. This physical behavior
can be represented by a system of well-documented partial differential equations (PDE), such as those developed by Newman.
Solving these PDEs, however, can only be achieved through the
use of computationally intensive techniques such as finite-element
(FE) and computational fluid dynamics (CFD). These approaches
make them unsuitable for system-level modeling since they can
take hours to compute behavior over a few seconds.
In the last few years a new technique has been developed.
This compromise approach provides real-time performance
while almost completely maintaining the accuracy of the
full physics models. This uses a rigorous PDE discretization
technique to simplify the model to a set of ordinary differential
equations (ODE) that can be readily solved by system-level
tools like MapleSim. The model optimization techniques employed in MapleSim also allow the resulting model code to be
fast and capable of running in real-time.
Using these physics-based models, it is possible to implement
battery models that predict the charge/discharge rates, state of
charge (SoC), heat generation and state of health (SoH) through
a wide range of loading cycles within complex, multi-domain
system models. This approach provides the performance needed
for system-level studies, with minimal loss in model fidelity.
Furthermore, the underlying foundation for model formulation and integration in MapleSim is based on the conservation of
energy. Where there are inherent inefficiencies in the model, lost
energy is computed as well as the effective (or "useful") energy.
This means that the user can allow for energy loss through heat
(in large applications that proportion is very close to 100 percent), making these models useful for performing thermal studies to determine component sizes in cooling systems required to
control battery temperature.
This is critically important when battery SoH must be considered, as the temperature of operation of each cell has a very
significant effect on the degradation of its performance. If not
carefully controlled, this can lead to reduced operational life or, in
extreme cases, destruction through thermal runaway, a common
problem in many battery-powered systems. This can be caused
by unforeseen loading cycles, or a compromised cooling system
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Table of Contents for the Digital Edition of Battery Power - Fall 2015
Real-Time Battery Modeling in HiL Testing of
Battery Management Systems
Battery Second Use Offsets Electric Vehicle Expenses,
Improves Grid Stability
2015 Battery Power Resource Guide
Batteries
Converters & Inverters
ICs & Semiconductors
Industry News
Calendar of Events
Marketplace
Battery Power - Fall 2015
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