IEE-Battery simulations save expensive test series and help to accelerate development processes.

The IEE-battery models have been deployed successfully for more than 15 years in the international automotive industry.

Ask for further information to benefit from our experience!

We parameterize your equivalent circuit diagram battery model!

Batterysimulation and -emulation

At Fraunhofer IEE a focus is on the development of software for the simulation of lead-acid and Li-ion batteries: BaSiS - Battery Simulation Studio.

The software products BaSiS-LIB and BaSiS-LAB are offered for use in industrial and pre-commercial research
(car manufacturers, battery manufacturers etc.). The real-time version of this software is used to emulate batteries (virtual battery).

For the model validation and the development of new storage concepts, IEE has battery labs with high precision measurement technology,
in which batteries can be measured in the time domain as well as in the frequency domain.

IEE-Products

Battery simulations save expensive test series and help to accelerate development processes.

Simulation of Li-Ion Batteries

BaSiS-LIB

The software BaSiS-LIB simulates all relevant physical and electrochemical processes in Lithium Ion batteries under different operating conditions.

The model inputs are constructive data as well as characteristic parameters of the cell chemistry.

The battery models of the Fraunhofer IEE have been deployed successfully for more than 15 years in the international automotive industry.

 

Simulation of Lead-Acid Batteries

BaSiS-LAB

The software BaSiS-LAB simulates all relevant physical and electrochemical processes in Lead-Acid batteries under different operating conditions. The model inputs are constructive data as well as characteristic parameters of the cell chemistry.

Emulation of Li-Ion Batteries

VIRTUAL BATTERY

A virtual battery consists of a bi-directional power supply system connected to a control unit running a real-time battery model. The voltage output of the power supply unit is controlled according to the voltage behavior of a real battery. The dynamical behavior of the battery is calculated by a real-time version of the IEE lead acid or lithium ion battery simulation software BaSiS-LAB and BaSiS-LIB respectively!

Battery Management Systems Tests

BMST

Testing a Battery Management Systems (BMST) with real Lithium-Ion Batteries (LIB) requires considerable conditioning efforts for each cell in terms of state and temperature, as well as precautions for the case of failures. To overcome these issues, a BMS Hardware-in-the-Loop (HIL) system was developed.

Please feel free to contact us!

We parameterize your equivalent
circuit diagram battery model!

Ask for further information to benefit
from our experience!

 

We parameterize your equivalent circuit diagram battery model!

Ask for further information to benefit from our experience!

Consulting

We would be pleased to give you advise about the simulation and emulation of batteries. Based on more than 15 years of expertise we can give you advise aboutthe different modelling approaches and fields of application. Additionally we can adapt the associated parameterization especially to your personal needs and interests.

 

 

Parametrization

Different parameters for the cell are needed for the simulation and emulation of batteries. Those paramters are dependant of the chosen modelling approach.

We carry out the parameterization for your equivalent circuit battery model and validate the results.

For our white-box battery models we also need the design parameters of your cell thatwe can identify for you too.

Software Adjustments

We offer you an individual software adaptation at our battery model to integrate our battery models purposefully in your simulation or test environment.

 

Workshops

You want to offer your team a professional advanced training on the simulation and emulation of batteries and their use in real cases?
Contact us and we adapt the seminar to the topics that you are interested in, the knowledge of your team and your aims.

Technology

Simulation models for Lead-Acid or Lithium-Ion batteries can be implemented in different ways depending on the modeling approach. They are based on equations and systems of equations.

A simulation model for batteries possesses input parameters (e. g., current and ambient temperature) and output parameters (e. g., voltage) which are calculated based on the underlying equations and on the present state of the battery (e. g., SOC).

Furthermore, model parameters are necessary that differ for the specific batteries. For several modeling approaches parameters can be stored in the form of characteristics to update these during the simulation, e. g., according to the temperature. Other models perform physical processes.

A model is always a simplified picture of reality, since the exact description of reality is often too complex. In principle, the initial difficulty in battery simulation is to choose the correct modeling approach for the specific issue and the acceptable model simplifications.

In doing so, in particular for application in the industry, there must be an appropriate ratio between cost and benefit. After choosing an appropriate modeling approach, challenging issues are the model development, the implementation and the parametrization.

 

 

Models

Basically, there exist three different approaches that can be chosen for battery modeling. Thereby, a distinction is made between Black-box, Grey-box and White-box models.

Black-Box

In the case of Black-box models virtually no knowledge is necessary of the system, which is modeled, however, relatively extensive and time-consuming measurements in the laboratory are required for parametrization. A disadvantage of this modeling approach is that the user does not obtain an insight into the system and does not gain further understanding of the battery system. However, an advantage of the approach is a fast computational speed. Currently, these models are rarely used in battery modeling.

Grey-Box

In the case of the Grey-box approach, typically in form of an electrical equivalent circuit, a part of the model parameters possesses a physical meaning, another part does not. Moreover, in this modeling approach only an input/output behavior of a battery can be observed and no insight into the battery system is possible. This approach provides a high computational speed. However, the approach considers the strongly non-linear behavior of the battery only in a certain operating point and not over the entire area of operation of the battery, i. e., SOH, SOC, I, T.

Due to the simple implementation, this approach is often applied in practice, in some cases extended by characteristic fields to perform parameter updates. However, in terms of accuracy over the operating window it holds larger disadvantages.

White-Box

In the case of the White-box approach the occurring physical / electrochemical processes in the battery are characterized via partial differential equations. Every model parameter holds a physical meaning. Thus, insights into the battery are possible which would be impossible without a simulation model. For the development of such a model an accurate understanding of the battery system is mandatory. This approach is typically computationally more expensive than the two previous approaches, however, it considers the non-linear behavior of the battery over the entire area of operation.

Our models for Lithium-Ion and Lead-Acid secondary batteries

The models BaSiS-LIB and BaSiS-LAB have been developed via the White-box approach since all batteries are strongly non-linear systems and possess a strong dependency on temperature, the SOH, the SOC and the current. “With high precision” implies that the model is able to cover exactly this area and the simulation accuracy for the respective application is sufficiently precise. Furthermore, so far there is no uniform standard for the comparison of battery models in form of current and temperature profiles.

In  BaSiS-LIB and BaSiS-LAB the user can choose between a higher accuracy and higher computational speed via a control parameter. For these reasons, no absolute specifications for precision are possible.

The models have been developed in a 6-step process:

  1. Gain understanding of the problem
  2. Choice of a modeling approach
  3. Model development
  4. Simulation
  5. Validation of the simulation
  6. Further development or another iteration of the steps 3-5

The models describe the current known and relevant physical/electrochemical processes in Lithium-Ion and Lead-Acid cells. Depending on the industry, the model can support development processes in industry in various manners.

On that point, following examples:

Battery manufacturer:

Via the alteration of the constructive data of a cell (layer thickness, cell structure) and the chemistry of the cell in a few clicks, a manufacturer may partially renounce the construction and testing of battery prototypes and thereby save money. Moreover, a manufacturer obtains detailed insight into processes of the battery and, thus, may optimize the cell design and the manufacturing. The analysis of sensitivities to individual parameters is possible as well.

Automobile manufacturers and suppliers:

In the automotive industry Hardware-in-the-loop (HIL) systems are used to perform tests fast and cost-efficient. In  doing so, a real device under test (e. g., E-vehicle, battery management system) is connected to an emulated battery system. The advantage of battery emulation is that arbitrary states of the battery can be adjusted in a few seconds, whereas this may take several hours to days within real systems. Since the actual test proceeds partly very fast, the use of a battery emulator may save a lot of time and money in this case. Furthermore, the influence of aged batteries can be examined in a fast way, while in real tests even longer periods would be required.

 

Press Release

 

15.9.2015

HIL battery simulator and e-mobility inductive power transmission system

11.3.2015

Neue Features für das Batteriesimulationsmodell des Fraunhofer IEE

11.3.2015

Simulationstool für neue und gealterte Batterien

Scheduled Events

15.3.2018

Energy Storage Europe

13.-15.03.18 | Düsseldorf

 

27.2.2018

Battery Experts Forum

27.02.- 01.03.18 | Aschaffenburg

27.2.2018

VDA Technischer
Kongress

27.- 28.02.18 | Maritim Hotel Berlin

 

 

23.4.2018

Hannover Messe

23.- 27.04.18 | Hannover

3.4.2018

Seminar
Batteriesimulation

03.- 04.07.18 | Stuttgart

26.- 27.09.18 | München

17.- 18.10.18 | Frankfurt am Main

9.4.2018

Kraftwerk
Batterie 2018

09.- 11.04.18 | Münster

Posternummer P1-10

»Combined hybrid energy storage for electric vehicles - development, system integration, energy and thermal management.«

Fraunhofer IEE (formerly IWES) and Virtual Battery

Interview with Matthias Puchta
(in german)

Fraunhofer-Alllianz Batterien

Publications

  • Using a Newman-based battery model and post mortem analysis to develop a non-destructive method for SoH estimation
    Schledde, D.; Leiva, D.; Schwalm, M.; Puchta, M.; Nikolowski, K.; Wolter, M.
    Kraftwerk Batterie, Aachen 2016
  • Simulationsstudie zu thermisch gekoppelten, inhomogenen Lithium-Ionen Zellen im Batteriepack
    C. Kettenring, M. Puchta, D. Schledde, Dr. rer. nat. M. Schwalm
    4. Symposium Elektromobilität, Ostfildern/Stuttgart, Juni 2015
  • Accelerated Devlopment and Test of BMS Using an Emulation Based HIL
    D. Schledde, M. Schwalm, C. Kettenring, u.a.
    Kraftwerk Batterie, Aachen 2015
  • Modellierung und Simulation von Li-Ionen Batterien
    M. Puchta, M. Schwalm, D. Schledde
  • 5. Symposium Test und Qualifizierung für Lithium-Ionen-Batterien und Brennstoffzellentechnik, Vötsch 2013
  • Virtuelle Batterien in der Entwicklung von Elektrofahrzeugen
    M. Puchta, D. Schledde
    Digital Engineering Magazin 8/2011, Win-Verlag GmbH & Co. KG, pp. 24. Juni 2011

References

 

Please do not hesitate to contact us.

* Required

Please do not hesitate to contact us.

We parameterize your equivalent circuit diagram battery model!
Ask for further information to benefit from our experience!

Matthias Puchta

Contact Press / Media

Dipl.-Ing. Matthias Puchta

Head of Department Energy Storage

Koenigstor 59
34119 Kassel, Germany

Phone +49 561 7294-367

Fax +49 561 7294-100