This paper describes the comparison and parameterization process of dynamic battery models for cell and system simulation. Three commonly used equivalent circuit battery models are parameterized using a numeric optimization method and basic electrical tests with a lithium-ion polymer battery cell. The maximum model performance is investigated, and the parameterized models are compared regarding the parameterization effort and the model accuracy. For the model with the best tradeoff between the parametrization effort and the model accuracy, a reasonable simplification of the parameterization process is presented. This model is parameterized with the simplified parameterization process and, finally, validated by using a current profile obtained from an electric vehicle simulation performing a real-life driving cycle.

Active charge balancing is a commonly used method to increase the usable energy of a battery stack with serially connected battery cells with different cell capacities. In this article an active charge balancing method with a multiple winding transformer DC/DC converter is described. The advantage of active charge balancing is shown using a prototype with twelve serially connected Lithium ion battery cells. Moreover, the influence of the cell capacity variance and the influence of the balancing current on the discharging energy is investigated.

In this article a method to estimate the capacity of individual lithium ion battery cells during operation is presented. When having two different states of charge of a battery cell as well as the transferred charge between these two states, the capacity of the battery cell can be estimated. The method is described in detail and validated on a battery cell with a current pulse test cycle. It is then applied to a real life cycle, the accuracy is analyzed and discussed.

This article presents a method to control the current of each battery cell in a serially connected battery stack according to each cell capacity. With this method battery cells with different capacities and even battery cells with different chemistries can be serially connected. Therefore, significantly more energy can be extracted from the battery stack and a second-life-option for old batteries with a large capacity variation is possible. The required power converter structure as well as the calculation of the individual cell currents are presented. A simulation of the power converter is performed and validated by a prototype with twelve serially connected battery cells.

This article describes the parameterization process of dynamic battery models for cell and system simulation. Three commonly used equivalent circuit battery models are parameterized using a numeric optimization method and basic electrical tests with a lithium-ion polymer cell. The maximum model performance is investigated and a reasonable simplification is presented. The parameterized models are then validated and compared using a current profile obtained from an electric vehicle simulation performing a real life driving cycle.

This article gives an overview of the Electric Energy Storage (EES) library, which is proposed for inclusion in the Modelica Standard Library.The library contains models with different complexity for simulating of electric energy storages like batteries (single cells as well as stacks) interacting with loads, battery management systems and charging devices.It is shown how the models are defined and how they can be parametrized.Finally, two example simulations are presented.

This paper presents a method to control the current of each battery cell in a serially connected battery stack according to each cell capacity. With this method, the performance of a battery stack can be increased significantly. In a second life concept, battery cells with different capacities and even battery cells with different chemistries can be connected together. Moreover, if one or more battery cells become inoperative, then the battery stack can still be used in the limp-home operation mode. The required power converter structure and the calculation of the individual cell currents are presented, and a simulation for two different power converters during discharging and charging is performed. A current equalization prototype is used to validate the simulation results and to show the performance on a real battery stack with 12 serially connected cells. Finally, the influence of the equalization currents and the capacity variance in the battery stack on the discharging energy are investigated.

This paper presents an active cell balancing method for lithium-ion battery stacks using a flyback dc/dc converter topology. The method is described in detail, and a simulation is performed to estimate the energy gain for ten serially connected cells during one discharging cycle. The simulation is validated with measurements on a balancing prototype with ten cells. It is then shown how the active balancing method with respect to the cell voltages can be improved using the capacity and the state of charge rather than the voltage as the balancing criterion. For both charging and discharging, an improvement in performance is gained when having the state of charge and the capacity of the cells as information. A battery stack with three single cells is modeled, and a realistic driving cycle is applied to compare the difference between both methods in terms of usable energy. Simulations are also validated with measurements.

This article describes the parameterization of a simple, dynamic single cell battery model for cell and system simulation. It is shown how to parameterize the model based on basic electrical tests and publicly available data such as data sheets. The performance of the parameterized model is validated with test results gained from two different cycles with the li-Tec HEA 40 High Energy Cell. Additionally, it is shown how the model can be extended to consider basic aging effects.

In this work a buck converter model for multidomain simulations is proposed and compared with a state-of-the-art buck converter model. In the proposed model no switching events are calculated. By avoiding the computation of the switching events in power electronic models the processing time of multi-domain simulations can be decreased significantly. The proposed model calculates any operation point of the buck converter in continuous inductor current conduction mode (CICM) while considering the conduction losses and switching losses. It is possible to utilize the proposed modeling approach also for other dc-to-dc converter topologies. Laboratory test results for the validation of the proposed model are included.

This article presents how active charge balancing of energy storage devices such as batteries and supercaps can be improved by using the capacity and the state of charge instead of the cell voltage as balancing criterion. Both for charging and discharging an improvement of performance is gained when using the state of charge and the capacity of the cells as information. A battery stack is modeled and a realistic driving cycle is applied to compare the difference between both methods in terms of usable energy. Finally, the simulation is validated by measurements.

In this work a buck converter model for multi-domain simulations is proposed and compared with a state-of-the-art buck converter model. In the proposed model no switching events are calculated. By avoiding the computation of the switching events in power electronic models the processing time of multi-domain simulations can be decreased significantly. The proposed model calculates any operation point of the buck converter in continuous inductor current conduction mode (CICM) while considering the conduction losses and switching losses. It is possible to utilize the proposed modeling approach also for other dc-to-dc converter topologies. Laboratory test results for the validation of the proposed model are included.

In this work a buck converter model for multi- domain simulations is proposed and compared with a state- of-the-art buck converter model. In the proposed model no switching events are calculated. By avoiding the computation of the switching events in power electronic models the processing time of multi-domain simulations can be decreased significantly. The proposed model calculates any operation point of the buck converter in continuous inductor current conduction mode (CICM) while considering the conduction losses and switching losses. It is possible to utilize the proposed modeling approach also for other dc-to-dc converter topologies. Laboratory test results for the validation of the proposed model are included. Index Terms—simulation, DC-DC power conversion, losses I. INTRODUCTION For the efficient utilization of multi-domain simulation software it is of high importance to have fast simulation models of power electronic components on hand. Especially in simulations of vast and complex electromechanical systems (e.g. power trains of hybrid electric vehicles (1) or drive systems in processing plants (2)) it is crucial to limit the processing effort to a minimum. Many times such elec- tromechanical systems contain power electronic subsystems such as rectifiers, inverters, dc-to-dc converters, balancing systems (for energy sources), etc. When simulating these power electronic devices together with the other electrical and mechanical components of the application, computing the quantities of the power electronic models requires a large share of the available processing power if switching events are calculated in the power electronic models. Simulation models including power electronic devices with switching frequencies around 100kHz require at least four calculation points within simulation times of around 10µs for calculating the switching events. However, if the energy flow in an electromechanical system has to be investigated by simulation it is not necessary to calculate the switching events in the power electronic model as long as the relevant losses are considered. In this work two different buck converter models are described. The first model, modelA, which is state-of-the- art describes the behavior of a conventional buck converter, as shown in fig.1, including the calculation of switching events. This means that in modelA the switching of the semiconductors in the circuit is implemented with if-clauses. Therefore, modelA directly calculates the ripple of the current through the storage inductor and the ripple of the voltage across the buffer capacitor. Due to the if-clauses in modelA the duration of the computing time is very high. The second model in this work, indicated as modelB, de- scribes the behavior of the buck converter without calculating