• Energy Research

Abstract The paper presents an equivalent circuit based simulation model for the static and dynamic behavior of lithium-ion battery systems and explains the different steps of the theoretical and experimental modeling process. The equivalent circuit based model describes the voltage-current characteristic, the state of charge behavior and the occurring losses of the battery system. A parameter identification method based on three characteristic experiments, a cycle test, an open circuit voltage test and pulse tests, is introduced. The model parameters are estimated employing a nonlinear optimization method. Furthermore, the experimental test bed for investigation of lithium-ion battery systems is described. The battery model approach is demonstrated and validated for a commercial 5 kWh lithium-ion battery system, including the model validation for test profiles and emulated battery charging and discharging profiles of a real photovoltaic home storage application. The comparison of measured and simulated voltage profiles indicates an excellent performance of the battery model.

The increasing share of renewable energies in the electricity sector promotes a more decentralized energy supply and the introduction of new flexibility options. These flexibility options provide degrees of freedom that should be used optimally. Therefore, in this paper, a model predictive control-based multi-objective optimizing energy management concept for a hybrid energy storage system, consisting of a photovoltaics (PV) plant, a battery, and a combined heat pump/heat storage device is presented. The concept’s objectives are minimal operation costs and reducing the power exchanged with the electrical grid while ensuring user comfort. In order to prove the concept to be viable and its objectives being fulfilled, investigations based on simulations of one year of operation have been carried out. Comparisons to a simple rule-based strategy and the same model predictive control scheme with ideal forecasts prove the concept’s viability while showing improvement potential in the treatment of nonlinear system behavior, caused by nonlinear battery losses, and of forecast uncertainties.

Abstract This paper presents the results of investigation of energy management concepts for fuel cell – battery – hybrid energy storage systems in mobile applications. The energy management has to control the power spilt between fuel cell and battery to guarantee the coverage of the power demand of the vehicle. First of all, the simulation tool is introduced, which enables the evaluation and comparisons of the energy management concepts. Therefore, the PEM fuel cell and the lithium-ion battery are modelled and dimensioned adequately. Performance-criteria are defined to evaluate the energy management concepts. The usage of hydrogen, the efficiency of the hybrid energy storage system and the operating conditions with regard to the life time of the components are especially considered. Three energy management concepts are implemented and analysed: a hysteresis controller, a stiffness-coefficient-model and a fuzzy-logic-controller. The two latter concepts are determining the fuel cell power according to the power demand. The main result, presuming realistic designs of fuel cell and battery, is that the general performance of the stiffness-coefficient-model and the fuzzy-logic-controller is significantly superior to the one of the hysteresis controller. An active limitation of the fuel cell power is recommended to further improve the performance of the energy management concepts.

Abstract The paper describes a new optimizing design concept for autonomous power supply systems employing an enhanced particle swarm algorithm taking into account both component sizing and energy management parameters. The structure of the overall optimization problem is investigated showing high complexity and nonlinearity particularly for switching energy management strategies. The specific features of the enhanced particle swarm optimization algorithm are discussed. The new optimizing design concept is investigated for two application examples, a private household and a small agricultural holding. The performance of the enhanced particle swarm algorithm is compared to two reference algorithms, a standard particle swarm and a genetic algorithm. The enhanced particle swarm algorithm shows the highest accuracy and fastest convergence speed for both applications and for both energy management strategies.

AbstractThepaper presents a new concept for the optimal design of autonomous energy supply systems based on a photovoltaic plant, a battery and a hydrogen storage path (PV-battery-H2-hybrid system) with the focus on the optimal sizing of therated power and the capacity of all system components. For this purpose a multi-criteria objective function (combining installation and operating costsas well as componentlifetime) and multiple constrains (e.g. security of supply) are considered. The basic characteristics and the complexity of the resulting solutionspace of the optimization problem are described as a function of the sizing and energy management parameters. For the minimization of the objective function a particle swarm algorithm was employed. The paper presents the implementation of the algorithm and simulation results for an example hybrid system configuration. The behaviour of the particle swarm is investigated for different scenarios. Results demonstrate excellent computational speed and accuracy compared to other optimization methods.

Lithium-ion batteries are currently one of the key technologies for a sustainable energy transition. However, they have a limited calendar and cycle lifetime, which are directly affected by operating conditions. Therefore, our goal is to maximize the benefits of a battery storage over its entire lifespan. Stacking multiple services (multi-use) can increase the utilization of battery storage, whereas coupling different storage technologies with complementary characteristics (hybrid energy storage systems) adds a degree of freedom for efficient and degradation-aware operation. To exploit these technological and economic advantages, we develop an energy management concept and demonstrate it in the application example of a grid-connected photovoltaic plant with hybrid battery storage. The multi-use application consists of capacity firming, participation in the electricity spot market, and peak shaving. To address the different temporal scales of the battery storage tasks, we propose a hierarchical energy management with two levels. The model predictive upper level energy management optimizes the grid power considering the time-varying electricity prices and marginal costs of battery storage operation. This multi-objective optimization problem is solved using a mixed-integer linear program with two-dimensional piecewise linearization of conversion losses and battery degradation costs. The strategy-based lower level energy management allocates power in real time to meet the grid power and ramp-rate requirements despite model and forecast errors. Extensive simulations demonstrate the advantages of the proposed approach owing to a better compliance with grid power requirements, lower conversion losses, and significantly higher benefits of the battery storage system over its lifetime.

AbstractThe paper gives an overview of the innovative field of hybrid energy storage systems (HESS). An HESS is characterized by a beneficial coupling of two or more energy storage technologies with supplementary operating characteristics (such as energy and power density, self-discharge rate, efficiency, life-time, etc.). The paper briefly discusses typical HESS-applications, energy storage coupling architectures, basic energy management concepts and a principle approach for the power flow decomposition based on peak shaving and double low-pass filtering. Four HESS-configurations, suitable for the application in decentralized PV-systems: a) power-to-heat/battery, b) power-to-heat/battery/hydrogen, c) supercap/battery and d) battery/battery, are briefly discussed. The paper ends with a short description of the HESS-experimental test-bed at Chemnitz University of Technology.