INES - Institut für nachhaltige Energiesysteme
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Lithium-ion batteries exhibit capacity loss as a result of the combined degrading effects of cal-endaric and cyclic aging. In this study, we quantify the lifetime of large-format (180 Ah) com-mercial stationary-storage lithium iron phosphate-based lithium-ion cells by performing 1500 cycles of cyclic aging and ca. 850 days of calendaric aging. The aging tests were performed at two different temperatures (35 °C and 50 °C) to observe the effect of temperature on aging. The calendaric aging cells were stored at two different states of charge (SOC) (100 % and 75 %) to observe the effect of SOC. At the end of aging tests, the capacity loss of all cells at 50 °C ex-ceeded those of all cells at 35 °C. Temperature was thus identified as major aging driver. The observed global activation energy over all investigated aging protocols was 37.3 kJ/mol. Fur-thermore, aging modes (loss of lithium inventory and loss of active material) were investigated by differential voltage analysis of the charge-discharge curves; for the cyclic aging cells, this was performed on the cycling data directly. The degradation mode analysis showed that loss of lithium inventory is mainly responsible for capacity loss.
Ein validierter Wetterdatensatz der Hochschule Offenburg bietet eine solide Datengrundlage für die numerische Gebäude- und Anlagensimulation und zur energiewirtschaftlichen Bewertung von Energiesystemen. Mehrjährige Simulationsläufe bieten in Ergänzung zur Bewertung mit den durchschnittlichen und extremen Testreferenzjahren die Möglichkeit, ein sehr breites und aktuelles Spektrum von Wetterbedingungen abzubilden. Aus der Gegenüberstellung dieses 25-jährigen Datensatzes mit den aktuellen Testreferenzjahren (Bezugszeitraum 1995 bis 2012) bzw. den Wetterdaten aus der aktuellen DIN 4710 (Bezugszeitraum 1980 bis 1994) wird deutlich, dass sich die wesentlichen energiemeteorologischen Größen in den letzten beiden Dekaden deutlich geändert haben. So sind mehr Sommer- und Hitzetage, weniger Eis- und Frosttage, mehr Hitzewellen und eine höhere Solarstrahlung zu beobachten. Dies hat Auswirkungen auf die Planung von Gebäuden und die Dimensionierung gebäude- und energietechnischer Anlagen, wie ein Vergleich mit den Annahmen in gängigen Normen bzw. Auslegungsrichtlinien zeigt.
The invention relates to a method and a device for determining the state of charge (SOC) of a rechargeable battery (106) of a specified battery type, or a parameter physically related thereto, in particular a remaining charge amount Q contained in the battery, wherein the method operates by means of a voltage-controlled battery model (102), which is parameterized for the battery (106) in question or a corresponding battery type. It is merely necessary to measure the battery voltage Umeas and to provide it to the battery model (102) as an input variable. The invention further relates to a method and a device for determining the state of health (SOH) of a battery (102), wherein the battery model (102), which is also used to determine the SOC, provides a modelled battery current Imod. Modelled charge amounts during charging and discharging phases of the battery (106) can be determined therefrom and compared with measured charge amounts, which are determined from the measured battery current Imeas. Because the battery model (102) does not age, the SOH of the battery can thereby be determined.
본 발명은 특정한 배터리 유형의 충전식 배터리(106)의 충전상태(SOC) 또는 이와 물리적으로 관련된 매개변수, 특히 배터리에 포함된 잔류 충전량 Q를 결정하기 위한 방법 및 장치에 관한 것으로서, 본 발명의 방법은 해당 배터리(106) 또는 해당 배터리 유형에 대해 매개변수화된 전압-제어 배터리 모델(102)에 의해 작동한다. 배터리 전압 Umess 만이 측정되어 배터리 모델(102)에 입력변수로 사용할 수 있도록 만들어졌다. 본 발명은 또한 배터리(102)의 건강상태(SOH)를 결정하기 위한 방법 및 장치에 관한 것으로, 배터리 모델(102)은 SOC를 결정하는 데에도 사용되며 모델링된 배터리 전류 Imod 를 공급한다. 이로부터, 모델링된 충전량은 배터리(106)의 충전 및 방전 단계 동안 결정되고, 측정된 배터리 전류 Imess 로부터 결정된, 측정된 충전량과 비교할 수 있다. 배터리 모델(102)은 노화되지 않으므로 배터리의 SOH를 결정하는 데 사용할 수 있다.
本发明涉及一种用于确定规定电池类型的充电电池(106)的荷电状态(SOC)或与之物理相关的参数、特别是电池所含的剩余电量Q的方法和装置,其中,该方法借助电压控制的电池模型(102)工作,该电池模型针对相关电池(106)或相应电池类型被设置参数。只需测量电池电压Umess并将其作为输入参数提供给电池模型(102)。本发明还涉及一种用于确定电池(102)的健康状态(SOH)的方法和装置,其中,也被用于确定SOC的该电池模型(102)提供模型化的电池电流Imod。由此可以确定在电池(106)的充电和放电阶段中的模型化的电荷量并将其与从测量的电池电流Imess中确定的测量电荷量相比较。因为电池模型(102)不老化,故由此可确定电池的SOH。
The invention relates to a method and to a device for determining the state of charge (SOC) of a rechargeable battery (106) of a specified battery type or a parameter physically related thereto, in particular a remaining charge amount Q contained in the battery, the method operating by means of a voltage-controlled battery model (102), which is parameterized for the battery (106) in question or a corresponding battery type. It is merely necessary to measure the battery voltage U
The invention relates to a method and to a device for determining the state of charge (SOC) of a rechargeable battery (106) of a specified battery type or a parameter physically related thereto, in particular a remaining charge amount Q contained in the battery, the method operating by means of a voltage-controlled battery model (102), which is parameterized for the battery (106) in question or a corresponding battery type. It is merely necessary to measure the battery voltage U
Heat pumps play a crucial role in decarbonizing buildings, yet conventional control strategies limit their grid-supportive potential. Model Predictive Control (MPC) offers a promising alternative to optimize energy costs and grid performance, but real-world implementations remain scarce. This study demonstrates the feasibility of MPC in a low-energy, non-residential building by integrating a controller based on electricity market prices. The system, deployed on a Raspberry Pi and integrated into the building automation system, utilizes weather forecasts and a grey-box model for load prediction. A key challenge is the lack of standardized interfaces for heat pump controls, requiring custom solutions. A 7-day performance analysis compares MPC with conventional control, focusing on economic efficiency and grid support. MPC shifts heat pump operation to periods of lower electricity prices, increasing storage temperatures and reducing the average COP from 7.6 to 6.0. Despite this, energy costs decrease by 40%, lowering the electricity procurement price from 0.36 EUR to 0.12 EUR/kWh, while the Grid Support Coefficient improves by 13%. These results confirm that MPC can enhance heat pump operation with simple component models, provided the system allows flexibility and demand is predictable.