# Second Life Li-ion batteries: Influence of aging on the Techno-economic Analysis of a Microgrid

Lucas de O Albuquerque, Corentin Boennec, Fabien Lacressonnière, Xavier Roboam, Nicolas Damay, Christophe Forgez

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Lucas de O Albuquerque, Corentin Boennec, Fabien Lacressonnière, Xavier Roboam, Nicolas Damay, et al.. Second Life Li-ion batteries: Influence of aging on the Techno-economic Analysis of a Microgrid. The 26th European Conference on Power Electronics and Applications, GDR SEEDS France & EPE Association, Mar 2025, Paris, France. 10.34746/epe2025-0096. hal-05088965

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# Second Life Li-ion batteries: Influence of aging on the Techno-economic Analysis of a Microgrid

Lucas de O. Albuquerque¹, Corentin Boennec¹, Fabien Lacressonnière¹, Xavier Roboam¹, Nicolas Damay², Christophe Forgez²

¹ LAPLACE, UMR CNRS-INPT-UPS, Université de Toulouse.

² Roberval, Université de Technologie de Compiègne, France.

Address:

¹ 2 Rue Charles Camichel, 31500 Toulouse, France.

² UTC - Centre de recherche de Royallieu CS 60319 - 60203 Compiègne.

albuquerque@laplace.univ-tlse.fr, boennec@laplace.univ-tlse.fr, fablac@laplace.univ-tlse.fr.

**Index Terms**—Energy storage, Lithium-ion battery, Aging, Microgrid.

**Abstract**—In the context of second life lithium-ion batteries, understanding the aging process is crucial for accurately sizing a microgrid that employs Energy Storage Systems. This article introduces a novel aging model that captures the aging knee point of lithium-ion batteries and integrates it in a microgrid simulation. The goal is to understand how key aging parameters influence battery performance within the microgrid. The study analyzes the impact of the aging knee, initial State of Health and retail price.

## I. INTRODUCTION

As is now known, the insertion of intermittent renewable sources in electric grids is of extreme importance in tackling the climate crises. However, since energy availability does not always align with demand, Energy Storage Systems (ESS) are imperative for their efficient integration. The technologies available are vast and continue on improving, however, Li-ion Batteries (LIBs) are currently the most widely available and employed in stationary systems. LIBs are known for their robustness, high efficiency and long life-time associated with low maintenance.

Nevertheless, LIBs associated CO₂ emissions are still high [1] and are associated to the energy intensive metal extraction, which is why in this context, the idea of reusing Second Life Batteries (SLBs) from used

electric vehicles in less demanding applications such as stationary storage can be interesting in terms of lowering the overall ecological impact of the ESS. However, employing these SLBs can be more challenging, since their first life can greatly impact their second life performance as they are known to be path dependent [2]. LIBs in general can present an aging "knee", characterized by a sudden increase in the rate of capacity (or resistance) degradation during battery cycling. This aging acceleration effect can be even more detrimental to SLBs.

Lifetime expectancy and State of Health (SoH) estimation for LIBs are crucial for correctly assessing the economic performance of a microgrid [3]. This was proven for First Life Batteries, however, to our knowledge, few studies have focused on SLB simulation in microgrids, such as [4] and [5]. In this context, this article proposes a novel LIB aging model based on literature degradation data to assess the economic viability of SLBs. This model was used to evaluate the impact of battery degradation on various microgrid sizings and to better understand the influence of LIB aging on the techno-economic analysis of ESS deployment.

## II. THE AGING MODEL

The developed LIB aging model was derived from literature degradation data. NMC/Graphite degradation data published in [6] was used for the presented model since they go as far as 40% SoH for some cells.
This was important, since capturing the aging knee is essential for correctly estimating SLB capacity during their utilization.

### A. Calendar aging model

The first step for establishing the aging model was to create a simple calendar degradation model from the published data. An Arrhenius-type model similar to the one used in [7] was used to capture the calendar aging dynamics of the batteries:

$$ \text{SoH} = 1 + Q(t_0) - A_{cal} \cdot \exp\left(\frac{-B_{cal}}{k_b \cdot T}\right) \cdot \sqrt{t - t_0} \quad (1) $$

Where $k_b$ is the Boltzmann constant, $T$ is the temperature in Kelvin, $t$ is the elapsed time in days, and $A_{cal}$ and $B_{cal}$ are terms to be identified, the latter corresponding to the activation energy of the Solid Electrolyte Interphase (SEI) layer formation reactions. Moreover, since the time elapsed between the cells' manufacturing and their characterization is unknown, their actual capacity, $1 + Q(t_0)$, and the time before the first characterization, $t_0$, were estimated to achieve a better fit for the model. The estimated values for $Q(t_0)$ were 4.26%, 4.8%, and 9.97%, with corresponding $t_0$ values of 101, 70, and 68 days for 35°C, 40°C, and 50°C, respectively.

<figure><img src="image_2.png" /><figcaption>Fig. 1. Calendar aging model established using data from [6].</figcaption></figure>

Fig. 1 shows the data used to define the model as well as the results for four different temperatures. The obtained parameters are presented in Table I. Furthermore, since our goal is to simulate SLBs, we chose to bypass the calendar aging model up to the 1-year mark in our microgrid simulations. This means that the simulated batteries will experience only the slower, more linear rate of degradation, avoiding the sharp drop in capacity linked to the initial formation of the SEI (seen in first life batteries). The initial $\Delta\text{SoH}$ due to calendar aging was set to zero for the SLB simulations.

### B. Cycling aging model

In this work, a typical Cycles-to-Failure model [8] was used to capture battery aging due to cycling as a function of the Depth of Discharge (DoD). However, for our model, the degradation curves were separated into two Wöhler curves, here named Cycles-to-Knee (CTK) and Knee-to-Failure (KTF), the first gives the number of cycles for a given DoD to reach the knee point and the second the number of cycles to reach the battery's End of Life (EoL). In order to define these curves, first, the calendar aging component was removed from the cycle degradation data shown in Fig. 2 using the previously developed calendar model. Then, the pure capacity degradation points related to cycling with respect to the Equivalent Full Cycles (EFCs) were interpolated. This was done in order to establish the number of EFCs from 100% SoH until 80% (CTK) and from 80% until 40% (KTF).

<figure><img src="image_3.png" /><figcaption>Fig. 2. Pure cycling SoH degradation for various DoDs from [6].</figcaption></figure>

Since certain DoDs present more than one dataset, the number of EFCs was determined by averaging the available data. Once the EFCs for each DoD are determined, they are plotted and identified with an exponential model (Eq. 2) by minimising the Relative Root Mean Square Error (RRMSE) between the experimental data and the model.

$$ \text{EFC}(\text{DoD}) = A_{cyc} \cdot \exp(-B_{cyc} \cdot \text{DoD}) + C_{cyc} \quad (2) $$

Fig. 3 presents the identified models for the CTK and KTF data, with RRMSE values of 6.1% and 1.1%, respectively. The higher RRMSE for CTK is attributed to data points at low DoDs, resulting in a higher error. In contrast, the KTF model, which only includes high DoD datasets that exhibit the aging knee, extrapolates points at low DoDs, potentially leading to deviations. It
is important to mention that low DoDs can potentially delay the aging knee by imposing less mechanical stress on the battery's active materials [10]. In the end, the complete model was validated using the data employed to establish it. The average RRMSE was 2.4%, with a standard deviation of 2.5%.

<figure><img src="image_4.png" /><figcaption>Fig. 3. CTK and KTF curves identified using data from [6].</figcaption></figure>

<table><caption>TABLE I
PARAMETERS FOR CALENDAR, CTK AND KTF MODELS</caption><thead><tr><td>Model</td><td>Parameter A</td><td>Parameter B</td><td>Parameter C</td></tr></thead><tbody><tr><td>Calendar</td><td>1.00 × 10<sup>7</sup></td><td>0.59</td><td>—</td></tr><tr><td>CTK</td><td>2.49 × 10<sup>3</sup></td><td>2.24</td><td>0.76</td></tr><tr><td>KTF</td><td>38.83</td><td>-1.55</td><td>1.37</td></tr></tbody></table>

Table I exhibits the identified parameters for both the calendar and cycling models. These parameters were introduced in the microgrid simulation tool in order to proceed with the study. Moreover, the SoH was set to be updated with a fixed time-step 48 times per year using a Rainflow algorithm to count the half-cycles. Once they are computed, equations 3 and 4 are used to calculate the degradation of the ESS per half-cycle according to the DoD, hence the 2 in the denominator.

$$ \Delta SoH_{cyc} = (1 - 0.8) \sum_{i=1}^{\overrightarrow{DoD}} \frac{1}{2 \cdot CTK(\overrightarrow{DoD}_i)} \quad (3) $$

$$ \Delta SoH_{cyc} = (0.8 - 0.4) \sum_{i=1}^{\overrightarrow{DoD}} \frac{1}{2 \cdot KTF(\overrightarrow{DoD}_i)} \quad (4) $$

Eq. 3 is used before the knee and Eq. 4 is used after the knee. $\overrightarrow{DoD}$ is the vector containing all the half-cycles

DoDs since the previous SoH update, $CTK(\overrightarrow{DoD}_i)$ is the number of EFC required to reach the knee for a given $DoD_i$ (same principle for the KTF curve). Notice that the numerical values preceding the sums correspond to $SoH_{ini} = 100\%$, $SoH_{knee} = 80\%$ and $SoH_{EoL} = 40\%$. They were not expressed as variables in order to maintain a consistent rate of degradation throughout the simulations. This way, whether the knee onset is simulated at $SoH_{knee} = 80\%$ or $SoH_{knee} = 70\%$, the degradation rate until the knee apparition for a given $\overrightarrow{DoD}$ is the same.

### III. MICROGRID SIMULATION ENVIRONMENT

In order to simulate battery behavior within a microgrid, a tool developed at the Laplace Laboratory was employed. Originally created in the Julia programming language by Hugo Radet [11] and subsequently refined by Corentin Boennec [12], this simulation tool enables the simulation and optimization of microgrid sizing involving multiple assets. This section provides a brief explanation of the simulator. For readers seeking more detailed information, our previously published material is recommended [3].

<figure><img src="image_5.png" /><figcaption>Fig. 4. Microgrid used in simulation.</figcaption></figure>

A simple microgrid configuration, shown in Fig. 4, was used for the study. A set of 5 household consumers was chosen to represent a “Smart neighborhood”. Regarding the uncertainties of this microgrid environment (i.e. solar irradiation and load profiles), 10 scenarios generated using a Markov approach [9] (combinations of solar irradiation and consumption time series) were simulated over a time horizon of 20 years. The original data was provided by Ausgrid [13]. Concerning the battery aging, if it does not reach its EoL ($SoH = 40\%$) before the end of the simulation, a salvage value is calculated based on its SoH: 100% of its initial value for
$SoH_{ini}$, decreasing to 20% at $SoH_{knee}$ and then from $SoH_{knee}$ to $SoH_{EoL}$ it goes from 20% to 0% (similar to [14] and as proposed on [15]). As the EMS optimization is not the main goal of this study, a rule-based controller was used as the operation policy for the microgrid:

* In case of surplus production ($P^{load} - P^{pv} < 0$) the battery is charged. The excess is injected in the grid if $SoC^{max} = 80\%$ is reached.

* In case of a production deficit ($P^{load} - P^{pv} > 0$) the battery is discharged. The grid takes over if $SoC^{min} = 20\%$ is reached.

* $DoD^{max} = 60\%$.

Table II presents some economic assumptions used in the techno-economic analysis. It is important to note that the electricity selling price can significantly impact the attractiveness of installing PV panels. However, since this is not the focus of our research, the selling price was set to 0 €/kWh.

<table><caption>TABLE II
COSTS AND TARIFS OF THE SIMULATIONS.</caption><thead><tr><td>Element</td><td>Cost</td></tr></thead><tbody><tr><td>Subscription</td><td>203 €/year for 10 kVA</td></tr><tr><td>Over-limit penalty</td><td>10.2 €/h</td></tr><tr><td>Grid tariff</td><td>0.19 €/kWh</td></tr><tr><td>PV panels</td><td>1300 €/kWp</td></tr><tr><td>SL Li-ion ESS</td><td>[100 110 120 130] €/kWh</td></tr><tr><td>Grid selling price</td><td>0 €/kWh</td></tr></tbody></table>

## IV. TECHNO-ECONOMIC ANALYSIS

In this section our goal is to better understand how a few parameters such as the battery purchase price, the knee point onset and the initial SoH can influence the techno-economic output of a given microgrid sizing.

### A. Second life battery price influence

In 2023, the French agency for ecological transition (ADEME) cited several articles mentioning reconditioned second life battery pack prices ranging from 32 $/kWh to 80 $/kWh [17]. For our study, a range from 100 €/kWh to 130 €/kWh was considered, factoring in installation and power converters costs. However, these prices can vary significantly depending on the technology, the size of the project and the state of health of the batteries.

Fig. 5 shows the results of multiple simulations with different SLB prices. For these simulations, the initial SoH of the SLB is fixed at $SoH_{ini} = 90\%$, the knee

onset is set to $SoH_{knee} = 80\%$ with an $SoH_{EoL} = 40\%$. The chosen economic metric was the Net Present Cost (NPC) of the system over a 20-year period:

$$NPC = \sum_{y=1}^{20} \frac{C_y^{CapEx} + C_y^{OpEx} - C_y^{Salvage}}{(1 + \tau)^y} \quad (5)$$

where $C_y^{CapEx}$ and $C_y^{OpEx}$ represent the capital expenditures (CapEx) and operational expenditures (OpEx) for year $y$, respectively, and $C_y^{Salvage}$ is the salvage value of the batteries, which is non-zero only for $y = 20$. The discount rate $\tau$ is set at 4.5%.

The color bar is centered at 84.5 k€, which represents the NPC for the baseline point (0, 0), for which all energy is purchased from the grid and no assets are present. Thus, red-colored NPCs represent economic benefits and blue-colored economic costs compared to the baseline. As expected for all the results, the PV = 0 kWp row is unappealing for all 4 simulations since the batteries cannot be operated without any PV panels installed and are thus more expensive than the baseline. Whereas, the BAT = 0 kWh column is the same for all 4 graphics and represent the NPCs for PV installations without any batteries. The overall trend of the 4 heatmaps shows similar patterns, characterized by nearly horizontal iso-price rows. This is because when additional battery capacity is added to a PV system, it increases the initial CapEx. However, this investment also results in OpEx savings because the consumer can utilize more of the generated PV energy. This trend was also seen in [3].

Regarding the optimal solutions, indicated by black stars on the graphics, it is perceptible that only for the lowest battery price does the optimal solution include a battery. This effect is attributed to the nearly horizontal iso-price rows, where even a slight difference in battery price can cause the optimal point to shift abruptly. Additionally, as battery prices rises, the overall NPC of the optimum increases (higher CapEx), as anticipated.

Fig. 6 digs into the nearly iso-price rows for a 10 kWp PV system and multiple battery sizes and prices, alongside the average number of battery replacements, the Renewable Energy Share (RES), calculated as the complement of the proportion of energy purchased from the grid ($E^{g,+}$), and the salvage value in percentage of the initial battery price.

$$RES = 1 - \frac{E^{g,+}}{E^{load}} \quad (6)$$

Fig. 6a reveals that the global minimum for the lowest battery price (73 k€ @ 100€/kWh), for the
<figure><img src="image_6.png" /><figcaption>Fig. 5. NPC comparison between different simulations with different second life battery purchase prices. The color bar is centered at 84.5 €, which represents the NPC for the baseline point (0, 0). Configuration: $SoH_{ini} = 90\%$, $SoH_{knee} = 80\%$, $SoH_{EoL} = 40\%$, $PV_{price} = 1300 €/kWh$.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6. NPC (a) and mean number of battery replacements, RES and salvage value (b) as a function of Battery size for a fixed PV = 10 kWp. Configuration: $SoH_{ini} = 90\%$, $SoH_{EoL} = 40\%$, $PV_{price} = 1300 €/kWp$.</figcaption></figure>

given PV system, is the only situated below the NPC of the microgrid without any batteries (74.1 €). For the highest three battery prices, the local minima exceed that point (0 kWh, 10 kWc), resulting in an abrupt change in the optimal solution. We are facing here a typical “flat problem” with significant differences in the optimal decision variables but with few variations on the objective function. This issue is partly due to the single objective nature of the optimisation problem.

Introducing a second “ecological” criterion could distinguish these solutions leading to a set of optimal solutions inside a Pareto front. Furthermore, it is visible that the number of battery replacements decreases initially until reaching the optimal battery capacity for which the batteries get replaced once during the 20 years of simulation.

The optimal point with a battery for the fixed PV value is reached when the batteries can absorb all the energy produced by the PV panels. After reaching this value, the batteries do not improve the RES value as seen in Fig. 6b. Interestingly, as the transition takes place from replacing the batteries once to not replacing them at all, the salvage value hits its minimum, with the batteries ending the simulation at low SoH values.

## B. Second life battery knee point influence

One really important aspect of battery aging is the knee onset, as this last can really shorten a battery’s life. However, this phenomenon is complex because the degradation modes that lead to the knee can remain latent and suddenly manifest. For instance, the Loss of Active Material at the negative or positive electrode ($LAM_{NE}$ or $LAM_{PE}$) may go unnoticed if only battery capacity is being monitored during use, but it can eventually become the primary cause of capacity degradation. For more interested readers, [18] explains the multiple and complex mechanisms related to the knee appearance in LIBs.

When it comes to SLBs, taking into account the
knee point influence is particularly crucial. The first life of each battery can greatly influence their performance during second life, as depending on the degradation modes, their aging trajectories can be completely different. However, while this article is being written, few authors have taken into consideration the knee onset in their models. This is why the simple model presented in section II was developed from literature data. Ideally, a particular model should estimate the knee onset based on how the battery was used. In this section, however, due to the lack of a model determining the knee onset based on the first life, the goal is to conduct a sensitivity analysis in order to understand how the knee onset can influence microgrid performance.

As a means of comparison, the microgrid configurations (BAT, PV) presented before were here simulated with a knee point varying from $SoH_{knee} = 80\%$ to $SoH_{knee} = 70\%$. However, the rate of degradation of both simulations remained the same (Eq. 3 and 4). This way, we are able to understand how prolonging the battery life before the knee can affect the technological results. Fig. 7 shows the NPC benefits longer living batteries can have compared to the ones where the knee arrives before in terms of $\Delta$NPC between both simulations. A few interesting results can be highlighted, such as the darker lines at PV = 0 and 5 kWp, where there is no difference between the simulations. This is because the PV system is undersized for the grid, preventing it from charging the batteries effectively. As a result, the batteries age slowly in both cases. Consequently, the knee effect is not significant here, as the batteries mainly age due to the calendar contribution.

<figure><img src="image_8.png" /><figcaption>Fig. 7. $\Delta$NPC between $SoH_{knee} = 80\%$ and $SoH_{knee} = 70\%$. Configuration: $SoH_{ini} = 90\%$, $SoH_{EoL} = 40\%$, $PV_{price} = 1300$ €/kWp and $BAT_{price} = 100$ €/kWh.</figcaption></figure>

The difference in OpEx between both simulations ($\Delta$OpEx) is on average responsible for 0.2% of the $\Delta$NPC. This means that the associated costs come from replacing the aged batteries more often (CapEx) for the $SoH_{knee} = 80\%$ simulations compared to $SoH_{knee} = 70\%$. The negligible $\Delta$OpEx can be explained by the average $SoH$ of both simulations, which are really

similar independently of the asset sizes (BAT, PV). This means that the missions accomplished are the same for both, resulting in the same amount of energy being bought from the grid.

<figure><img src="image_9.png" /><figcaption>Fig. 8. SoH curves for $SoH_{knee} = 80\%$ and $SoH_{knee} = 70\%$. Example for (BAT = 80 kWh, PV= 30 kWp).</figcaption></figure>

The region towards higher (BAT, PV) values presents the largest $\Delta$NPC, since with such PV values, the batteries undergo larger DoDs for both simulations and thus age faster. However, for $SoH_{knee} = 80\%$, they get replaced more often. Fig. 8 shows one example of the aging dynamics for both simulations. As seen, the mean SoH is basically the same for both, however, for $SoH_{knee} = 70\%$, the batteries get replaced 3 times and for $SoH_{knee} = 80\%$ it gets replaced 6 times.

### C. Second life battery initial SoH influence

As for the knee onset, the $SoH_{ini}$ has a significant influence on the SLB life. This section aims to illustrate the impact of this battery parameter on the microgrid. It is frequently mentioned in the literature that once a car battery reaches $SoH = 80\%$, it is considered at the end of its first life [19]. However, depending on its first life usage, this value can greatly vary, as is already the case for SLB manufacturers who have to sort and diagnose multiple batteries coming from different usage scenarios.

In the following paragraphs two different $SoH_{ini} = 90\%$ and $80\%$ will be compared in multiple microgrid configurations (BAT, PV) with a $SoH_{knee} = 70\%$. In order have a fair comparison, the same amount of kWh will be simulated for both cases. This means that the $SoH_{ini} = 80\%$ will need a few more modules to reach the same capacity as the $SoH_{ini} = 90\%$ case. However, a given battery capacity in kWh will cost the same for both cases.

Fig. 9 shows the difference in $\Delta$NPC for both simulations. The impact on the economic indicators are similar to the ones analysed in the previous subsection.
This is due to the fact that even though the $SoH_{ini}$ and thus the $SoH_{mean}$ during the simulations are different, the actual available capacity is the same during the simulations. However, just like in the previous subsection, the $SoH_{ini} = 80\%$ case has its batteries more frequently replaced for higher (BAT, PV), since they have a shorter life before the knee point. Therefore, the $\Delta CapEx$ represents 94.6% of the $\Delta NPC$.

<figure><img src="image_10.png" /><figcaption>Fig. 9. $\Delta NPC$ comparison between $SoH_{ini} = 80\%$ and $SoH_{ini} = 90\%$. Configuration: $SoH_{knee} = 70\%$, $SoH_{EoL} = 40\%$, $PV_{price} = 1300 \€/kWp$ and $BAT_{price} = 100 \€/kWh$.</figcaption></figure>

## D. Second life battery efficiency influence

In [3], a battery efficiency model was taken into consideration for first life batteries in microgrids, accounting for variations in both $C_{rate}$ and SoH. A constant efficiency model was compared to a polynomial efficiency model and the general conclusion was that the simulated economic performance was quite similar in both cases, as the overall efficiency was very high for both. This result was due to the fact that the minimal SoH of the simulation was set to 80%. Here the goal is to extend our previous analysis for SLBs. A strong assumption was made that the performance of the SLB degrades similarly to that of a first life battery, though this may not be entirely accurate. With this in mind, the same model presented in [3] was used for the simulations:

$$ \eta^{dch}(C_{rate}) = 0.002232 \cdot C_{rate}^2 - 0.0246 \cdot C_{rate} + 1 \quad (7) $$

$$ \eta^{dch}(SoH) = \eta^{dch}(C_{rate}) - 0.2303 \cdot (1 - SoH) \quad (8) $$

This model was first described in [20] and although it does not represent the battery used for the aging model integrated in this work, it captures the overall efficiency degradation of a LIB. Fig. 10 illustrates the discharge efficiency as a function of time for a simulation where $SoH \in [0.4, 0.9]$. The efficiency with respect to $C_{rate}$ for the charge responds similarly to the example given.

The previous (BAT, PV) microgrids were simulated both with and without the polynomial efficiency model to assess its impact on the economic analysis. As seen

<figure><img src="image_11.png" /><figcaption>Fig. 10. SLB efficiency evolution for $SoH_{ini} = 90\%$, $SoH_{knee} = 70\%$ and $SoH_{EoL} = 40\%$ and (BAT = 80 kWh, PV= 20 kWp).</figcaption></figure>

in Fig. 11, the $\Delta NPC$ is relatively low and increases towards higher (BAT, PV). However, the overall impact is much lower compared to the previous factors considered in this work. The $\Delta OpEx$ accounts for 80% of the $\Delta NPC$, since more energy has to be bought from the grid. Nevertheless, it is important to note that these results assume the battery is at a constant temperature of 25°C. In colder weathers, the differences could worsen.

<figure><img src="image_12.png" /><figcaption>Fig. 11. $\Delta NPC$ comparison between the constant efficiency and the polynomial efficiency models. Configuration: $SoH_{ini} = 90\%$, $SoH_{knee} = 70\%$, $SoH_{EoL} = 40\%$, $PV_{price} = 1300 \€/kWp$ and $BAT_{price} = 100 \€/kWh$.</figcaption></figure>

## V. CONCLUSION

This paper emphasizes that modeling the aging knee is fundamental for accurately estimating the viability of Second Life Batteries (SLBs) in a given system. A later knee point can significantly impact the economic interest in SLBs, as it predominantly affects how often batteries need replacement (CapEx). The initial State of Health (SoH) also plays a crucial role in determining performance and economic impact.

The purchase price of SLBs significantly influences the chosen battery capacity for a system, especially if economic gain is the primary objective. In our case, a purchase price of 100 €/kWh was identified as the break-even point, though this might differ if the focus is on
minimizing ecological impact or maximizing grid inde-
pendence by improving the Renewable Energy Share.

The biggest takeaway from this study is the need for
accurate models that capture the knee point based on
prior battery usage. This study highlights how crucial
battery parameters affect techno-economic simulations
and will be extended with a new model from an ongoing
aging campaign, incorporating additional degradation
factors.

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