# A Model-Driven Approach for Conducting Simulation Experiments
## 5 Evaluation
### Increasing Productivity and Quality for Complex Experiments
#### 5.2.2 Sensitivity Analysis of a Battery Model  
Deep brain stimulation is a therapy option for a multitude of neurological disorders. While it is widely implemented in the clinical routine, especially for [PERSON]'s disease and dystonia, the underlying mechanisms are not fully understood. Since most experiments cannot be performed directly on humans due to ethical reasons, animal testings are necessary. However, rodent-specific implants require a highly optimized level of miniaturization and power consumption compared to human implants. Physical prototyping is not feasible as the pursued runtime is more than a year. Therefore, virtual prototyping is used to design neurostimulators for rodents. The model by [PERSON] et al. [49] considers the interplay between various heterogeneous system components (i.e., battery, boost converter, micro-controller, and stimulation unit) and, thus, combines digital, as well as analog components. Whereas previous studies focused on the optimization of voltage levels inside the system, this study focuses on understanding the implications of different battery parameters on the overall runtime of the implant. The described SA experiment and the analysis results are available at [[https://github.com/SFB-ELAINE/Case-Study-Neurostimulator](https://github.com/SFB-ELAINE/Case-Study-Neurostimulator)]([https://github.com/SFB-ELAINE/Case-Study-Neurostimulator](https://github.com/SFB-ELAINE/Case-Study-Neurostimulator)) (accessed on 26 July 2022). Unfortunately, the battery model itself cannot be provided, as it is part of a closed-source project.  
As they vary over different battery types, the internal resistance Ri, the battery capacity Q, and the polarization constant K are of special interest. To facilitate the analysis of these parameters, the modeler uses the model-driven framework and selects the meta model for global SA (Table 3) to enhance an already specified base experiment. Parameter ranges and Gaussian distributions are requested for the three factors and entered by the user, and a QMC sampling is selected with 1000 samples. A schematic of the generated code, based on the Python package Uncertainpy [67] and SystemC-AMS [56], is shown in Figure 4B. The generated experiment was started automatically; however, due to the complexity of the model, even after 12 h, the analysis did not converge. Therefore, the experiment  
Figure 5: First- and total-order Sobol indices calculated for the three models. Whereas the Wnt model (**A**) and the electric fields model (**C**) show linear behaviors, the difference between the first- and total-order values of the battery model (**B**) indicates that the behavior of the model is governed by the interaction of parameters.  
was terminated by the user to find a more efficient approach. With the assistance of the model-driven framework, the sampling strategy could easily be substituted by PC as the meta model for global SA provides a number of different sampling strategies to choose from and automatically accounts for additional inputs or constraints associated with this other method. Overall, the MDE framework allows the user to quickly try out and compare different methods, e.g., also different sensitivity indices might be compared in the same way.  
Using a PC, the described experiment was automatically repeated and the runtime could be reduced to less than 3 h. The SA results, visualized by the user, are depicted in Figure 5B and show that the model response is strongly determined by interactions between the parameters. The internal resistance seems to have a particularly strong effect on the overall battery runtime. Consequently, one should aim for measuring this parameter in (real) experiments and pre-select batteries with lower internal resistance.