*2.8. Test*

We tested the final GB model against the two remaining experimental data sets (half cycles and synthetic load profile). Again, we used the standard odeint backpropagation method from torchdiffeq. We tried to solve the differential equation system using Dopri8 with an absolute tolerance of 10−<sup>5</sup> and relative tolerance of 10−3. However, for the half cycles, this resulted in a step size underflow. Therefore, we changed the absolute tolerance to 10−<sup>3</sup> for the half cycles.

For both test data sets, we had to provide initial values for the SOC and *v*RC1. We initialised these values as before during training: We set *v*RC1(*t* = 0) = 0 V and derived the initial SOC from the battery voltage.

#### **3. Results and Discussion**

The training and test results are discussed in the following sections. First, the focus is on the training results, with the goal of selecting an appropriate number of hidden neurons in *f* ∗ and *g*∗ and of training epochs. Secondly, we compare the training results to the measurement data. Finally, simulations with the GB model are compared against the further test data sets.
