## 1 Introduction  
The market share of mild hybrid vehicles has been increasing, as they provide an easy and effective compromise between the challenging BEV technology and the drawbacks of ICE vehicles when it comes to satisfying set ecological standards. The efficiency of a mild hybrid vehicle depends largely on the battery-related hardware and software. Higher battery capacities and power capabilities are directly connected to a lower fuel consumption, as well as higher weight and costs. Thus, more accurate BMS software is the key to a viable cost-benefit ratio. An accurate prediction of battery voltage levels after a certain current load is of great significance for vehicle energy and power management systems (EPMSs). Using these values, the EPMS is able to adapt a strategy--either to reduce the load of the consumer or increase the energy recovered from the electrical machine. This ensures that the battery is capable of providing or storing energy for the drive train in more situations without exceeding legal voltage limits. Therefore, the overall energy efficiency can be increased and the fuel consumption decreased [1,2].  
Conventional battery models use equivalent circuit models (ECM) to estimate the voltage by modeling the electrochemical processes that take place in a battery during discharging or charging. Such models are very accurate, but require significant modeling effort. Knowledge about internal battery processes and their impact on the voltage behavior is necessary to conduct proper measurements and parameterize RC circuits appropriately. Measurement methods such as electrochemical impedance spectroscopy have to be carried out with accurately calibrated and precisely adjusted equipment. The experimental data then must be processed mathematically to obtain the modeling parameters. An online prediction of the terminal voltage was presented by [PERSON] et al. [3]. [PERSON] et al. used ECM as an input for their SOC estimation [4]. [PERSON] et al. showed the applicability of ECM to LTO batteries [5].  
Interest in modeling battery behavior using machine learning (ML) algorithms has recently been increasing. This trend has been enabled by an increase in both CPU and GPU power, whereby research activity is expected to increase dramatically in the field of ML. Most ML models in this area cover the field of SOC prediction, through the use of fuzzy logic [6], neural networks (NNs) [7], deep NN [8], LSTM cells [9; 10] and gated recurrent units (GRUs) [11; 12]. [PERSON] et al. [13] presented an approach on convolutional GRU. [PERSON] et al. [14] presented a comparison of SOC models based on FNN, RNN and Kalman filter models. Their comparison showed that RNNs deliver better results than FNN and similar results as models with Kalman filters. Extended Kalman filters [15] and cascaded Kalman filters [16] indicate improvements for models with Kalman filters. The battery state of health (SOH) is highly nonlinear and therefore ML is an appropriate approach. [PERSON] et al. [17] modeled the SOH with an FNN, [PERSON] et al. implemented an RNN [18] and [PERSON] et al. [19] used an LSTM.  
Some ML methods also use approaches to model the voltage behavior with acceptable accuracy. These approaches face the same problems as conventional ECMs, as auxiliaries such as the SOC or coulomb counters are a mandatory input [3; 20]. The proposed method uses only measurable physical parameters as input, in order to estimate the battery voltage. This has the advantage of easier adaption to other cell chemistries, causing fewer errors due to upstream inaccuracies.  
The theory of dense, dropout and LSTM layers are described in Section 2. Section 3 outlines the methods for pre-processing the raw data to input data. Section 4 presents the model architecture and the associated training process. Thereafter, Section 5 validates the resulting model with separate data. Our conclusion is drawn in Section 6.