## 1 Introduction  
The battery is the last wearing part of an electric vehicle. It accounts for a significant proportion of its economic and ecological cost. The responsible management of this asset requires both accurate battery sizing and utility assessment. System sizing usually depends on cell manufacturer specifications and accelerated aging tests. Remaining capacity and internal resistance are often the prevailing indicators of the battery's aging state and hence its utility. These quantities have one common drawback: their measurements rely on bench testing, which is tedious, time-consuming, cell-specific and expensive. The artificial and often uniform test setups rarely reflect the actual usage behavior, a crucial factor in battery aging. Real applications are characterized by different charging and discharging cycles under diverse combinations of current rate, temperature, state of charge (SOC) and voltage, among other factors.  
In the FeBaL project [1] (\"Felddatenbasierte Batteriediagnose und Lebensdauerprognose\", i.e., \"Field data-based battery diagnosis and lifetime prediction\"), we take steps to overcome the disadvantages of usual battery measurement campaigns in the laboratory. We use real data recorded during everyday operation and consider the constraints of the usage profile exactly as they occur in practice. In the short term, using field data enables us to analyze the battery aging and detect conspicuous operation behavior. As a side effect, we save time and money by dispensing with laboratory tests. In the long term, we want to predict the application-specific capacity loss and resistance increase as well as the end-of-life (EOL) of batteries by simulating specific usage scenarios and/or usage recommendations.  
We have gathered data from multiple vehicle fleets, both buses and passenger cars. This large database offers many usage scenarios, covering a wide variety of input ranges and combinations. It also allows us to develop and examine machine learning (ML) algorithms that go beyond hitherto published approaches.  
In this paper, we present our battery model, a neural network tailored to lithium-ion batteries and applied to data generated by electric buses and cars. Originally developed for the analysis of a single vehicle, we extended our method to handle data of a vehicle fleet. This way, we optimize the use of our data based on a simple idea: more buses mean more data which cover larger operation ranges. The additional information can be transferred between vehicles and usage variations can be compensated for.  
The paper is divided as follows: after summarizing the contributions of this paper and giving some examples of their possible applications, the first main part highlights the features of our field data. Besides an overview of the different fleets we have at our disposal, we explain the data selection and which criteria must be met to qualify for the neural network training. Following a short review of the current state of research in battery aging diagnostics, we explicate the structure of the neural network model for vehicle fleets in the second part of the paper. We characterize features of the training process, as well. The third part focuses on our findings: we first show that our fleet model is valid, predicting the voltage with a root-mean-square error (RMSE) below 5 mV. Then, we look at the promising results of our transfer learning approach, where using data from across the fleet has improved the error rate. We also use our fleet model to investigate capacity decrease and resistance increase as scaling factors for a more realistic SOH definition. Finally, we give an outlook of our research plans for the fleet model in the future.