While the second part of the 20th century was dominated by combustion engine powered vehicles, climate change and limited oil resources has been forcing car manufacturers and other companies in the mobility sector to switch to renewable energy sources. Electric engines supplied by Li-ion battery cells are on the forefront of this revolution in the mobility sector. A challenging but very important task hereby is the precise forecasting of the degradation of battery state-of-health and state-of-charge. Hence, there is a high demand in models that can predict the SOH and SOC and consider the specifics of a certain kind of battery cell and the usage profile of the battery. While traditional physics-based and data-driven approaches are used to monitor the SOH and SOC, they both have limitations related to computational costs or that require engineers to continually update their prediction models as new battery cells are developed and put into use in battery-powered vehicle fleets. In this dissertation, we enhance a hybrid physics-informed machine learning version of a battery SOC model to predict voltage drop during discharge. The enhanced model captures the effect of wide variation of load levels, in the form of input current, which causes large thermal stress cycles. The cell temperature build-up during a discharge cycle is used to identify temperature-sensitive model parameters. Additionally, we enhance an aging model built upon cumulative energy drawn by introducing the effect of the load level. We then map cumulative energy and load level to battery capacity with a Gaussian process model. To validate our approach, we use a battery aging dataset collected on a self-developed testbed, where we used a wide current level range to age battery packs in accelerated fashion. Prediction results show that our model can be successfully calibrated and generalizes across all applied load levels.