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Electric Vehicle (EV) penetration and renewable energies enables synergiesbetween energy supply, vehicle users, and the mobility sector. However, alsonew issues arise for car manufacturers: During charging and discharging of EVbatteries a degradation (battery aging) occurs that correlates with a valuedepreciation of the entire EV. As EV users' satisfaction depends on reliableand value-stable products, car manufacturers offer charging assistants forsimplified and sustainable EV usage by considering individual customer needsand battery aging. Hitherto models to quantify battery aging have limitedpracticability due to a complex execution. Data-driven methods hold feasiblealternatives for SOH estimation. However, the existing approaches barely useuser-related data. By means of a linear and a neural network regression model,we first estimate the energy consumption for driving considering individualdriving styles and environmental conditions. In following work, the consumptionmodel trained on data from batteries without degradation can be used toestimate the energy consumption for EVs with aged batteries. A discrepancybetween the estimation and the real consumption indicates a battery agingcaused by increased internal losses. We then target to evaluate the influenceof charging strategies on battery degradation.

Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility