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Battery aging is a main concern within hybrid and electrical cars. Determining the current state-of-health (SOH) of the battery on board of a vehicle is still a challenging task. Electrochemical Impedance Spectroscopy (EIS) is an established laboratory method for the characterization of electrochemical energy storages such as Lithium-Ion (Li-Ion) cells. EIS provides a lot of information about electrochemical processes and their change due to aging. Therefore it can be used to estimate the current SOH of a cell. Standard EIS methods require the excitation of the cell with a certain waveform for obtaining the impedance spectrum. This waveform can be a series of monofrequent sinusoidal signals or a time-domain current pulse with a dedicated Fourier spectrum. However, any form of dedicated perturbation is not generally applicable on board of an electric vehicle. This work presents a new passive spectroscopy method, which obtains the impedance spectrum directly out of real driving data.

This work provides a new method for estimating the capacity of an automotive Lithium-Ion cell under real application conditions present in Hybrid and Electrical vehicles. Reliable online capacity estimation is needed for accurate prediction of the remaining electrical driving range. This is a crucial criterion for customer acceptance of Electrical vehicles. Dynamic excitations of real driving cycles, temperature variation as well as the variation of electrical battery behavior with capacity and resistance degradation are challenges that need to be overcome. For this paper, a long-term aging study on 120 automotive Lithium-Ion cells is evaluated with respect to the correlation between electrical cell behavior, temperature and the cell capacity over the complete cell lifetime. The results are used for a dynamic state-space model which provides the current-voltage relationship valid for all aging states of the battery.

Lithium-ion batteries are the most favored energy storage technology for high-efficiency hybrid and electrical vehicles. Online State-of-Charge (SOC) estimation is required for this application to estimate the remaining cruising distance. However, variation of battery parameters with temperature and cycle life has to be taken into account in order to achieve high accuracy. In this work electrical tests on lithium-ion cells in different States-of-Health are performed and used to extract model parameters such as open-circuit-voltage and impedance. High precision test equipment has been developed to accurately track the true SOC of the cell during measurements. A strong influence of cycle-life on electrical battery behavior is observed. A dynamic cell model based on the measurement results including temperature and aging effects is generated and subsequently used for SOC estimation with an Extended-Kalman-Filter.