Battery management system for an electric air vehicle
A battery management system and method for performing a battery health parameter observation, in particular, cell impedance observation, with two redundant, independent and dissimilar lanes. Specifically, a cell impedance observation in a first one of the lanes is based on Electrochemical Impedance Spectroscopy, EIS. The other lane employs a different algorithm than EIS. In embodiments, a battery state observation is further performed independently by the two lanes, wherein again the first lane employs EIS and the other lane a different (dissimilar) algorithm. On the basis of state and health observation, state (state of function) of the battery system can be predicted to determine a range of flight in accordance with a predetermined flight profile.
1. A battery management system for an electric air vehicle, the battery management system being adapted to observe a current battery health of a battery system forming an energy storage system of the air vehicle, the battery management system comprising two redundant and dissimilar lanes for battery cell measurements, wherein:
a first lane of the two lanes comprises equipment to determine a cell impedance as a battery health parameter for each individual battery cell of a plurality of battery cells of the battery system by using Electrochemical Impedance Spectroscopy (EIS); and
a second lane of the two lanes comprises equipment to perform cell impedance determination using a different algorithm than EIS.
2. The system according to claim 1 , wherein the battery management system for the electric air vehicle is adapted for battery state observation, and wherein:
each of the lanes comprises equipment to perform a state-of charge (SOC) or a cell core temperature determination of each individual battery cell;
the determination by the first lane is based on EIS; and
the determination by the second lane is based on a different algorithm than EIS.
3. The system according to claim 1 , wherein the second lane utilizes a model-based cell impedance estimation algorithm or cell core temperature estimation algorithm.
4. The system according to claim 3 , wherein the model-based cell impedance estimation algorithm or algorithms utilized by the second lane utilize a dual Kalman filter.
5. The system according to claim 1 , wherein the second lane comprises equipment to measure at least one of a current, a voltage and a temperature of each individual cell of the plurality of battery cells of the battery system.
6. The system according to claim 1 , wherein the first lane comprises equipment to excite battery cells of the battery system with sinusoidal current of variable frequency and equipment to excite a voltage response of each cell.
7. The system according to claim 6 , wherein the first lane further comprises processing circuitry to calculate a system impedance spectrum based on a ratio between an input excitation current and a voltage response.
8. The system according to claim 1 , wherein the air vehicle is an electric vertical take-off and landing aircraft (eVTOL).
9. An air vehicle comprising a battery management system for an electric air vehicle, the battery management system being adapted to observe a current battery health of a battery system forming an energy storage system of the air vehicle, the battery management system comprising two redundant and dissimilar lanes for battery cell measurements, wherein:
a first lane of the two lanes comprises equipment to determine a cell impedance as a battery health parameter for each individual battery cell of a plurality of battery cells of the battery system by using Electrochemical Impedance Spectroscopy (EIS); and
a second lane of the two lanes comprises equipment to perform cell impedance determination using a different algorithm than EIS.
10. A battery management method of observing a current battery health of a battery system forming an energy storage system of an electric air vehicle, the battery management method comprising:
determining a cell impedance as a battery health parameter for each individual battery cell of a plurality of battery cells of the battery system utilizing Electrochemical Impedance Spectroscopy (EIS); and
independently determining the cell impedance for each individual battery cell of the plurality of battery cells as the battery health parameter based on a different algorithm than EIS.
11. The method according to claim 10 , further comprising:
determining a state-of-charge (SOC) or a cell core temperature of each individual battery cell based on EIS; and
independently determining the state-of-charge or the cell core temperature of each individual battery cell at least partially based on a different algorithm than EIS.
12. The method according to claim 11 , wherein the different algorithm is a model-based cell impedance estimation algorithm.
13. The method according to claim 12 , wherein the model-based cell impedance estimation algorithm utilized by a second lane utilizing a dual Kalman filter.
14. The method according to claim 12 , wherein the model-based cell impedance estimation algorithm comprises measuring at least one of a current, a voltage and a temperature of each individual cell of the plurality of battery cells of the battery system.
15. The method according to claim 10 , wherein utilizing EIS comprises:
exciting battery cells of the battery system with sinusoidal current of variable frequency and measuring a voltage response of each cell.