Spatial-temporal compression of sensing data for sensor network
Spatial-temporal compression of sensing data from a plurality of sensors involves using a plurality of sensors to obtain measured physical data associated with a plurality of sensed elements. At each of a plurality of sampling times a set of N sampled data is acquired from the N sensed elements corresponding to the measured physical data. The sets of sampled data are analyzed to obtain statistical characteristic information. Thereafter, one or more frames of compressed data is generated using the statistical characteristic information to facilitate temporal and spatial data compression.
1. A method for spatial-temporal compression of sensing data from a plurality of battery sensors in a battery management system, comprising:
using a plurality of N battery sensors to respectively obtain measured physical data associated with a plurality of N battery cells;
acquiring at each of a plurality of sampling times a set of N sampled data from the N battery cells corresponding to the measured physical data;
analyzing the sets of N sampled data acquired at the plurality of sampling times to obtain statistical characteristic information for one or more of the sets of N sampled data; and
obtaining at least one frame of compressed data using the statistical characteristic information in a differential encoding process to facilitate at least one of temporal and spatial data compression of the one or more sets of N sampled data acquired at the plurality of sampling times, wherein at least one of a temporal and a spatial domain characteristic of the N battery cells facilitate lossless data compression.
2. The method according to claim 1 , wherein the statistical characteristic information comprises one or more selected from a group consisting of minimum, mean, and median data values.
3. The method according to claim 2 , wherein the statistical characteristic information further comprises special status information indicating an abnormal condition of the battery cell.
4. The method according to claim 1 , wherein the differential encoding process comprises reference-based differential block coding (RDBC).
5. The method according to claim 1 , further comprising communicating the at least one frame of compressed data to a remote data receiver, and decompressing the at least one frame of compressed data to recover the one or more sets of N sampled data obtained from each of the plurality of sampling times.
6. The method according to claim 1 , where the measured physical data is battery cell voltage data.
7. The method according to claim 1 , where the physical data is selected from group consisting of battery cell voltage, battery cell temperature, and battery cell current data.
8. The method according to claim 7 , wherein the remote data receiver comprises a battery management unit configured to monitor a status of the plurality of N battery cells.
9. The method according to claim 1 , wherein a spatial domain for purposes of the spatial data compression is defined by the set of N sampled data from the N battery cells at a certain sampling time, and a temporal domain for purposes of temporal data compression is defined by a set of sampled data from a same battery sensor at different sampling times.
10. The method according to claim 9 , wherein the differential encoding process includes both the temporal data compression and the spatial data compression of the one or more sets of N sampled data.
11. The method according to claim 10 , wherein the differential encoding process comprises data compression in the temporal domain followed by data compression in the spatial domain.
12. The method according to claim 9 , further comprising dynamically varying the differential encoding process to include a data compression type selected from a group consisting of temporal data compression, spatial data compression, and a combination of temporal and spatial data compression.
13. The method according to claim 12 , wherein the dynamic variation of the differential encoding process is performed so as to maximize a compression ratio of each said frame while also ensuring lossless data transmission.
14. A method for spatial-temporal compression of battery data obtained from a multiplicity of battery sensors associated with a battery management system, comprising:
using a plurality of N battery sensors to respectively obtain measured physical data associated with a plurality of N battery cells;
acquiring at each of a plurality of sampling times a set of N sampled data from the N battery cells corresponding to the measured physical data;
obtaining at least one frame of compressed data using a reference-based differential block coding (RDBC) process to facilitate at least one of temporal and spatial data compression of one or more sets of N sampled data acquired at the plurality of sampling times;
communicating the at least one frame of compressed data to a battery management unit configured to monitor a status of the multiplicity of battery cells, and decompressing the at least one frame of compressed data to recover the one or more sets of N sampled data obtained from each of the plurality of sampling times;
wherein a spatial domain for purposes of the spatial data compression is defined by the set of N sampled data from the N battery cells at a certain sampling time, and a temporal domain for purposes of temporal data compression is defined by a set of sampled data from a same battery sensor at different sampling times.
15. The method according to claim 14 , where the measured physical data is selected from a group consisting of a battery cell voltage, a battery cell temperature, and a battery cell current data.
16. The method according to claim 14 , wherein the RDBC process comprises data compression in the temporal domain followed by data compression in the spatial domain.
17. The method according to claim 14 , further comprising dynamically varying the RDBC process to facilitate a data compression type selected from a group consisting of temporal data compression, spatial data compression, and a combination of temporal and spatial data compression.
18. The method according to claim 17 , wherein the dynamic variation of the RDBC process is performed so as to maximize a compression ratio of each said frame while also ensuring lossless data transmission.
19. The method according to claim 14 , wherein at least one of a temporal and a spatial domain characteristic of the N battery cells facilitates lossless data compression.