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Time [s]
float64
Time diff [s]
float64
Current [C-rate]
float64
Terminal voltage [V]
float64
Surface temperature [degC]
float64
Ambient temperature [degC]
float64
Cooling alpha [W.m-2.K-1]
float64
0
0.9008
0
3.117589
22.599245
20
20
0.9008
1.0471
0
3.117609
22.599245
20
20
1.9479
10.9995
-0.973207
3.302082
22.640877
20
20
12.9474
13.0009
-0.973242
3.367038
22.659269
20
20
25.9483
9.9994
-0.973371
3.41676
22.672314
20
20
35.9477
11.0001
-0.973151
3.447459
22.719405
20
20
46.9478
13.0008
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3.475767
22.780374
20
20
59.9486
8.9989
-0.973199
3.503685
22.867331
20
20
68.9475
8.9999
-0.973356
3.520206
22.850372
20
20
77.9474
10.0001
-0.973421
3.534857
22.924334
20
20
87.9475
11.0009
-0.973217
3.548959
22.989256
20
20
98.9484
12
-0.973183
3.56237
23.03907
20
20
110.9484
8.9992
-0.97331
3.57462
23.186407
20
20
119.9476
9.0007
-0.973367
3.582173
23.263422
20
20
128.9483
7.9996
-0.973252
3.588323
23.308735
20
20
136.9479
11.0007
-0.973234
3.592829
23.366961
20
20
147.9486
11.9995
-0.97315
3.597603
23.472601
20
20
159.9481
13.9993
-0.973405
3.601791
23.627974
20
20
173.9474
10.0002
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3.605901
23.739815
20
20
183.9476
8.0002
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3.608937
23.843172
20
20
191.9478
6.0007
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3.611161
23.871397
20
20
197.9485
17.9994
-0.973357
3.61288
24.01001
20
20
215.9479
7.0002
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3.618299
24.147738
20
20
222.9481
5.9998
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3.620503
24.234501
20
20
228.9479
14.9999
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3.622126
24.311344
20
20
243.9478
12.0007
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3.626658
24.42042
20
20
255.9485
1.9997
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24.543215
20
20
257.9482
10.9993
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3.630868
24.516445
20
20
268.9475
14.0008
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3.633997
24.63303
20
20
282.9483
9.9998
-0.973342
3.638239
24.693814
20
20
292.9481
10.9996
-0.973364
3.6411
24.804508
20
20
303.9477
13.9999
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3.644506
24.935272
20
20
317.9476
3.9999
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3.648582
25.016459
20
20
321.9475
18.001
-0.973272
3.649583
25.005785
20
20
339.9485
0.9996
-0.973346
3.654933
25.168685
20
20
340.9481
10.9995
-0.973416
3.65515
25.16869
20
20
351.9476
16.9998
-0.973276
3.658327
25.186577
20
20
368.9474
7.0011
-0.973386
3.663353
25.439753
20
20
375.9485
11.9997
-0.973284
3.665213
25.407273
20
20
387.9482
6.0003
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3.668765
25.448809
20
20
393.9485
15.9998
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3.670399
25.557076
20
20
409.9483
6.9998
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3.674939
25.599867
20
20
416.9481
14.9994
-0.973243
3.676979
25.676933
20
20
431.9475
10
-0.973276
3.681183
25.685038
20
20
441.9475
0.9999
-0.97345
3.683851
25.774555
20
20
442.9474
13.0003
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3.684226
25.774555
20
20
455.9477
11.0002
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3.687743
25.777229
20
20
466.9479
8.0003
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3.690924
25.877029
20
20
474.9482
8.9991
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3.693213
25.922909
20
20
483.9473
18.0007
-0.973105
3.695565
26.011131
20
20
501.948
7.9997
-0.973385
3.700581
26.068539
20
20
509.9477
13
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3.702712
26.071249
20
20
522.9477
7.0001
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3.706305
26.080158
20
20
529.9478
10.9997
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3.708254
26.186934
20
20
540.9475
9.0005
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3.711025
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20
20
549.948
14.9996
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3.713433
26.217726
20
20
564.9476
6.0001
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3.717212
26.284935
20
20
570.9477
11.0004
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3.718582
26.318726
20
20
581.9481
13.9996
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3.721406
26.344532
20
20
595.9477
9.0003
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3.724544
26.339195
20
20
604.948
4.9995
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3.726739
26.386406
20
20
609.9475
16.0003
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3.727723
26.420626
20
20
625.9478
3.9999
-0.973518
3.731401
26.449995
20
20
629.9477
12.0005
-0.973422
3.732139
26.470947
20
20
641.9482
5.9993
-0.973327
3.734728
26.467426
20
20
647.9475
16.001
-0.973383
3.735854
26.512358
20
20
663.9485
9.9995
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3.739168
26.526075
20
20
673.948
15
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3.741122
26.574116
20
20
688.948
0.9998
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3.743998
26.678663
20
20
689.9478
13.0003
-0.973348
3.744083
26.678669
20
20
702.9481
13.9996
-0.973434
3.746425
26.712431
20
20
716.9477
7.9999
-0.973167
3.749157
26.630642
20
20
724.9476
11.0009
-0.973401
3.750514
26.700909
20
20
735.9485
8.9999
-0.973464
3.752484
26.786654
20
20
744.9484
8.9993
-0.973329
3.754314
26.741781
20
20
753.9477
8.9997
-0.973526
3.755862
26.781843
20
20
762.9474
17
-0.973397
3.757672
26.82493
20
20
779.9474
3.0006
-0.973403
3.760736
26.848896
20
20
782.948
10.9995
-0.973398
3.761453
26.855602
20
20
793.9475
15.9998
-0.973378
3.763331
26.888054
20
20
809.9473
3.0004
-0.973218
3.766509
26.898275
20
20
812.9477
18.9999
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3.766969
26.883646
20
20
831.9476
1.0003
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3.77076
26.913425
20
20
832.9479
18.0003
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3.770873
26.913425
20
20
850.9482
3
-0.973415
3.774384
26.939164
20
20
853.9482
16.9992
-0.973189
3.774953
26.928056
20
20
870.9474
6.0001
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3.778445
26.953873
20
20
876.9475
16.0005
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3.779558
26.917389
20
20
892.948
1.9995
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3.782757
26.910717
20
20
894.9475
15.9999
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3.783242
26.910717
20
20
910.9474
12.0007
-0.973532
3.786381
27.011166
20
20
922.9481
2.9995
-0.973385
3.788923
27.026686
20
20
925.9476
11.0001
-0.973071
3.789388
27.040905
20
20
936.9477
16
-0.973201
3.791703
27.041828
20
20
952.9477
13.0001
-0.973308
3.794768
27.061424
20
20
965.9478
9.0001
-0.973312
3.797335
27.080456
20
20
974.9479
2.9996
-0.973049
3.799069
27.072454
20
20
977.9475
19.0009
-0.973376
3.79978
27.094673
20
20
996.9484
1
-0.97337
3.803419
27.110706
20
20
997.9484
16.9996
-0.973204
3.803524
27.089377
20
20
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batgrad datasets

Normalized battery time-series data for the batgrad template.

Included dataset IDs:

  • pozzato-2022
  • synthetic-pozzato-2022-m50t

The repository preserves the canonical type=.../dataset=.../source=normalized/... paths expected by batgrad. Download both datasets with:

uv run scripts/hf_assets.py download \
  --dataset pozzato-2022 synthetic-pozzato-2022-m50t

Provenance

The published-data portion is a modified derivative of Gabriele Pozzato, Anirudh Allam, and Simona Onori, "Lithium-ion battery aging dataset based on electric vehicle real-driving profiles," Data in Brief 41, 107995, 2022. doi:10.1016/j.dib.2022.107995

The files are not the original dataset. Processing by batgrad includes canonical mapping, type conversion, validation, derived features, protocol-specific sharding, normalization, and resampling/downsampling. The original authors are not responsible for these modifications.

The synthetic portion contains randomized profiles generated with PyBaMM using a modified OKane2022 parameter set. Particle cracking was disabled, and the LLI/LAM parameters were increased.

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Models trained or fine-tuned on marplan6/batgrad