michaelbosello/battery-rul-estimation

Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs

Hey there! 👋

Interested in Reinforcement Learning, Deep Learning, Autonomous Robotics

battery-rul-estimation's Issues

base path in unibo rul estimation

dataset = UniboPowertoolsData(
test_types=[],
chunk_size=1000000,
lines=[37, 40],
charge_line=37,
discharge_line=40,
base_path="data/unibo-powertools-dataset/unibo-powertools-dataset/test_result.csv"
)

error:
FileNotFoundError Traceback (most recent call last)
Input In [13], in <cell line: 1>()
----> 1 dataset = UniboPowertoolsData(
2 test_types=[],
3 chunk_size=1000000,
4 lines=[37, 40],
5 charge_line=37,
6 discharge_line=40,
7 base_path="Desktop/battery unibo/battery-rul-estimation-main/battery-rul-estimation/data/unibo-powertools-dataset/unibo-powertools-dataset/test_result.csv"
8 )

File ~\Desktop\battery unibo\battery-rul-estimation-main\battery-rul-estimation\experiments\unibo-powertools../..\data_processing\unibo_powertools_data.py:79, in UniboPowertoolsData.init(self, test_types, chunk_size, lines, charge_line, discharge_line, base_path)
76 self.cyc_path = base_path + TEST_RESULT_DATA_PATH
77 self.cap_path = base_path + TEST_RESULT_TRIAL_END_DATA_PATH
---> 79 self.__load_raw_data()

File ~\Desktop\battery unibo\battery-rul-estimation-main\battery-rul-estimation\experiments\unibo-powertools../..\data_processing\unibo_powertools_data.py:82, in UniboPowertoolsData.__load_raw_data(self)
81 def __load_raw_data(self):
---> 82 self.__load_csv_to_raw()
83 self.__clean_cycle_raw()
84 self.__clean_capacity_raw()

File ~\Desktop\battery unibo\battery-rul-estimation-main\battery-rul-estimation\experiments\unibo-powertools../..\data_processing\unibo_powertools_data.py:92, in UniboPowertoolsData.__load_csv_to_raw(self)
88 def __load_csv_to_raw(self):
89 self.logger.debug("Start loading data with lines: %s, types: %s and chunksize: %s..." %
90 (self.lines, self.test_types, self.chunksize))
---> 92 iter_cyc = pd.read_csv(
93 self.cyc_path, chunksize=self.chunksize, iterator=True)
94 self.cycle_raw = pd.concat(self.__filter_raw_chunk(iter_cyc))
96 iter_cap = pd.read_csv(
97 self.cap_path, chunksize=self.chunksize, iterator=True)

File ~\anaconda3\lib\site-packages\pandas\util_decorators.py:311, in deprecate_nonkeyword_arguments..decorate..wrapper(*args, **kwargs)
305 if len(args) > num_allow_args:
306 warnings.warn(
307 msg.format(arguments=arguments),
308 FutureWarning,
309 stacklevel=stacklevel,
310 )
--> 311 return func(*args, **kwargs)

File ~\anaconda3\lib\site-packages\pandas\io\parsers\readers.py:680, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
665 kwds_defaults = _refine_defaults_read(
666 dialect,
667 delimiter,
(...)
676 defaults={"delimiter": ","},
677 )
678 kwds.update(kwds_defaults)
--> 680 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\lib\site-packages\pandas\io\parsers\readers.py:575, in _read(filepath_or_buffer, kwds)
572 _validate_names(kwds.get("names", None))
574 # Create the parser.
--> 575 parser = TextFileReader(filepath_or_buffer, **kwds)
577 if chunksize or iterator:
578 return parser

File ~\anaconda3\lib\site-packages\pandas\io\parsers\readers.py:933, in TextFileReader.init(self, f, engine, **kwds)
930 self.options["has_index_names"] = kwds["has_index_names"]
932 self.handles: IOHandles | None = None
--> 933 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\lib\site-packages\pandas\io\parsers\readers.py:1217, in TextFileReader._make_engine(self, f, engine)
1213 mode = "rb"
1214 # error: No overload variant of "get_handle" matches argument types
1215 # "Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]"
1216 # , "str", "bool", "Any", "Any", "Any", "Any", "Any"
-> 1217 self.handles = get_handle( # type: ignore[call-overload]
1218 f,
1219 mode,
1220 encoding=self.options.get("encoding", None),
1221 compression=self.options.get("compression", None),
1222 memory_map=self.options.get("memory_map", False),
1223 is_text=is_text,
1224 errors=self.options.get("encoding_errors", "strict"),
1225 storage_options=self.options.get("storage_options", None),
1226 )
1227 assert self.handles is not None
1228 f = self.handles.handle

File ~\anaconda3\lib\site-packages\pandas\io\common.py:789, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
784 elif isinstance(handle, str):
785 # Check whether the filename is to be opened in binary mode.
786 # Binary mode does not support 'encoding' and 'newline'.
787 if ioargs.encoding and "b" not in ioargs.mode:
788 # Encoding
--> 789 handle = open(
790 handle,
791 ioargs.mode,
792 encoding=ioargs.encoding,
793 errors=errors,
794 newline="",
795 )
796 else:
797 # Binary mode
798 handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'Desktop/battery unibo/battery-rul-estimation-main/battery-rul-estimation/data/unibo-powertools-dataset/unibo-powertools-dataset/test_result.csvdata/unibo-powertools-dataset/unibo-powertools-dataset/test_result.csv'

what is the version of tensorflow in this code?

Hi, thanks for sharing this great work. The paper and code have inspired me a lot.
I want to reproduce your result, so i would like to ask what version of tensorflow you used in this code? thanks in advance

ZeroDivisionError: float division by zero

train_data_test_names = [
'000-DM-3.0-4019-S',
'001-DM-3.0-4019-S',
'002-DM-3.0-4019-S',
'006-EE-2.85-0820-S',
'007-EE-2.85-0820-S',
'018-DP-2.00-1320-S',
'019-DP-2.00-1320-S',
'036-DP-2.00-1720-S',
'037-DP-2.00-1720-S',
'038-DP-2.00-2420-S',
'040-DM-4.00-2320-S',
'042-EE-2.85-0820-S',
'045-BE-2.75-2019-S'
]

test_data_test_names = [
'003-DM-3.0-4019-S',
'008-EE-2.85-0820-S',
'039-DP-2.00-2420-S',
'041-DM-4.00-2320-S',
]

dataset.prepare_data(train_data_test_names, test_data_test_names)

Error:

2022/06/28 14:03:12 [DEBUG]: Start preparing data for training: ['000-DM-3.0-4019-S', '001-DM-3.0-4019-S', '002-DM-3.0-4019-S', '006-EE-2.85-0820-S', '007-EE-2.85-0820-S', '018-DP-2.00-1320-S', '019-DP-2.00-1320-S', '036-DP-2.00-1720-S', '037-DP-2.00-1720-S', '038-DP-2.00-2420-S', '040-DM-4.00-2320-S', '042-EE-2.85-0820-S', '045-BE-2.75-2019-S'] and testing: ['003-DM-3.0-4019-S', '008-EE-2.85-0820-S', '039-DP-2.00-2420-S', '041-DM-4.00-2320-S']...
/content/drive/My Drive/battery-state-estimation/battery-state-estimation/data_processing/unibo_powertools_data.py:273: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
cyc_data = np.array(cyc_data)
2022/06/28 14:03:36 [DEBUG]: Finish getting training and testing charge data.
2022/06/28 14:03:48 [DEBUG]: Finish getting training and testing discharge data.
2022/06/28 14:03:48 [DEBUG]: Finish cleaning training and testing charge data.
2022/06/28 14:03:48 [DEBUG]: Finish cleaning training and testing discharge data.

ZeroDivisionError Traceback (most recent call last)
in ()
22 ]
23
---> 24 dataset.prepare_data(train_data_test_names, test_data_test_names)

1 frames
/content/drive/My Drive/battery-state-estimation/battery-state-estimation/data_processing/unibo_powertools_data.py in prepare_data(self, train_names, test_names)
218
219 self.train_discharge_cyc = self.__add_discharge_soc_pars(
--> 220 self.train_discharge_cyc, self.train_charge_cap
221 )
222 self.test_discharge_cyc = self.__add_discharge_soc_pars(

/content/drive/My Drive/battery-state-estimation/battery-state-estimation/data_processing/unibo_powertools_data.py in __add_discharge_soc_pars(self, discharge_cyc, charge_cap)
311 np.zeros(discharge_cyc[i].shape[0])]
312 discharge_cyc[i][:, -1] = (charge_cap[i][CapacityCols.CHARGING_CAPACITY] - discharge_cyc[i]
--> 313 [:, CycleCols.DISCHARGING_CAPACITY]) / charge_cap[i][CapacityCols.CHARGING_CAPACITY]
314
315 # Time remaining to cycle end: (Time of last row in cycle - current time)

ZeroDivisionError: float division by zero