diff --git a/OMPython/modelica_system_abc.py b/OMPython/modelica_system_abc.py
index 6ddb8f5d..15261141 100644
--- a/OMPython/modelica_system_abc.py
+++ b/OMPython/modelica_system_abc.py
@@ -5,6 +5,7 @@
 
 import abc
 import ast
+import csv
 from dataclasses import dataclass
 import logging
 import numbers
@@ -216,6 +217,15 @@ def _xmlparse(self, xml_file: OMPathABC):
         root = tree.getroot()
         if root is None:
             raise ModelicaSystemError(f"Cannot read XML file: {xml_file}")
+        # check OM version - force the version used by the model executable
+        if 'generationTool' in root.attrib:
+            generation_tool_version = self._parse_om_version(version=root.attrib['generationTool'])
+            if self._version != generation_tool_version:
+                logger.warning(f"Mismatch in OpenModelica version: {self._version!r} (OMSession) "
+                               f"vs. {generation_tool_version!r} (model executable) "
+                               f"- using {generation_tool_version!r}!")
+                self._version = generation_tool_version
+
         for attr in root.iter('DefaultExperiment'):
             for key in ("startTime", "stopTime", "stepSize", "tolerance",
                         "solver", "outputFormat"):
@@ -927,6 +937,29 @@ def setOptimizationOptions(
             datatype="optimization-option",
             overridedata=None)
 
+    @staticmethod
+    def toInputs(data: dict[str, list[float]]) -> dict[str, list[tuple[float, float]]]:
+        """
+        Converts a dictionary of lists (from pandas DataFrame.to_dict(orient='list'))
+        into the OMPython setInputs input format.
+
+        Example: mod.setInputs(**toInputs(pdf.to_dict(orient='list')))
+
+        Assumes the dictionary contains a key named 'time'.
+        """
+        if "time" not in data:
+            raise ValueError("The provided data must contain a 'time' key.")
+
+        time_series = data["time"]
+
+        inputs = {
+            var_name: list(zip(time_series, values))
+            for var_name, values in data.items()
+            if var_name != "time"
+        }
+
+        return inputs
+
     def setInputs(
             self,
             *args: Any,
@@ -989,6 +1022,44 @@ def setInputs(
 
         return True
 
+    def setInputsCSV(
+            self,
+            csvfile: os.PathLike,
+    ) -> None:
+        """
+        Read content from a CSV file and use it to define the time based input data.
+        """
+
+        # real type is 'dict[str, list[tuple[float, float]]]' - 'dict[str, Any]' is used to make setInputs() happy
+        inputs: dict[str, Any] = {}
+        try:
+            with open(csvfile, newline='') as csvfh:
+                dialect = csv.Sniffer().sniff(csvfh.read(1024))
+                csvfh.seek(0)
+                reader = csv.DictReader(csvfh, dialect=dialect)
+
+                keys: list[str] = []
+                for idx, line in enumerate(reader):
+                    if not keys:
+                        keys = list(line.keys())
+                        for var in keys[1:]:
+                            if var in inputs:
+                                raise ModelicaSystemError(f"Error reading {csvfile}: duplicated column {var}!")
+                            inputs[var] = []
+                    try:
+                        # use key[0] as time; all other columns use the header as name
+                        for var in keys[1:]:
+                            inputs[var].append((float(line[keys[0]]), float(line[var])))
+                    except (ValueError, TypeError) as exc2:
+                        raise ModelicaSystemError(f"Invalid value reading {csvfile} line {idx}/{var}: "
+                                                  f"{line}!") from exc2
+
+        except IOError as exc1:
+            raise ModelicaSystemError(f"Error reading {csvfile}: {exc1}") from exc1
+
+        if inputs:
+            self.setInputs(**inputs)
+
     def _createCSVData(self, csvfile: Optional[OMPathABC] = None) -> OMPathABC:
         """
         Create a csv file with inputs for the simulation/optimization of the model. If csvfile is provided as argument,