pyPicoSDK for EV Battery Characterization in India: Automated Cell Testing and Long-Duration Data Capture

India’s EV battery engineering is moving fast. Ola Electric, Ather Energy, Exponent Energy, Log9 Materials, TVS iQube, Bajaj Chetak, the major Indian passenger and commercial OEM EV programs, and the Tier-1 supplier base in Pune, Chennai, and Bengaluru are all running serious cell and pack characterization programs, and the test instrumentation tolerances those programs demand are different from what automotive bench engineering has historically needed in India.

Battery characterization is a long-duration, high-channel-count, mixed-measurement-type workload. You need to capture cell voltage (millivolt resolution, kilohertz bandwidth during load steps, hour-long soak runs at slow sample rates), cell current (ampere range, accurate through both steady-state and transient), cell and pack temperature (at multiple points), and often environmental data like chamber temperature, chamber humidity, and busbar vibration. None of this fits a traditional “trigger a scope, read a waveform, plot in Matlab” workflow.

pyPicoSDK with the right PicoScope and PicoLog hardware underneath it solves this, and cleanly. This post walks through the measurement architecture Indian EV battery teams actually deploy, which Pico instrument is right for which measurement, the Python code patterns for multi-day automated capture, and how to tie the results into the pack-level simulation and drive-cycle validation workflows that every Indian OEM and Tier-1 is building. GSAS Micro Systems is India’s authorized Pico Technology partner, every instrument mentioned here is available with INR invoicing and application engineering support across Bengaluru, Chennai, Hyderabad, Delhi NCR, Mumbai, and Pune.

The measurement architecture for Indian EV battery characterization

Battery characterization is not a single measurement; it is several overlapping measurements running in parallel on the same device under test for hours or days at a time. The practical architecture Indian teams deploy maps to the Pico product family like this:

Measurement	What you need	Pico tool
Cell voltage (steady state + transient)	Differential input, ±10 V range, 14-16 bit resolution, few-microsecond response for load-step transients	PicoScope 4444 differential
Pack-level DC bus voltage	Up to 800 V differential, isolated measurement, less bandwidth needed	PicoScope 4444 with a high-voltage differential probe kit
Cell current (with a current clamp or shunt)	DC-to-100 kHz bandwidth on the clamp, high-resolution on the scope	PicoScope 4444 with an appropriate current clamp or PicoScope 4000A with a shunt
Multi-point temperature	Up to 8 thermocouples, 24-bit resolution, 100 ms sample period	PicoLog TC-08
Multi-point PT100 RTD temperature	Up to 4 high-accuracy PT100 inputs	PicoLog PT-104
Precision analog (cell temperature via NTC, shunt voltage, chamber sensors)	16+ single-ended channels, 20-24 bit resolution	PicoLog ADC-20 / ADC-24
Current logging via AC clamps	3 AC current clamps for 3-phase chargers or motor-side measurement	PicoLog CM3
Logic signals from BMS (CAN, SPI to gauges, etc.)	Mixed-signal analog + digital capture with protocol decode	PicoScope 2205A MSO, 5000D MSO, or 6000E MSO

In a fully instrumented bench, all of these run simultaneously and dump data into the same pyPicoSDK Python process. The critical property: every one of these instruments shares the same SDK family (PicoSDK / pyPicoSDK), so the Python test harness opens them all, configures them uniformly, and correlates results using a common timestamp.

Long-duration characterization: the pyPicoSDK streaming pattern

For hours-to-days battery capture runs, streaming mode is the only practical acquisition mode (see our pyPicoSDK streaming deep-dive for the full pattern). The abbreviated Python skeleton for a multi-instrument streaming capture:

from picosdk.ps4000a import ps4000a as ps      # PicoScope 4444 differential
from picosdk.plcm3 import plcm3 as cm3         # PicoLog CM3 current logger
from picosdk.usbtc08 import usbtc08 as tc08    # PicoLog TC-08 thermocouple

from datetime import datetime
import numpy as np, threading, time

# Open all three instruments
scope = open_picoscope_4444()
cm3_logger = open_picolog_cm3()
tc08_logger = open_picolog_tc08()

session_id = datetime.now().strftime("%Y%m%d_%H%M%S")
session_dir = f"/data/battery_runs/{session_id}"
os.makedirs(session_dir, exist_ok=True)

# Kick off streaming on each instrument with its own thread
stop_flag = threading.Event()

def scope_streamer():
    mmap = np.memmap(f"{session_dir}/scope.bin", dtype=np.int16, mode="w+", shape=(BIG_NUMBER,))
    # ... streaming acquisition loop, drains buffers into mmap ...

def tc08_streamer():
    mmap = np.memmap(f"{session_dir}/temperature.bin", dtype=np.float32, mode="w+", shape=(SMALLER_NUMBER,))
    # ... 8 thermocouple channels sampled every 100 ms ...

def cm3_streamer():
    mmap = np.memmap(f"{session_dir}/current.bin", dtype=np.float32, mode="w+", shape=(SMALLER_NUMBER,))
    # ... 3 AC clamps sampled every 100 ms ...

threads = [
    threading.Thread(target=scope_streamer, daemon=True),
    threading.Thread(target=tc08_streamer, daemon=True),
    threading.Thread(target=cm3_streamer, daemon=True),
]
for t in threads: t.start()

# Run the test profile, e.g., 48 hours of drive-cycle emulation
try:
    run_drive_cycle_profile(duration_hours=48)
finally:
    stop_flag.set()
    for t in threads: t.join()
    scope.close()
    tc08_logger.close()
    cm3_logger.close()

All three instruments write to memory-mapped files on SSD in a common session directory, tagged with a common session ID and timestamp. The drive-cycle emulation (running in the main thread via a power-cycler API or a CAN command sequence) happens concurrently with the measurement capture, and the result is a single /data/battery_runs/{session_id}/ directory containing every sample from every instrument for the full 48-hour run.

Post-processing with NumPy: extracting battery parameters from a raw capture

Once the raw capture is on disk, the analysis is a NumPy / SciPy problem. Typical Indian EV battery engineering parameters extracted from a completed run:

• State of charge (SoC) vs time: integrate the cell current and divide by nominal capacity.
• Coulombic efficiency per cycle: charge charge divided by discharge charge, aggregated per drive cycle.
• DC internal resistance (DCIR): voltage step divided by current step, measured across the transient moments when the load changes.
• AC impedance at selected frequencies: FFT the current and voltage stream during a small-signal excitation phase and read off the impedance at each tone.
• Temperature rise per cycle: correlate the thermocouple stream with the current stream to extract heat generation per ampere.
• Cell-to-cell spread: standard deviation across cells measured simultaneously, flagged when a specific cell diverges from the pack average.

NumPy and SciPy handle every one of these natively. The only reason to reach for Matlab at this point is if your team has legacy battery-analysis scripts in it, for new work, Python is the better investment.

Safety and bench design notes for Indian battery benches

Battery testing is not like any other electronic measurement, a mismanaged abuse test can destroy the bench, injure the engineer, or start a fire. Indian battery engineering teams building new benches should take the following seriously, regardless of which instrument vendor they standardize on:

• Isolation. Every differential probe (including the PicoScope 4444 inputs) must be rated for the voltage and energy of the cell or pack under test. A 48 V pack under short-circuit abuse testing can discharge through a probe ground path if the probe is not properly isolated, do not guess, read the datasheet.
• Enclosures. Abuse tests, nail penetration, crush, overcharge, over-discharge, must happen in a purpose-built enclosure rated for the thermal runaway energy release. Battery engineering groups that do not yet have this capacity in-house should partner with third-party test labs until they do.
• Emergency shutdown. Every long-duration automated run must have a hardware shutdown path that a human can trigger independent of the Python test harness. If your Python script hangs, you need to be able to stop the test.
• Data integrity for safety-relevant runs. For abuse tests where the data is being captured as legal or regulatory evidence, the capture chain must be verifiable, signed data files, independent second-instrument cross-checks, and synchronized video.

GSAS application engineers can help Indian battery engineering teams design the PicoScope and PicoLog portion of the bench, but the mechanical and safety engineering of the battery fixture itself is a specialist topic and should be consulted with a battery test engineering firm.

Hardware selection: three typical Indian EV battery bench configurations

Entry bench (single-cell characterization, one or two engineers, startup stage)

• 1× PicoScope 4444 differential oscilloscope
• 1× PicoLog TC-08 for 8 thermocouple channels
• 1× PicoLog CM3 for 3 AC current clamps (if testing charger-side)
• 1× appropriate current clamp for cell-level DC current
• High-voltage differential probe kit for pack-level work

Mid-range bench (module characterization, team of 3-5 engineers, Series A stage)

• Add: 1× PicoLog PT-104 for 4 high-accuracy RTD channels
• Add: 1× PicoLog ADC-24 for 16 precision analog channels (cell NTCs, shunt voltages, chamber sensors)
• Add: 1× PicoScope 5000D MSO for BMS CAN / SPI / protocol decode

Production-grade bench (pack-level characterization for PLI-qualified or OEM-approved production programs)

• All of the above, plus: 1× PicoScope 6000E for flagship multi-channel analog + MSO capture
• Multiple PicoLog TC-08 units in parallel for multi-point pack temperature mapping (48+ thermocouple channels)
• Multiple PicoLog ADC-24 units in parallel for 64+ precision analog channels

All configurations are supported by a common pyPicoSDK Python harness. Adding more instruments is a matter of adding them to the session’s open/close logic and giving them their own streaming thread.

Further reading

• pyPicoSDK streaming mode for continuous data capture, companion deep-dive on long-duration streaming
• Building an automated test framework with pyPicoSDK and pytest, Python test harness patterns
• PicoSDK and pyPicoSDK product page
• PicoScope 4444 differential input oscilloscope
• PicoLog TC-08 thermocouple data logger
• Pico Technology at GSAS
• Two-wheelers and EV industry solutions