Abstract:
Electrochemical Impedance Spectroscopy (EIS) is emerging as a critical diagnostic and
characterization technique in the automotive industry. By measuring a cell’s frequency response to
small AC perturbations, EIS provides detailed insights into internal resistance, charge transfer
kinetics, ion diffusion, and degradation processes within batteries and capacitors. For electric and
hybrid vehicles, these parameters are vital to ensuring accurate State of Health (SoH) and State of
Charge (SoC) estimation, enhancing both performance and safety, alongside supporting
initiatives/norms like ‘GB 38031’ and ‘NHTSA safety initiative’ for early detection and prevention of
thermal runaway. To achieve this function, affordable AFE with low noise performance are needed,
which is critical while performing EIS measurements at low frequencies.
Texas Instruments (TI), a global leader in analog and embedded processing, is uniquely positioned
to overcome these challenges. With a broad portfolio of low-noise analog front ends, high side gate
drivers, combined with powerful microcontrollers, TI can deliver compact, scalable, and cost efficient EIS solutions suitable for in-vehicle deployment. TI’s highly sophisticated analog front ends
integrate rich features like EIS signal generation, synchronization of the devices, compression of the
measured data etc which can be used for a pack level or a module level EIS measurements. A high
level block diagram of the implementation can be seen in the block diagram below.
Methods of EIS excitation:
Generally, two methods can be used for EIS excitation i.e. Pack level or module level. Below is a
representation on both the methods.
Both methods of excitation bring in their pros and cons. While pack level excitation offers higher
flexibility to use the signal from different sources like DCDC, Inverter etc, it comes with challenges
such as synchronizing the voltage and current measurements.
The module level excitation is simpler because the voltage and current measurements occur using
the same reference clock. However, the method poses some challenges on thermal dissipation,
higher energy drain during EIS measurements and potential higher cost.
Understanding the Nyquist plot:
Interpreting the Nyquist plot is key in EIS i.e. looking for changes in the Nyquist plot versus a specific
monitored condition (ex- SoC, temp, aging, etc). As an example, we can look at the Nyquist plot for
a 18650 cell as a function of SoC for a given temperature. Based on the impedance value calculated
and the known temperature, the SoC can be interpreted.
Enabling EIS with such an approach supports predictive maintenance, adaptive control, and
improved lifetime modeling of lithium-ion and emerging chemistries such as solid-state, LFP, and
sodium-ion cells. As electrification expands across automotive segments, EIS integration offers a
new frontier for intelligent energy management.