Combined experimental and numerical evaluation of the differences between convective and conductive thermal control on the performance of a lithium ion cell

Testing of lithium ion batteries is necessary in order to understand their performance, to parameterise and furthermore validate models to predict their behaviour. Tests of this nature are normally conducted in thermal/climate chambers which use forced air convection to distribute heat. However, as they control air temperature, and cannot easily adapt to the changing rate of heat generated within a cell, it is very difficult to maintain constant cell temperatures. This paper describes a novel conductive thermal management system which maintains cell temperature reliably whilst also minimising thermal gradients. We show the thermal gradient effect towards cell performance is pronounced below operating temperature of 25 °C at 2-C discharge under forced air convection. The predicted internal cell temperature can be up to 4 °C hotter than the surface temperature at 5 °C ambient condition and eventually causes layers to be discharge at different current rates. The new conductive method reduces external temperature deviations of the cell to within 1.5 °C, providing much more reliable data for parameterising a thermally discretised model. This method demonstrates the errors in estimating physiochemical paramet ers; notably diffusion coefficients, can be up to four times smaller as compared to parameterisation based on convective test data

An easy-to-parameterise physics-informed battery model and its application towards lithium-ion battery cell design, diagnosis, and degradation

Accurate diagnosis of lithium ion battery state-of-health (SOH) is of significant value for many applications, to improve performance, extend life and increase safety. However, in-situ or in-operando diagnosis of SOH often requires robust models. There are many models available however these often require expensive-to-measure ex-situ parameters and/or contain unmeasurable parameters that were fitted/assumed. In this work, we have developed a new empirically parameterised physics-informed equivalent circuit model. Its modular construction and low-cost parametrisation requirements allow end users to parameterise cells quickly and easily. The model is accurate to 19.6 mV for dynamic loads without any global fitting/optimisation, only that of the individual elements. The consequences of various degradation mechanisms are simulated, and the impact of a degraded cell on pack performance is explored, validated by comparison with experiment. Results show that an aged cell in a parallel pack does not have a noticeable effect on the available capacity of other cells in the pack. The model shows that cells perform better when electrodes are more porous towards the separator and have a uniform particle size distribution, validated by comparison with published data. The model is provided with this publication for readers to use

Differential thermal voltammetry for tracking of degradation in lithium-ion batteries

Monitoring of lithium-ion batteries is of critical importance in electric vehicle applications in order to manage the operational condition of the cells. Measurements on a vehicle often involve current, voltage and temperature which enable in-situ diagnostic techniques. This paper presents a novel diagnostic technique, termed differential thermal voltammetry, which is capable of monitoring the state of the battery using voltage and temperature measurements in galvanostatic operating modes. This tracks battery degradation through phase transitions, and the resulting entropic heat, occurring in the electrodes. Experiments to monitor battery degradation using the new technique are compared with a pseudo-2D cell model. Results show that the differential thermal voltammetry technique provides information comparable to that of slow rate cyclic voltammetry at shorter timescale and with load conditions easier to replicate in a vehicle