Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.</p

Assessing and comparing German and UK transition policies for electric mobility

AbstractThis paper presents a novel policy assessment approach for sustainable transitions using insights from the multilevel perspective (MLP). An analysis of current German and UK policies for sustainable transport is conducted to illustrate its application. For both cases a potential transition pathway, that can satisfy environmental protection and industrial competitiveness goals, is derived from archetypal transition pathways. These are then put in relation to current policies, discussing whether these measures support these pathways. In the UK case, where emission reduction goals and industrial development are pursued together, current policies of promoting the diffusion of electric vehicles as well as industrial niches are supporting the emergence of a reconfiguration pathway. Replacing foreign suppliers, the local automotive industry shall become a significant part of the future regime. In contrast to that, Germany focuses on a careful transformation and conservation of its automotive industry where none of the current actors is left behind

Novel Methods for Measuring the Thermal Diffusivity and the Thermal Conductivity of a Lithium-Ion Battery

Thermal conductivity is a fundamental parameter in every battery pack model. It allows for the calculation of internal temperature gradients which affect cell safety and cell degradation. The accuracy of the measurement for thermal conductivity is directly proportional to the accuracy of any thermal calculation. Currently the battery industry uses archaic methods for measuring this property which have errors up to 50 %. This includes the constituent material approach, the Searle’s bar method, laser/Xeon flash and the transient plane source method. In this paper we detail three novel methods for measuring both the thermal conductivity and the thermal diffusivity to within 5.6 %. These have been specifically designed for bodies like lithium-ion batteries which are encased in a thermally conductive material. The novelty in these methods comes from maintaining a symmetrical thermal boundary condition about the middle of the cell. By using symmetric boundary conditions, the thermal pathway around the cell casing can be significantly reduced, leading to improved measurement accuracy. These novel methods represent the future for thermal characterisation of lithium-ion batteries. Continuing to use flawed measurement methods will only diminish the performance of battery packs and slow the rate of decarbonisation in the transport sector

PTFE mapping in gas diffusion media for PEMFCs using fluorescence microscopy

Differentiating between the various polytetrafluoroethylene based structures inside polymer electrolyte membrane fuel cells with a degree of certainty is necessary to optimize manufacturing processes and to investigate possible degradation mechanisms. We have developed a novel method using fluorescence microscopy for distinguishing the origin and location of PTFE and/or Nafion® in Membrane Electrode assemblies and the gas diffusion media from different sources and stages of processing. Fluorescent material was successfully diffused into the PTFE based structures in the GDM by addition to the ‘ink’ precursor for both the microporous layer and the catalyst layer; this made it possible to map separately both layers in a way that has not been reported before. It was found that hot pressing of membrane coated structures resulted in physical dispersion of those layers away from the membrane into the GDM itself. This fluorescence technique should be of interest to membrane electrode assembly manufacturers and fuel cell developers and could be used to track the degradation of different PTFE structures independently in the future

Diffusion-aware voltage source: An equivalent circuit network to resolve lithium concentration gradients in active particles

Traditional equivalent circuit models (ECMs) have difficulties in estimating battery internal states due to the lack of relevant physics, such as the lithium diffusion in active particles. Here we configure a circuit network to describe the lithium diffusion and define it as a new high-level circuit element called diffusion-aware voltage source. The circuit representation is proven equivalent to the discretized diffusion equation. The new voltage source gives the electrode potential as a function of the surface concentration and thus automatically incorporates the diffusion overpotential. We show that an ECM with the proposed diffusion-aware voltage sources (called "shell ECM") can reproduce the single particle model simulation results, making it a trustworthy easy-to-implement substitute. Furthermore, the simplest shell ECM consisting of a single diffusion-aware voltage source and a resistor is validated against experimental constant-current discharges at various rates. The diffusion-aware voltage source can be used to measure diffusivity by fitting the diffusion resistance against experimental data. The viability of the shell ECM for onboard usage is confirmed by implementation into a battery management system of WAE Technologies. By tracking the internal concentration states, the shell ECM demonstrates robustness to dynamic applied-current profiles.Comment: 35 pages, 14 figure

PTFE mapping in gas diffusion media for PEMFCs using fluorescence microscopy

Differentiating between the various polytetrafluoroethylene based structures inside polymer electrolyte membrane fuel cells with a degree of certainty is necessary to optimize manufacturing processes and to investigate possible degradation mechanisms. We have developed a novel method using fluorescence microscopy for distinguishing the origin and location of PTFE and/or Nafion® in Membrane Electrode assemblies and the gas diffusion media from different sources and stages of processing. Fluorescent material was successfully diffused into the PTFE based structures in the GDM by addition to the ‘ink’ precursor for both the microporous layer and the catalyst layer; this made it possible to map separately both layers in a way that has not been reported before. It was found that hot pressing of membrane coated structures resulted in physical dispersion of those layers away from the membrane into the GDM itself. This fluorescence technique should be of interest to membrane electrode assembly manufacturers and fuel cell developers and could be used to track the degradation of different PTFE structures independently in the future

Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a ‘behavioural’ interpretation of the ECN model; as Li-S exhibits a ‘steep’ open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV