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

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

Seeing is believing: in situ/operando optical microscopy for probing electrochemical energy systems

This review discusses a range of in situ/operando techniques based on optical microscopy reported in literatures for studying electrochemical energy systems. Compared to other techniques (scanning probe microscopy, electron microscopy, X-ray microscopy), optical microscopy offers many advantages including the simplicity of the instrument and operation, cost effectiveness, and nondestructive nature. In the past few decades, significant advances in the field of optical microscopy have been made, enabling new opportunities of more elaborate studies on electrochemical energy systems. Herein, different methodologies are compared, with the emphasis on experimental setup designs and findings, to illustrate their aptness

Module design and fault diagnosis in electric vehicle batteries

Systems integration issues, such as electrical and thermal design and management of full battery packs - often containing hundreds of cells - have been rarely explored in the academic literature. In this paper we discuss the design and construction of a 9 kWh battery pack for a motorsports application. The pack contained 504 lithium cells arranged into 2 sidepods, each containing 3 modules, with each module in a 12P7S configuration. This paper focuses particularly on testing the full battery pack and diagnosing subsequent problems related to cells being connected in parallel. We demonstrate how a full vehicle test can be used to identify malfunctioning strings of cells for further investigation. After individual cell testing it was concluded that a single high inter-cell contact resistance was causing currents to flow unevenly within the pack, leading to cells being unequally worked. This is supported by a Matlab/Simulink model of one battery module, including contact resistances. Over time the unequal current flowing through cells can lead to significant differences in cells' state of charge and open circuit voltages, large currents flowing between cells even when the load is disconnected, cells discharging and aging more quickly than others, and jeopardise capacity and lifetime of the pack

From Atoms to Cells:Multiscale Modeling of LiNi_xMn_yCo_zO₂Cathodes for Li-Ion Batteries

First-generation cathodes for commercial lithium-ion batteries are based on layered transition-metal oxides. Research on ternary compounds, such as LiCoO2, evolved into mixed-metal systems, notably Li(Ni,Mn,Co)O2 (NMCs), which allows significant tuning of the physical properties. Despite their widespread application in commercial devices, the fundamental understanding of NMCs is incomplete. Here, we review the latest insights from multiscale modeling, bridging between the redox phenomena that occur at an atomistic level to the transport of ions and electrons across an operating device. We discuss changes in the electronic and vibrational structures through the NMC compositional space and how these link to continuum models of electrochemical charge-discharge cycling. Finally, we outline the remaining challenges for predictive models of high-performance batteries, including capturing the relevant device bottlenecks and chemical degradation processes, such as oxygen evolution. </p

The Regeneration Games: Commodities, Gifts and the Economics of London 2012

This paper considers contradictions between two concurrent and tacit conceptions of the Olympic ‘legacy’, setting out one conception that understands the games and their legacies as gifts alongside and as counterpoint to the prevailing discourse, which conceives Olympic assets as commodities. The paper critically examines press and governmental discussion of legacy, in order to locate these in the context of a wider perspective contrasting ‘gift’ and ‘commodity’ Olympics – setting anthropological conceptions of gift-based sociality as a necessary supplement to contractual and dis-embedded socioeconomic organizational assumptions underpinning the commodity Olympics. Costbenefit planning is central to modern city building and mega-event delivery. The paper considers the insufficiency of this approach as the exclusive paradigm within which to frame and manage a dynamic socio-economic and cultural legacy arising from the 2012 games