Jointly learning consistent causal abstractions over multiple interventional distributions

An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution. Learning abstractions which guarantee consistency with respect to interventional distributions would allow one to jointly reason about evidence across multiple levels of granularity while respecting the underlying cause-effect relationships. In this paper, we introduce a first framework for causal abstraction learning between SCMs based on the formalization of abstraction recently proposed by Rischel (2020). Based on that, we propose a differentiable programming solution that jointly solves a number of combinatorial sub-problems, and we study its performance and benefits against independent and sequential approaches on synthetic settings and on a challenging real-world problem related to electric vehicle battery manufacturing

Evaluation of cyclic battery ageing for railway vehicle application

Mobile transportation systems rely heavily on hydrocarbon based internal combustions engines (ICE) as the prime mover of vehicles. For rail applications electrification of the route provides an opportunity to improve efficiency and eliminate local emissions at point of use. However, route electrification is not always cost effective for secondary routes which see lower passenger volumes and less frequent trains; there is therefore an increasing interest in railway vehicles being equipped with energy storage based propulsion systems. Most of the railway vehicles that use an electrical traction energy storage system are at a prototype stage. Therefore, long-term real life data for the behaviour of traction batteries is not available up to now. The study presented in this paper describes ageing characterisation of two battery chemistries (Nickel-Manganese-Cobalt (NMC) and Lithium-Iron-Phosphate (LFP)) for representative rail duty cycles. Test bench trials are performed to represent roughly 1500 h of battery operation. A Battery Only and a Hybrid Energy Storage System case are considered

Impact of solid-electrolyte interphase reformation on capacity loss in silicon-based lithium-ion batteries

High-density silicon composite anodes show large volume changes upon charging/discharging triggering the reformation of the solid electrolyte interface (SEI), an interface initially formed at the silicon surface. The question remains how the reformation process and accompanied material evolution, in particular for industrial up-scalable cells, impacts cell performance. Here, we develop a correlated workflow incorporating X-ray microscopy, field-emission scanning electron microscopy tomography, elemental imaging and deep learning-based microstructure quantification suitable to witness the structural and chemical progression of the silicon and SEI reformation upon cycling. The nanometer-sized SEI layer evolves into a micron-sized silicon electrolyte composite structure at prolonged cycles. Experimental-informed electrochemical modelling endorses an underutilisation of the active material due to the silicon electrolyte composite growth affecting the capacity. A chemo-mechanical model is used to analyse the stability of the SEI/silicon reaction front and to investigate the effects of material properties on the stability that can affect the capacity loss

Impact of solid-electrolyte interphase reformation on capacity loss in silicon-based lithium-ion batteries

High-density silicon composite anodes show large volume changes upon charging/discharging triggering the reformation of the solid electrolyte interface (SEI), an interface initially formed at the silicon surface. The question remains how the reformation process and accompanied material evolution, in particular for industrial up-scalable cells, impacts cell performance. Here, we develop a correlated workflow incorporating X-ray microscopy, field-emission scanning electron microscopy tomography, elemental imaging and deep learning-based microstructure quantification suitable to witness the structural and chemical progression of the silicon and SEI reformation upon cycling. The nanometer-sized SEI layer evolves into a micron-sized silicon electrolyte composite structure at prolonged cycles. Experimental-informed electrochemical modelling endorses an underutilisation of the active material due to the silicon electrolyte composite growth affecting the capacity. A chemo-mechanical model is used to analyse the stability of the SEI/silicon reaction front and to investigate the effects of material properties on the stability that can affect the capacity loss

Systematic derivation of a Single Particle Model with Electrolyte and Side Reactions (SPMe+SR) for degradation of lithium-ion batteries

Battery degradation, which is the reduction of performance over time, is one of the main roadblocks to the wide deployment of lithium-ion batteries. Physics-based models, such as those based on the Doyle-Fuller-Newman (DFN) model, are invaluable tools to understand and predict such phenomena. However, these models are often too complex for practical applications, so reduced models are needed. In this article we introduce the Single Particle Model with electrolyte and Side Reactions (SPMe+SR), a reduced model with electrochemical degradation which has been formally derived from the DFN model with Side Reactions (DFN+SR) using asymptotic methods. The SPMe+SR has been validated against the DFN+SR for three scenarios (SEI growth, lithium plating, and both effects combined) showing similar accuracy at a much lower computational cost. The implications of the results are twofold: the SPMe+SR is simple and accurate enough to be used in most real practical applications, and the reduction framework used is robust so it can be extended to account for further degradation effects.Comment: 31 pages (5 figures) + 8 pages of supplementary material (9 figures

Transfer learning LSTM model for battery useful capacity fade prediction

Lithiumion (Li-ion) batteries have become increasingly useful within the automotive industry and modern life applications due to high energy and power densities. However, these batteries suffer capacity loss due to different ageing mechanisms in various applications. Despite several existing models, lack of accurate predictability of capacity degradation limits the advancement of Li-ion batteries. The present work focuses on prediction of battery useful capacity degradation using long-short term memory (LSTM) transfer learning neural network model. At first, a base model was developed and trained using all the (100 % ) degradation data available at 0°C and 10°C environmental temperatures. Thereafter, the training of the base model was fixed, and additional hidden layers were added on top of the base model to further fine tune it with only the initial 30% degradation data available at 25°C environmental temperature. The remaining (70 % ) data of the 25°C case was tested for model prediction. To decide the number of fixed hidden layers to be transferred from base model to transfer model and the number of additional hidden layers on top, an optimization for minimum cross validation error was performed. It was found that the resulting model was able to forecast the remaining battery degradation with 96% accuracy. The model prediction was also compared with LSTM deep learning architecture without using transfer learning. The LSTM with transfer learning model was found to be 17% higher in prediction accuracy than that without utilizing transfer learning