Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise**

Physico-chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black-box optimizer suited for modular continuum battery models. Standard approaches compare the experimental data in its raw entirety to the model simulations. By dividing the data into physics-based features, our data-driven approach uses orders of magnitude less simulations. For validation, we process full-cell GITT measurements to characterize the diffusivities of both electrodes non-destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases

EP-BOLFI: Bayesian Optimization for Automated Parameterization of 1+1D Battery Cell Models

Our goal is the automated parameterizing of battery cell models for model-based evaluation of experimental databases. The manual standard approach requires cell disassembly and individual measurements on the various cell components. Measurement techniques include, e.g., galvanostatic intermittent titration technique (GITT) or impedance spectroscopy. They are complicated by their long run-time and considerably noise sensitivity. Bayesian algorithms can directly incorporate the inherent uncertainties of model and measurement. The standard approach for parameterization is Markov-Chain Monte Carlo (MCMC). But with 1+1D battery cell models, their simulation time is too large for the tens of thousands of required samples. In this contribution, we extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black-box optimizer suitable for modular 1+1D battery cell models. The algorithm can exploit a partitioning of the experimental data into features that is motivated by physico-chemcial understanding. However, the algorithm does not rely on approximative formulas and can be applied to a broad range of techniques. This approach reduces the number of required simulations for four parameters from 100,000 to about 500. Furthermore, we can estimate parameter uncertainties and inter-dependencies. As an example, we process GITT full-cell measurements of lithium-ion batteries to non-destructively characterize the diffusivities of both electrodes at the same time

Automated Battery Model Selection with Bayesian Quadrature and Bayesian Optimization

In the process of constructing physics-based battery models, there are usually several candidate submodels for any mechanism of interest, as seen in the modular battery model software PyBaMM [1]. Parameterizing such varied model sets is a challenging task, since developing a specialized routine for each combination of submodels is unfeasible. Aitio et al. have shown that Metropolis-Hastings can di- rectly fit a model to measured voltage [2]. Kuhn et al. have shown that Metropolis-Hastings scales poorly when the models or measurements get more involved [3]. Hence, they propose EP-BOLFI as an alter- native, which can parameterize a wide variety of models reliably, and do it faster as well. But, a well parameterized model does not imply that the data supports that model. Adachi, Kuhn et al. have shown that the closeness of the fitted model to the data is not a reliable measure [4]. Hence, EP-BOLFI does not help in selecting a model. Instead, they propose a Bayesian Quadrature approach for model selection, BASQ [5]. The caveat is that BASQ needs to perform a successful parameterization to then give good measures for model quality. And the result of BASQ depends on the randomly chosen model evaluations it is initialized with. In contrast, if the optimal parameter set is within the prior bounds, Metropolis-Hastings and EP-BOLFI have a much higher chance to eventually reach that optimum. In this work, we investigate if the stability of EP-BOLFI can supplement BASQ. We showcase this on the example used in Ref. 4, the selection of the number of RC-pairs in a R-RC-RC-etc. equivalent circuit model. We find that preconditioning the prior probability distribution with EP-BOLFI before giving it to BASQ can improve the parameterization, and hence, the model selection success rate

Parameterisation of Physics-Based Battery Models From Few Noisy Measurements

A wealth of measurement techniques are available for determining the transport or thermodynamic properties of batteries. Some examples are the Galvanostatic Intermittent Titration Technique, nuclear magnetic resonance imaging or impedance spectroscopy, which are excellent at retrieving a subset of battery model parameters. They achieve this at the cost of accuracy and compatibility since each employs a different approximation in order to obtain analytic expressions. So it remains a challenge to obtain a complete and consistent parameter set that is useful for running simulations that can accurately predict future battery behaviour. Exacerbating this challenge is the long runtime and/or high cost of any battery measurement, which means that in practice only a few measurements of varying type with considerable noise are available and that the parameters might change between measurements due to battery ageing. Due to the complexity of the widely used Doyle-Fuller�Newman model and its simplifications, their parameters are not directly observable in normal battery operation. Thus, some measurements involve the destruction of the battery, which make the parallel parameterisation of "identical" batteries with slightly different manufacturing defects necessary. The goal is to enable automated material screening with a flexible selection of various measurements. The issues described above necessitate that an inverse parameter identification algorithm for this task is aware of the uncertainties in the parameters and the measurements and can quantify the uncertainties of the estimated parameters. These uncertainties are most certainly intractable, so we decided on a Bayesian approach where the likelihood is substituted by a simulator, realised with Expectation Propagation and Bayesian Optimisation. We will discuss the results of their application

Automating The Selection Of Battery Models With Bayesian Quadrature And Bayesian Optimization

The development of modern physics-based battery models increasingly specializes in individual processes in a cell. Selecting from the competing explanations for itemized phenomena grows more complicated. The scope of this challenge is visible in the wide variety of submodels offered in the modular battery model software PyBaMM [1]. The ability to parameterize any combination of submodels for a given measurement is crucial, as differing specialized routines are unfeasible for the large variety of submodels. This challenge gets addressed by Aitio et al. [2] and Kuhn et al. [3], which utilize Markov-Chain Monte Carlo methods to directly fit a model to the measured voltage. However, different submodels may fit with similar accuracy to the same data. Often this results from overparameterization, other times this happens because the difference gets lost in measurement noise. In either case, just the quality of the fit does not reliably tell whether the data supports the model, as shown by Adachi, Kuhn et al. [4]. To remedy that, they propose the Bayesian Quadrature algorithm BASQ [5] to calculate how well the data support a model, and verify its reliability on impedance data. BASQ considers not only a fit of the model to data but also the model-data distance for a wide range of model parameter values [5]. With this information, BASQ can discern two models for their ability to explain a particular dataset. Still, BASQ needs to find a good fit of the model to data as a basis for reliable model selection. However, the ability of BASQ to find said good fit directly depends on the initialization samples taken from the Prior. A Prior is, simply put, the weighted search area in the model parameter space, given in the form of a probability distribution. In this work, we find that the dependency of BASQ on the Prior can be alleviated by preconditioning the Prior with a parameterization algorithm. We choose EP-BOLFI from Kuhn et al. [3] as the parameterization algorithm, as it scales better with the model complexity than Metropolis-Hastings from Aitio et al. [2] does. EP-BOLFI has the additional benefit of itemizing its result into the given features one defines on the data. With featurization, we find that EP-BOLFI more quickly discerns the correlations, i.e., interdependencies, between the model parameters, long before it narrows down to a specific model fit. BASQ [5] profits off these correlations more than from a narrower search area, allowing us to preserve its model selection capability across a wide range of model parameters. We showcase the synergy between EP-BOLFI and BASQ on the example used in Adachi, Kuhn et al. [4], the determination of the length of the RC-chain in a R-RC-RC-etc. equivalent circuit model. The authors acknowledge support by the Helmholtz Association through grant no KW-BASF-6 (Initiative and Networking Fund as part of the funding measure "ZeDaBase-Batteriezelldatenbank")

Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature

A wide variety of battery models are available, and it is not always obvious which model `best' describes a dataset. This paper presents a Bayesian model selection approach using Bayesian quadrature. The model evidence is adopted as the selection metric, choosing the simplest model that describes the data, in the spirit of Occam's razor. However, estimating this requires integral computations over parameter space, which is usually prohibitively expensive. Bayesian quadrature offers sample-efficient integration via model-based inference that minimises the number of battery model evaluations. The posterior distribution of model parameters can also be inferred as a byproduct without further computation. Here, the simplest lithium-ion battery models, equivalent circuit models, were used to analyse the sensitivity of the selection criterion to given different datasets and model configurations. We show that popular model selection criteria, such as root-mean-square error and Bayesian information criterion, can fail to select a parsimonious model in the case of a multimodal posterior. The model evidence can spot the optimal model in such cases, simultaneously providing the variance of the evidence inference itself as an indication of confidence. We also show that Bayesian quadrature can compute the evidence faster than popular Monte Carlo based solvers.Comment: 11 pages, 2 figures, accepted at IFAC202

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise

Abstract Physico-chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black-box optimizer suited for modular continuum battery models. Standard approaches compare the experimental data in its raw entirety to the model simulations. By dividing the data into physics-based features, our data-driven approach uses orders of magnitude less simulations. For validation, we process full-cell GITT measurements to characterize the diffusivities of both electrodes non-destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases

Distinct mechanisms eliminate mother and daughter centrioles in meiosis of starfish oocytes

Centriole elimination is an essential process that occurs in female meiosis of metazoa to reset centriole number in the zygote at fertilization. How centrioles are eliminated remains poorly understood. Here we visualize the entire elimination process live in starfish oocytes. Using specific fluorescent markers, we demonstrate that the two older, mother centrioles are selectively removed from the oocyte by extrusion into polar bodies. We show that this requires specific positioning of the second meiotic spindle, achieved by dynein-driven transport, and anchorage of the mother centriole to the plasma membrane via mother-specific appendages. In contrast, the single daughter centriole remaining in the egg is eliminated before the first embryonic cleavage. We demonstrate that these distinct elimination mechanisms are necessary because if mother centrioles are artificially retained, they cannot be inactivated, resulting in multipolar zygotic spindles. Thus, our findings reveal a dual mechanism to eliminate centrioles: mothers are physically removed, whereas daughters are eliminated in the cytoplasm, preparing the egg for fertilization.European Molecular Biology Laboratory (EMBL)- EMBL International PhD Program; Laura and Arthur Colwin Endowed Summer Research Fellowship; Deutsche Forschungsgemeinschaft grant: (MU1423/4-1)

Genome of the Asian Longhorned Beetle (\u3cem\u3eAnoplophora glabripennis\u3c/em\u3e), a Globally Significant Invasive Species, Reveals Key Functional and Evolutionary Innovations at the Beetle-Plant Interface

Background: Relatively little is known about the genomic basis and evolution of wood-feeding in beetles. We undertook genome sequencing and annotation, gene expression assays, studies of plant cell wall degrading enzymes, and other functional and comparative studies of the Asian longhorned beetle, Anoplophora glabripennis, a globally significant invasive species capable of inflicting severe feeding damage on many important tree species. Complementary studies of genes encoding enzymes involved in digestion of woody plant tissues or detoxification of plant allelochemicals were undertaken with the genomes of 14 additional insects, including the newly sequenced emerald ash borer and bull-headed dung beetle. Results: The Asian longhorned beetle genome encodes a uniquely diverse arsenal of enzymes that can degrade the main polysaccharide networks in plant cell walls, detoxify plant allelochemicals, and otherwise facilitate feeding on woody plants. It has the metabolic plasticity needed to feed on diverse plant species, contributing to its highly invasive nature. Large expansions of chemosensory genes involved in the reception of pheromones and plant kairomones are consistent with the complexity of chemical cues it uses to find host plants and mates. Conclusions: Amplification and functional divergence of genes associated with specialized feeding on plants, including genes originally obtained via horizontal gene transfer from fungi and bacteria, contributed to the addition, expansion, and enhancement of the metabolic repertoire of the Asian longhorned beetle, certain other phytophagous beetles, and to a lesser degree, other phytophagous insects. Our results thus begin to establish a genomic basis for the evolutionary success of beetles on plants