From 5f24ac52c2196558312c081563c8ff1a1c8a1220 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 29 Nov 2024 12:08:44 +0000 Subject: [PATCH] adds numerical impedance predictions, figs, citation. updts nyquist grids, --- joss/figures/impedance_contour.png | Bin 0 -> 58787 bytes joss/figures/impedance_spectrum.png | Bin 0 -> 40679 bytes joss/figures/joss/contour_evolution.png | Bin 141039 -> 141039 bytes joss/figures/joss/contour_gradient.png | Bin 94135 -> 94135 bytes joss/figures/joss/contour_heuristic.png | Bin 113656 -> 113656 bytes joss/figures/joss/contour_total.png | Bin 351857 -> 351857 bytes joss/figures/joss/converge.png | Bin 53426 -> 53426 bytes joss/figures/joss/design.png | Bin 132350 -> 132350 bytes joss/figures/joss/impedance.png | Bin 0 -> 72856 bytes joss/figures/joss/optimisers_parameters.png | Bin 254747 -> 254747 bytes joss/figures/joss/sim-landscape.png | Bin 100042 -> 100108 bytes joss/figures/landscape.png | Bin 66470 -> 66595 bytes joss/image_combine.sh | 2 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figures/contour_gradient_2.png figures/contour_gradient_3.png figures/joss/contour_gradient.png convert +append figures/contour_evolution_0.png figures/contour_evolution_1.png figures/contour_evolution_2.png figures/contour_evolution_3.png figures/joss/contour_evolution.png diff --git a/joss/paper.bib b/joss/paper.bib index 677d2646..25cffb2b 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -330,3 +330,10 @@ @article{metropolis:1953 pages = {1087--1092}, file = {Submitted Version:/Users/engs2510/Zotero/storage/LKZVAGE4/Metropolis et al. - 1953 - Equation of State Calculations by Fast Computing M.pdf:application/pdf}, } + +@software{pybamm-eis, +author = {Dhoot, Rishit and Timms, Robert and Please, Colin}, +title = {PyBaMM EIS: Efficient Linear Algebra Methods to Determine Li-ion Battery Behaviour}, +url = {https://www.github.com/pybamm-team/pybamm-eis}, +version = {0.1.4} +} diff --git a/joss/paper.md b/joss/paper.md index 295aeca8..a88b09c6 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -18,6 +18,9 @@ authors: - name: Martin Robinson orcid: 0000-0002-1572-6782 affiliation: 3 + - name: Agriya Khetarpal + orcid: 0000-0002-1112-1786 + affiliation: 5 - name: Ferran Brosa Planella affiliation: "2, 4" orcid: 0000-0001-6363-2812 @@ -33,6 +36,8 @@ affiliations: index: 3 - name: Mathematics Institute, University of Warwick, Coventry, UK index: 4 + - name: New delhi? + index: 5 date: 28 November 2024 bibliography: paper.bib repository: https://github.com/pybop-team/PyBOP @@ -46,7 +51,7 @@ Similarly, `PyBOP` can be used for parameter design optimisation under user-defi # Statement of need -`PyBOP` is a Python package designed to provide a user-friendly, object-oriented interface for optimising battery models. `PyBOP` leverages the open-source `PyBaMM` package [@Sulzer:2021] to formulate and solve of these battery models. `PyBOP` is intended to serve a broad audience of students, engineers, and researchers in both academia and the battery industry by enabling the use of predictive battery models where previously this was not possible. `PyBOP` emphasises clear and informative diagnostics and workflows for users of varying expertise, by providing advanced optimisation and sampling algorithms. These methods are provided through interfaces to `PINTS` [@Clerx:2019], `SciPy` [@SciPy:2020], in addition to the PyBOP's own algorithms such as Adaptive Moment Estimation with Weight Decay (AdamW), and Cuckoo search. +`PyBOP` is a Python package designed to provide a user-friendly, object-oriented interface for optimising battery models. `PyBOP` leverages the open-source `PyBaMM` package [@Sulzer:2021] to formulate and solve of these battery models. `PyBOP` is intended to serve a broad audience of students, engineers, and researchers in both academia and the battery industry by enabling the use of predictive battery models where previously this was not possible. `PyBOP` emphasises clear and informative diagnostics and workflows for users of varying expertise, by providing advanced optimisation and sampling algorithms. These methods are provided through interfaces to `PINTS` [@Clerx:2019], `SciPy` [@SciPy:2020], in addition to the PyBOP's own algorithms such as Adaptive Moment Estimation with Weight Decay (AdamW), Gradient descent, and Cuckoo search. `PyBOP` supports the Battery Parameter eXchange (BPX) standard [@BPX:2023] for sharing battery parameter sets. As these parameter sets are costly to obtain due to: the equipment and time required for characterisation experiments, the need for battery domain knowledge, and the computational cost of parameter estimation. `PyBOP` reduces these costs by providing fast computational estimation with parameter set interoperability. @@ -144,9 +149,13 @@ where $\mathcal{L} : \mathbf{\theta} \mapsto [0,\infty)$ is a cost (or likelihoo Next, we demonstrate the fitting of synthetic data where the system parameters are known. In this example problem, we use `PyBaMM`'s implementation of the single particle model (SPM) with an added contact resistance submodel. We assume that the battery model is already parameterised except for two dynamic parameters, namely the lithium diffusivity of the negative electrode active material particle (denoted "negative particle diffusivity") and the contact resistance. We generate synthetic data from a one-hour discharge from 100% state of charge, to 0% (denoted as 1C rate), followed by 30 minutes of relaxation. This data is then corrupted with zero mean Gaussian noise of amplitude 2mV, shown by the dots in \autoref{fig:inference-time-landscape} (left). The initial states are assumed known, although such an assumption is not generally necessary. The underlying cost landscape explored by the optimiser is shown in \autoref{fig:inference-time-landscape} (right). -![The cost landscape for the parameterisation problem with a root mean squared error cost function. \label{fig:inference-time-landscape}](figures/joss/sim-landscape.png){ width=100% } +![The cost landscape for the time-series parameterisation problem with a root mean squared error cost function. \label{fig:inference-time-landscape}](figures/joss/sim-landscape.png){ width=100% } - As gradient information is available for this problem, the choice of distance-based cost function and optimiser is not constrained. Due to the different magnitudes of the two parameters, we apply the logarithmic parameter transformation offered by `PyBOP`. This transforms the optimisers search space of the optimiser to allow for a common step size between the parameters, which is generally is not required, but improves convergence in this problem. As a demonstration of the parameterisation capabilities of `PyBOP`, \autoref{fig:convergence-min-max} (left) shows the rate of convergence for each of the distance-minimising cost functions, while \autoref{fig:convergence-min-max} (right) shows analogous results for maximising a likelihood. The optimisation is performed with SciPy Minimize using the gradient-based L-BFGS-B method. +As mentioned above, `PyBOP` also provides inference and optimisation capabilities through numerical impedance spectroscopy. This is based on the methods presented in the `pybamm-eis` package and allows fast impedance computation by inversion of the sparse PyBaMM mass matrix after scaling and subtraction of the auto-differentiated Jacobian matrix. More information about this procedure is available in [@pybamm-eis]. The \autoref{fig:impedance-landscape} below shows the numerical impedance prediction available in `PyBOP` alongside the cost landscape constructed for the inference task. At the time of publication, gradient-based optimisation and sampling methods are not available when using an impedance workflow. + +![The cost landscape for the frequency domain impedance parameterisation problem with a root mean squared error cost function at 5% SOC. \label{fig:impedance-landscape}](figures/joss/impedance.png){ width=100% } + + Moving forward, we will continue with identification in the time-domain. As gradient information is available for this problem, the choice of distance-based cost function and optimiser is not constrained. Due to the different magnitudes of the two parameters, we apply the logarithmic parameter transformation offered by `PyBOP`. This transforms the optimisers search space of the optimiser to allow for a common step size between the parameters, which is generally is not required, but improves convergence in this problem. As a demonstration of the parameterisation capabilities of `PyBOP`, \autoref{fig:convergence-min-max} (left) shows the rate of convergence for each of the distance-minimising cost functions, while \autoref{fig:convergence-min-max} (right) shows analogous results for maximising a likelihood. The optimisation is performed with SciPy Minimize using the gradient-based L-BFGS-B method. ![Convergence in the likelihood functions obtained using various likelihood functions and the L-BFGS-B algorithm. \label{fig:convergence-min-max}](figures/joss/converge.png){ width=100% } diff --git a/joss/paper.pdf b/joss/paper.pdf index 8294081b99d54ed34e9ad2d48936b292c82827d8..db33545edfa4e48c1810bd97ddf271a87252f9ad 100644 GIT binary patch delta 198164 zcmZs?1yo#1ur3S(!QF#HaCevB?(XjH78qQD>)`J0?tuWof(8xl?hcRSoU`8j-~Csw z-D|$;>Z)oProQgYL54|arbX>790;`@g9?&^iH!@ik%WT*JmDXSF>Q0Yd;R(xu@$J5!u4TpGa;OQ2a8iq9ajN_(JhUC}3Z#EA z7y+wU>wdlkk|cG&oTQ>p7<9k*V&*ysG6H=!bOC$P8zlpRk!u@AYuzso@HukFFU#{Q zenVJ!mpNG49H~vl)2;CSCgSZ;=x1H?b#M>32Cz&Q;Cv0m7;m=@f`dD^JzSgfsS?t- zx-m|63>r4vi4YX@i2#&Rq{L9}0>vjC)3b$X_)YMXJ3GbZgT5 zpT}%sb2K%8NiF^k?A0Oj_jf5hyw9vfq0M>Y8VAC5YBFl&c;3sNYwJ%LkWIt;lPe3rT*?DG1G78`X;IK|;DgfRuf%fQ8lLSko z=>q-0PH#~xwjJEFkIaxlc)mFUvVuf;>W|#YfeTv;toE#XRXPLNyHOdTdTu6 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create_plot = {} create_plot["simulation"] = False -create_plot["landscape"] = False +create_plot["landscape"] = True create_plot["minimising"] = False create_plot["maximising"] = False create_plot["gradient"] = False create_plot["evolution"] = False -create_plot["heuristic"] = True +create_plot["heuristic"] = False create_plot["posteriors"] = False +create_plot["eis"] = True # Parameter set and model definition @@ -127,7 +128,7 @@ color="white", line_color="black", line_width=1, - size=14, + size=16, showscale=False, ), name="Initial values", @@ -143,7 +144,7 @@ color="black", line_color="white", line_width=1, - size=14, + size=16, showscale=False, ), name="True values", @@ -607,3 +608,101 @@ def format_axis(ax): plt.tight_layout() plt.savefig("joss/figures/joss/posteriors.png") plt.show() + + +if create_plot["eis"]: + + def noise(sigma, values): + # Generate real part noise + real_noise = np.random.normal(0, sigma, values) + + # Generate imaginary part noise + imag_noise = np.random.normal(0, sigma, values) + + # Combine them into a complex noise + return real_noise + 1j * imag_noise + + var = pybamm.standard_spatial_vars + var_pts = {var.x_n: 10, var.x_s: 10, var.x_p: 10, var.r_n: 30, var.r_p: 30} + + # Construct model + model = pybop.lithium_ion.SPMe( + parameter_set=parameter_set, + eis=True, + options={"surface form": "differential", "contact resistance": "true"}, + var_pts=var_pts, + ) + + initial_state = {"Initial SoC": 0.05} + n_frequency = 35 + sigma0 = 5e-4 + f_eval = np.logspace(-3.75, 4, n_frequency) + + # Create synthetic data for parameter inference + sim = model.simulateEIS( + inputs={ + "Negative particle diffusivity [m2.s-1]": parameter_set[ + "Negative particle diffusivity [m2.s-1]" + ], + "Contact resistance [Ohm]": 0.01, + }, + f_eval=f_eval, + initial_state=initial_state, + ) + + # Form dataset + dataset = pybop.Dataset( + { + "Frequency [Hz]": f_eval, + "Current function [A]": np.zeros(len(f_eval)), + "Impedance": sim["Impedance"] + noise(sigma0, len(sim["Impedance"])), + } + ) + + signal = ["Impedance"] + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) + cost = pybop.RootMeanSquaredError(problem) + + optim = pybop.CMAES(cost, max_iterations=75, min_iterations=75, sigma0=0.25) + results = optim.run() + + parameter_fig = pybop.plot.nyquist(problem, results.x, title="") + parameter_fig[0].write_image("joss/figures/impedance_spectrum.png") + + landscape_fig = pybop.plot.contour(cost, steps=30, title="") + initial_value = parameters.initial_value() + true_value = parameters.true_value() + landscape_fig.add_trace( + go.Scatter( + x=[initial_value[0]], + y=[initial_value[1]], + mode="markers", + marker_symbol="x", + marker=dict( + color="white", + line_color="black", + line_width=1, + size=16, + showscale=False, + ), + name="Initial values", + ) + ) + landscape_fig.add_trace( + go.Scatter( + x=[true_value[0]], + y=[true_value[1]], + mode="markers", + marker_symbol="cross", + marker=dict( + color="black", + line_color="white", + line_width=1, + size=16, + showscale=False, + ), + name="True values", + ) + ) + landscape_fig.show() + landscape_fig.write_image("joss/figures/impedance_contour.png") diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py index 08ab7129..f1253ccc 100644 --- a/pybop/plot/nyquist.py +++ b/pybop/plot/nyquist.py @@ -120,13 +120,13 @@ def nyquist(problem, problem_inputs: Inputs = None, show=True, **layout_kwargs): showgrid=True, gridwidth=1, gridcolor="lightgray", - minor=dict(showgrid=True, gridwidth=0.5, gridcolor="lightgray"), + zerolinecolor="lightgray", ) fig.update_yaxes( showgrid=True, gridwidth=1, gridcolor="lightgray", - minor=dict(showgrid=True, gridwidth=0.5, gridcolor="lightgray"), + zerolinecolor="lightgray", ) # Overwrite with user-kwargs