BatteryML: An Open-source Platform for Machine Learning on Battery Degradation
Authors: Han Zhang
Xiaofan Gui
Shun Zheng
Ziheng Lu
Yuqi Li
Jiang Bian
Venue: ICLR 2024 spotlight (2024)
Keywords: BatteryML
Battery life prediction
Machine learning
Open-source platform
Unified standards
Collaborative research
Primary area: infrastructure, software libraries, hardware, etc.
TL;DR: BatteryML is an open-source platform that simplifies machine learning applications for battery life prediction, addressing challenges faced by researchers and fostering collaboration across disciplines.

Abstract: Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions. However, this intersection of electrochemical science and machine learning poses complex challenges. Machine learning experts often grapple with the intricacies of battery science, while battery researchers face hurdles in adapting intricate models tailored to specific datasets. Beyond this, a cohesive standard for battery degradation modeling, inclusive of data formats and evaluative benchmarks, is conspicuously absent.  Recognizing these impediments, we present BatteryML—a one-step, all-encompass, and  open-source platform designed to unify data preprocessing, feature extraction, and the implementation of both traditional and state-of-the-art models. This streamlined approach promises to enhance the practicality and efficiency of research applications. BatteryML seeks to fill this void, fostering an environment where experts from diverse specializations can collaboratively contribute, thus elevating the collective understanding and advancement of battery research.

Published as a conference paper at ICLR 2024
BATTERYML: AN OPEN-SOURCE PLATFORM FOR MA- CHINE LEARNING ON BATTERY DEGRADATION
Han Zhang': Xiaofan Gui Shun Z Ziheng Lu², Yuqi Li Jiang Bian? 'Inttitute for Interdisciplinary Information Sciences, Tsinghua University 2 Microsoft Research
Department of Materials Science and Engineering, Stanford University han nrn-zhann770 70mails ts singhuu, edu. cn, (xiaofangui, shun zheng, zihenglu, jiang. bian wmmoorrot.t.coommm yuqil reglli@stanford.com
ABSTRACT
Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as potent tool to drive forward insights and solutions. However, this intersection of electrochemical science and machine learning poses complex challenges. Machine learning experts often grapple with the intricacies of battery science, while battery researchers face hurdles in adapting intricate models tailored to specific datasets. Beyond this, a cohesive standard for battery degrada- tion modeling, inclusive of data formats and evaluative benchmarks, is conspic- uously absent. Recognizing these impediments, We present BatteryMi one- step, all-encompass, and open-source platform that integrates data preprocessing. feature extraction, and the implementation of both conventional and state-of-the- art models. This streamlined approach promises to enhance the practicality and efficiency of research applications. BatteryML seeks to fill this void, fostering collaborative platform where experts from diverse specializations can contribute, thereby accelerating collective progress in battery research.
INTRODUCTION
Lithium-ion batteries, characterized by their high energy density and prolonged cycle life, have rev- olutionized energy storage across sectors like electric vehicles, consumer electronics, and renewable energy solutions. However, the ubiquitous adoption of these batteries comes with inherent chal- lenges surrounding their capacity degradation and performance stability (Edge et al.] (02T)) Con- tinuous cycling tends to diminish their charging and discharging capacities, posing dire implications for real-world applications. For instance, 'range anxiety" becomes prevalent among electric vehicle owners, and reliability concerns arise for energy storage systems. Beyond the user experience, rapid degradation introduces broader issues, such escalated maintenance costs, heightened resource age, environmental strain, and potential economic inefficiencies. As such, decoding and forecasting battery performance degradation has ascended a pivotal topic in industrial artificial intelligence.
Peeling back the layers of lithium-ion batteries reveals their intricate, non-linear electrochemical dy- namies (Hu al.) 2020). Degradation, observed as diminishing performance with increased charge- discharge iterations, branches mainly into losses in lithium-ion inventory (e.g., solid electrolyte interphase film formation and electrolyte dccomposition) and active material losses (c.g.. graphite delamination and binder decomposition) (Pop et al.) 2007; Dubarry et al.) 2012; Sarasketa-Zabala al. 2013). Moreover, the internal resistance and excessive electrolyte losses further contribute to the battery's 's declining health. Such losses in electrolytes, in particular, can precipitate stark capacity plunge towards a battery's 's lifecycle end (Edge al., (021).
Confronting this degradation complexity, reliably predicting a battery's s remaining useful life (RUL), state of health (SOH), and state of charge (SOC) becomes a herculean endeavor (Lipu et al. 2018).
Han Zhang and Yuqi Li worked on this project during their internship at Microsoft Research.
Project repository: https: //g/ /tggt/ttuuu..ooo/ wwt.u//.cccmmmccccccccccm.c.c http://///mmmoooooo/aaattteeeeee

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The significance of RUL, especially in battery management, second-hand vehicle evaluation, and more, has spurred extensive research. For instance, integrating techniques like electrochemical impedance spectroscopy with machine learning has been demonstrated to hold promise (Zhang et al. 2020; Sevveson et al. 2019 Attia et al. [021] 2020). Similarly, SOH and SOC estimation has seen advances through capacity- based, Coulomb counting, impedance methods, and model-based
techniques, with machine learning bringing innovative dimensions (Wang et al. 2011 Plett 2004
Barsoukov al. 2005 Doyle et al. 1993; He al. 2011
Yet, a glaring gap persists in the domain. While individual studies have made strides in understand- ing battery degradation, their focal points often remain narrowly defined by specific use scenarios or Haarge-iischarge strategies. Existing research predominantly uses particular battery types and operation paradigms, making findings less generalizable. The disparities across datasets in terms of battery forms, chemistries, operational profiles, or environmental conditions render universal approach elusive. Consequently, the absence of consistent standard in battery research underscores the need for comprehensive and unified methodology.
Challenges In the realm of battery research and modeling, diverse challenges often impede the streamlined application and integration of machine learning techniques.
Data heterogeneity. Battery data exhibits considerable heterogeneity in terms of both data format and data patterns. The output format of various battery testing systems differs with respect the recorded fields, time granularity, file types, etc. Sometimes, severe concep- tual confusion may arise due to differences in terminology conventions. For example, the capacity of the battery may be reported through the areal specific capacity, the total ca- pacity, or even normalized capacity. Subsequent data processing further adds to the data heterogeneity. Even for the same data format, different cathode material compositions such as LiCoO, (LCO), LiFePO, (LFP), and LiNiMnCoOp (NMC) lead to diverse degradation patterns.
Domain knowledge. On the one hand, machine learning professionals struggle to craft effective feature spaces due to the high-dimensional and heterogeneous characteristics of battery data. This intricacy presents a significant challenge in applying advanced machine learning techniques to battery performance modeling.
Model development. On the other hand, battery experts, while proficient in understanding degradation mechanisms, often face challenges in building robust machine learning model due to the nuanced data cleaning, feature engineering, and model fine-tuning processes. Existing tools and models that arecrafted for specific data structures might not seamlessly adapt to other scenarios.
Contributions BatteryML addresses the above challenges in a holistic manner. As an inclusive open-source platform, BatteryML simplifies every stage of battery modeling, from data preprocess- ing and feature construction to model training and inference.
Unified data representation. Recognizing the challenges of diverse battery data, Bat- teryML. introduces a standardized data representation method. It provides comprehensive processing tools to collate and harmonize virtually public battery datasets. With this consistent data representation, uniform evaluation criterion for assessing battery degra- dation becomes feasible, promoting robust comparisons and insights across diverse battery contexts.
Comprehensive open-source platform. BatteryMI. covers essential hattery research tasks like State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL). offers a holistic suite of tools encompassing data preprocessing, feature and target extrac- tion, model training, prediction, and visualization. This integrative design allows experts from varied fields to contribute, nurturing ongoing innovation in battery research.
State-of-the-an model integration. BatteryML seamlessly integrates wide array of models, spanning both traditional and cutting-edge techniques. The platform's 's modular design ensures clear demarcation between models and data processing stages, facilitating effortless integration and refinement by machine learning experts. With a unified data repre- sentation, researchers can leverage multiple datasets simultaneously, unlocking techniques
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like transfer learning. This fluidity not only accelerates research but also sets the stage for the integration of more sophisticated models in the coming times.
2 RELATED WORK
Battery modeling tasks. Lithium-ion battery lifetime modeling has been the subject of numer- studies. A vast number of researchers have proposed both physical and semi-empirical models to capture various mechanisms, including the growth of the solid- eleccrolly interphase, lithium
plating, active material loss, and impedance increase (Das al.. 2019 Palacín 2018 Woosung et al. 2020). Predictive state estimation for remaining useful life in battery management systems often hinges upon these mechanistic and semi-empirical models. Specialized diagnostic measure- ments, such as coulombic efficiency and impedance spectroscopy, further assist in lifetime estima- tion (Burns et al., 2013] Chen al. 2001 Tröltzsch et al.) 2006 Love et al., 2014). Despite their success, these chemistry or Seshhannsm--peeifii models often struggle to accurately characterize the battery degradation on the cell level due to the intricate interactions between multiple degradation modes and the thermal and mechanical variances within cell (Waldmann et al. 2014 2015
Bach
et al. 2016, Jain al. 2013 Aykol et al. 2016).
While semi-empirical approaches require in-depth battery and chemistry domain knowledge to model various intricate degradation mechanisms, the rapid evolution of machine learning provides fully data-driven methodology, using linearmodels, support vector machines, and neural networks
for accurate battery degradation modeling (Severson et al. 2019 Segler et al. 2018 Ng et al. 2020
Lu et al., 2023: Chemali et al. 2018 Zhang et al. 2018 Li et al. [020 Attia al. [202]] | al,
2022
Ren et al.
2018
Khumprom & Yodo 2019; Sahinoglu et al. [0188]
2018:ppménee Jiménez Bermejo et al,
2018
Wu et al.
2018 Zhang al.] 2019). Such data-driven approaches allow seamlessly inte-
gration of different degradation factors such as electrical signals, temperature and electrochemical
impedance spectroscopy data, for flexible battery modeling (Han et al. 2019 Li et al. [2019] Meng & Li 2019 Liu et al. [020 Hossain Lipu et al. [021]] Ayob et al. 2022 Rauf et al. (2022). On one hand, the flourishing of these data-driven approaches continues to propel advancements in battery data acquisition and the performance of machine learning models. On the other hand, due to the absence of unified modeling framework, the problem settings and data representations vary across different models, making it challenging to achieve stable replication and comparison. This accen- tuates the urgent need for a consolidated platform that standardizes battery degradation research, further propelling the field's progression.
Battery Early Prediction Framewok. The Battery Evaluation and Early Prediction Software Pack- age (BEEP) offers an open-source, Python-centric framework designed for the efficient handling and processing of extensive battery cycling data streams (Herring et al. 2020). Notable features of BEEP encompass tie-system-orreenee organization of raw cycling data, validation procedures for data thenticity, linear model learning for anomaly detection and cycle life carly-prediction. While BEEP positions itself tool designed to assist battery experts with coding skills more efficiently con- duct battery life predictions to validate design ideas, Battery ML. aims to bridge the gap between the battery and machine learning communities. Through its modular design, BatteryML decouples the knowledge dependencies of the two communities, enabling battery experts to utilize the most advanced machine learning models, while also allowing machine learning professionals to more ef- fectively optimize models for battery data. BatteryML's data representation naturally supports the transformation of battery data into multidimensional tensors, thereby facilitating seamless integra- tion for battery experts into existing deep learning frameworks. Moreover, Battery ML empowers machine learning professionals to explore advanced learning paradigms, such as transfer learning, on variety of battery data.
3 BATTERYML PLATFORM
Pipeline Overview As depicted in Figure I the BatteryML pipeline comprises organized se- quence of functional modules, guiding users through the process of model creation and application. The initial step involves converting all incoming data into a consistent format. Following this, configuration file is crafted to specify data locations, partitioning strategies, feature and label gen- eration methods, as well as the associated model parameters. An elaborate sample of these settings
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Training
Fvaluat:
Lineer models
TrafnTersolliteer
Tree
DutaProprocessee
Smoothing
Normalization
Neural networks
Battery testers
FestureFxtrastor
Labe Extrraatrrtttor
Model architecture
Cycle Data
M
M
M
Supervised leamnin
Meta Information
Nominal Eapacily
Cathode Skewvi
Unsuperv sed leamning
Min Vollae
0 Becartyse Material
Max Canoot
D Form Fackor
Transfor leaming
Public datasets
BatteryData
Learning paradigm
Data Sources
BatteryMML Pipeline
ML Tools
Figure 1: An overview of BatteryMI.
presented Code Once configured, the comprehensive pipeline process allows end-to-ent battery degradation modeling. Components integrated in this process encompass the train-test data split module, label extractor module, feature extractor module, data preprocessing module, and the model module. Detailed examples of the pipeline's 's core functions, including pipeline initialization, model training, and result extraction, is available in Appendix E.1
Train-test split module. This module determines the strategy of the learning process. BatteryMI. allows users to randomly allocate battery cells into training and test subsets with respect given split proportion. Alternatively, BatteryML also provide standard data splits for popular datasets such as MATR (Severson et al. (019) and HUST (Ma et al., 2022 to enable reproducible and comparable experiments. The module also offers high flexibility for custom data partitioning.
Label extractor module. This module automatically annotates the prediction target for major battery modeling tasks such as RUL and SOH prediction.
Feature extractor module. This module implements the popular features designed by domain experts such the cycle-difference features for battery life prediction (Severson jet al. 2019). This module also enables extraction of raw electric signals and conversion to tensors for training neural networks. This module highly extensible support custom feature design.
Data preprocessing module. This module supports flexible transformation and refinement independently for both features and labels, including data normalization and augmentation techniques to boost model performance.
Model module. This module specifies the model structure and learning parameters. Bat- teryML currently supports a broad spectrum of machine learning models including lin- ear models, tree-based models, and neural network- based models. Users effortlessly manipulate the model behavior without the concerning the intricacies the specialized domain knowledge.
3.1 BATTERYDATA: A UNIFIED BATTERY DATA REPRESENTATION
The multifaceted landscape of battery data stems from the diverse data collection apparatuses and preprocessing methodologies employed by manufacturers. This diversity results in varied data for- mats, terminological conventions, data fields, and signal recording strategies. Recognizing the chal- lenge this poses, we introduced a unified data representation Batter teytt that encompasses both meta information and charge/discharge cycles.
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1.00
NeT 30/14
NAT 10020
2.90
(D1228
NAT 12:0
N071 bi220
NATI 32:31
22:33
NATTLb2220
12:30
N072 birin
NOTA, 22-1
krT--hire7
NATA, 22-53
L52-44
22:0
1000
2000
Sydles
Figure 2: MATRI degradation curve showcasing the train and test dataset discharge capacity
Meta information specifications. Battery attributes such the anode, cathode, and elec- trolyte materials are recorded as meta information fields. Parameters such as nominal ca- pacity, depth of charge/discharge, and the operating thresholds for voltage and current also summarized as cell-level attributes.
Charge/Discharge cycles. BatteryDat: organizes the time series records as a list of cy- cles, detailing charge/discharge protocols and capacity, voltage, current, time, temperature, and internal resistance records.
A comprehensive outline of this BatteryData is presented in Appendix
Our commitment to this unified data representation not only facilitates efficient data management from diverse sources but also supports insightful comparisons battery performance and highlights degradation nuances battery ages. It creates conducive environment for deploying machine learning and sophisticated data analysis strategies, propelling advancements in battery data opera- tion and maintenance. For users, Batteryaa allows flexible visualization for battery data. As evident in Figure 2 one can glean insights into capacity degradation trends by aggregating all the cycles of the battery cell. Additionally, users can delve into cycle-level signals such as the trajectory of voltage curves of successive cycles or the evolution of Coulombic efficiency for further degrada- tion analysis, as highlighted in Figure Such graphical interpretations provide users with clearer and more intuitive understanding of battery performance and degradation pathways, which in turn benefits the feature and model design.
BatteryML seamlessly supports converting output formats from various battery cycler systems into BatteryDat a out of the box. Moreover, we offer suite automated processing tools to trans- form existing datasets into the BatteryData format. These dual capabilities ensure BatteryML's excellent compatibility with a wide range of battery data sources.
3.2 FEATURE ENGINEERING
BatteryML boasts an array of degradation features, enabling users to flexibly tailor the data based on distinct experimental needs. These features bifurcate into categories: within-cycle and between- cycle features.
Within-cycle features. This category encompasses characteristics observed within individual charge/discharge cycles. Examples include
QdLinear, which is derived by linear interpolation of the capacity-voltage curve in dis- charge cycles (Attia et al. (2021).
Coulombic efficiency. which is the fraction of charge and discharge capacity within single cycle, an important indicator of how efficiently a battery can release its stored energy.
Internal resistance, which can be calculated by the voltage and current signals to measure the opposition to the flow of electric current within the battery.
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Between-cycle features. These features captures the degradation patterns of any battery cell higher level, usually across multiple cycles. Examples include
Variance of the difference of QdLinear curves, an intuitive yet effective feature that indi- cates battery degradation speed (Severson et al. 2019).
Capacity decay dynamics, the slope of capacity decay curve fitted in early cycles.
Average charging time, which reflects the irreversible structural changes within the battery, such as lithium plating and the growth the SEI layer.
Temperature dynamics, which indicates the intensity of the electrochemical reactions oc- curring within the battery.
Minimal internal resistance, reflecting the upper bound of battery health state.
Both the within- and between-cycle features represented as tensors that are compatible with modern machine learning frameworks. This allows a flexible manipulation of the feature space and natural combination with deep learning models. We provide detailed introduction of BatteryML feature module in Appendix EE33 Through these feature extraction methodologies, Battery ML em- powers users freely design and combine features, significantly simplifying the process of repro- ducing existing work and testing new features.
3.3 AUTOMATIC LABEL ANNOTATION
BatteryML supports automatic label annotation for supervised battery degradation modeling. For different degradation modeling tasks, BatteryML calculates the label as tensors according to the definition. Here we briefly introduce the most important degradation modeling tasks.
As batteries undergo continuous cycles, their capacity inevitably declines due to aging, resulting in slight variances in performance with each use. From the moment of manufacture until battery is fully aged and retired, direct and crucial question is to measure the loss of the battery's maximum available capacity in any given cycle relative to its capacity when new, namely, the cycle's State of Health (SOH). Specifically, if we denote the battery's 's nominal capacity as Cnom and the discharge capacity in the current cycle Crull, the SOH is defined as the percentage of these two quantities
Crull
SOH
x 100%.
Cnom
Here the Cfull in a strict definition of SOH refers to the capacity measured under the same discharge protocol the nominal capacity. Note that in practice, this requires additional cycles under strained temperature and current conditions and is usually infeasible due to significant human labor costs.
This requires accurately estimating the ratio of the capacity already discharged in the current cycle to the original total capacity. that is. the State of Charge (SOC). Essentially, when the depth of discharge is 100%, and the Battery Management System (BMS) has recorded the discharged capacity, SOC is equivalent to SOH. Estimating SOC becomes more challenging when the depth of discharge less than 100% (for example, when discharging begins again after a brief charge). Specifically, denoting the remaining capacity of the battery as Ceurr, the definition of SOC is
Ceurr 100%.
SOC
Crun
SOH requires prediction for each cycle, whereas SOC demands prediction at every moment dur- ing charging and discharging phases. Both tasks requires online prediction, with SOC estimation imposing higher demands on the real-time performance of the model. Another offline task, early battery life prediction, involves conducting a limited number of charge-discharge tests to predict the full lifespan of the battery-typicalll until its capacity falls below 80% of the nominal capacity (Li jtt al., 2019) before significant degradation occurs. This task can greatly shorten the cycling test duration for batteries, playing crucial role in downstream battery optimization tasks such as the selection of battery materials, optimization of charging strategics, and control of operating temper- atures.

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BatteryML automates the task of label annotation through sequential traversal of each eycle's charge/discharge stages, significantly reducing the reliance of model design on battery domain knowledge and allowing machine learning experts to focus developing superior models.
3.4 MODEL DEVELOPMENT
Following (Attia et al. 2021), BatteryML incorporates multiple off-the-shelf baselines for battery lifetime predictions, including linear models, tree-based models and neural networks. Domain- Enhanced methods like the 'Variance', 'Discharge', and 'Full' models from the (Severson et al., 2019 are implemented using handcrafted features and linear models. BatteryML also includes statistical models such as Ridge regression (Hoerl & Kennard 2000). Principal Component Re- gression(PCR) (Tipping & Bishop 1999), Partial Least Squares Regression(PLSSR) (Geladi & Kowalski 1986). Gaussian process (Williams & Rasmussen 2006). XGBoost (Chen & Guestrin 2016). Random forest (Breiman 2001) models. For nigh-perffrmance needs, we offer neural network models like Multi-Layer Perceptron (MLP) (Haykin 1994), Convolutional Neural Net- works (CNN) (Krizhevsky et al. 2012), Long Short-Term Memory networks (LSTM) (Hochreiter & Schmidhuberr 1997). Additionally,
introduce Transformer (Vaswani al. (017)) ground- breaking architecture in language and vision domains (Brown et al. 2020 Dosovitskiy et al. 2021), new neural baseline. These implementations utilize scikit-learn (Pedregosa al. 201 for all statistical models, barring XGBoost, and PyTorch (Paszke et al. 2019) for neural networks. We re- train each model with 10 random seeds and report averaged results eliminate the effeet of random initializaiton.
To cater the diversity of experimental contexts, users have the liberty to tweak and customize these models as their needs. A deeper dive into model intricacies can be found in Appendix [
BatteryMM's versatility lies in offering a spectrum of models, ensuring users can cherry-pick and tailor the most fitting analytical approach aligned with their research objectives. Anchored on BatteryData BatteryMI, paves the way for integrating cutting-edge machine learning paradigms like transfer learning and multi-task learning into battery modeling Moreover, era where large-scale model architectures are blossoming, BatteryML lays a robust foundation to harness the power of these expansive models for future battery research.
sail through an
4 EVALUATION
In this section, we provide an in-depth evaluation of model performance across various datasets to inform model selection. Through a comprehensive analysis, our intent is offer a holistic per- spective on the efficacy of each model, empowering researchers and practitioners to make informed decisions tailored to their specific goals.
4.1
DATA
We based our evaluation several publicly accessible battery datasets: CALCE (ing et al.
2013
He et al. 2011a). HNEI (Devic et al. 2018). HUST (Ma et al. 2022). MATR (Severson et al. 2019
Hong al. 2020), RWTH (Li et al.) 2021), SNL (Preger al. 2020), and UT PUR Jublez-Robles
et al. 2020 2021 These datasets encompass LFP, LCO, NMC, NCA and NMC_LCC battery types. Further details are outlined Table D Certain datasets were excluded due their unsuitability for tasks such as RUL estimation. The datasets differ in terms of materials, capacities, voltages, and RUL ranges. For RUL tasks, we also created combined datasets from the public sources assess training efficacy when various battery data are combined. Notably, CRUH combines CALCE, E, RWTH, PUR, and HNEI datasets; CRUSH merges CALCE, RWTH, UL PUR, SNL, and HNEI datasets; and MIX incorporates all datasets used in our study. For more detailed information on the data, please refer to the Appendix A
4.2 BATTERY DEGRADATION MODELING
BatteryML currently supports battery degradation tasks, including RUL prediction, SOH estimation and SOC estimation. Here we report the main benchmark results, and leave the detailed analysis and further ablation studies in the appendix.
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Table Specifications of data sources.
Data
Electrode
Nominal capacity
Voltage range (V)
RUL dist.
SOC
SOH
Cell
source
chemistry
dist. (%)
dist. (%)
count
CALCE
LCO/graphite
1.1
2.7-4.2
5664106
77+17
48430
13
MATR
LFP/graphite
1.1
2.0-3.6
823+368
93+7
36+36
180
HUST
LFP/graphite
1.1
2.0-3.6
1899:389
100+10
43+28
HNEI
NMC. LCO/graphite
2.8
3.0-4.3
248
64+17
49+28
14
RWTH
NMC/carbon
1.11
3.5-3.9
658=64
60+24
46+22
48
SNL
.CA.NMC.LFPPgraphhic
1.1
2.0-3.6
1256#1321
86#7
45+27
61
PUR
NCA/graphite
3.4
2.7-4.2
209+50
8946
41+33
Table 2: Benchmark results for remaining useful life prediction. The comparison methods are split into four types, including 1) dummy regressor, trivial baseline that uses the mean of training label as predictions; 2) linear models with features designed by domain experts; 3) traditional statistical models with QdLinear feature; 4) deep models with QdLinear feature. For models sensitive to initialization, we present the error mean across ten seeds and attach the standard deviation subscript.
Models
MAIR1
MATR2
HUST
SNL
CLO
CRUH
CRUSH
MIX
Dummy regressor
398
510
419
466
331
239
576
573
"Variance" model
136
211
398
360
179
506
521
"Discharge" model
329
149
322
267
143
76
> 1000
> >1000
"Full" model
167
1000
335
433
138
93
> 1000
331
Ridge regression
116
184
>1000
242
169
65
> 1000
372
PCR
90
187
435
200
197
68
560
376
PLSR
104
181
431
242
176
60
535
383
Gaussian process
154
> >100
204
> 1000
573
XGBoost
334
799
395
547
215
119
330
205
Random forest
168,
2337
368;
5322s
1922
811
416s
197.
MI.P
149;
27527
459,
370g1
146g
103,
565g
451
CNN
102a
228104
4657s
924267
>> 1000
17492.
54511
272
LSTM
119
219ts
443t9
53940
222
105
519
268p
Transformer
135
3642s
39111
42423
18714
81s
55031
27
Remaining useful life prediction. In the task of RUL prediction, BatteryML. models predict the number of cycles until battery's SOH falls below certain threshold, e.g. 80%, in comparison with the nominal capacity. The performance metrics of various methods are presented in Table 2
Linear models using handcrafted features, such the "Discharge" and "Full" model, offer relatively accurate predictions for LFP battery datasets. However, their performance diminishes on the CRUSH and MIX dataset, which features diverse aging conditions, due the limited feature set and model capacity.
Traditional statistical models, capable of discerning non-linear patterns from low-level features such as Qe(Va) curves, employ specific modeling mechanisms such as the decision tree ensemble ap- proach in Random Forests and XGBoost. Despite robust performance on CRUH, CRUSH, and MIX, their efficacy decreases on datasets such as MATR2 and SNI, where the number of training samples are limited. This finding indicates that these statistical models require a larger volume of training data to effecttvvly Iearn and represent meaningful insights in RUL task.
Neural network models, through automatic representation learning on low-level features, offer ad- vancements, but face significant performance variations due to different random parameter initial- izations. For instance, our observations CNN reveal its ability to make accurate predictions with many random seeds (as exemplified by the results MATR1, see Table(C.I) However, certain seeds can lead to a surprising increase in error, causing significant regression error variations. This un- derscores both the potential benefits and challenges of applying neural networks to RUL predictio
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tasks. The observed disparities in performance across various network architectures also highlight the absence universally optimal architecture for battery modeling.
From the feature space perspective, linear models, utilizing handcrafted features, have demonstrated satisfactory performance on datasets such as MATR2, HUST, and CLO, which solely consist one battery type, LiFePO4 (LFP). This finding validates the efficacy of domain knowledge. However, these models appear to be less successful when applied to datasets that encompass a wider range of battery types and aging conditions, such as CRUSH and MIX. In these instances, models that are directly fitted on the Qd (Vd) curve have proven to be more effective than those using manually crafted features. This highlights deficiency in domain-specinn feature design and underscores the necessity for more versatile, generalizable features, emphasizing the potential advantages of automated representation learning.
Please refer to the appendix C.I for more detailed comparison analysis. We also provided an in- depth exploration of the impact of features and model hyperparameters in appendix D
State of Health estimation. SOH estimation task requires model to predict the ratio of the cur- rent discharge capacity in reference performance test (RPT) the nominal capacity. Since the RPT results are not always available in the public datasets, we turn to predict the ratio of observed dis- charge capacity to nominal capacity in this study We directly employ cells from the data sources in Table [ for training and evaluation. Table showcases the comparison results.
The effectiveness of methods in SOH prediction varies across datasets. Linear models are generally effective but face challenges with the MATR cells due to variable charging strategies. frse- models show consistent, robust performance across datasets, establishing a strong baseline in SOH estimation. Deep learning models, however, haven't 't consistently outperformed traditional methods, indicating potential areas for improvement. We provided detailed analysis in appendix C.2
State of Charge estimation. Similar to SOH estimation, the exact SOC value is unattainable in practice by definition. Given the fact that RPT results are also not available in most public datasets, in this study we predict the SOC derived from the observed discharge capacity. Table C.2 demonstrates the benchmark results.
LightGBM consistently surpassed other methodologies in most tasks, thereby establishing tree- based models as the current state-of-the-art in SOC prediction. Moreover, linear models continue to excel over deep learning models, highlighting the need for further research unlock the full potential of neural networks in battery modeling. For detailed insights, please see the appendix C.3
CONCLUSION
At the core of BatteryML is commitment to fostering collaboration and bridging divides, As comprehensive open-source platform, effectively bridges the knowledge chasm between battery researchers and AI experts, streamlining data preprocessing, feature extraction, and model applica- tion, both traditional and advanced. This synthesis not only elevates battery modeling endeavors but also catalyzes two-way exchange, i.e., empowering battery scientists to harness Al-driven tools for research and equipping Al experts with insights to tackle intricacies specific the battery sector.
Furthermore, BatteryML serves as anchor in standardizing practices within the battery research realm. By pioneering a unified data format and integrating advanced models into the baseline, Bat- teryML promotes consistency and rigour, thereby catalyzing a harmonious evolution of the industry. is our aspiration that through BatteryML, the pace research in battery degradation is accelerated, fostering seamless integration across industry, academia, and research spheres.
In the future, we envision BatteryML will be developed to facilitate the translation of lab data into tangible real-world applications. Such advancements promise bolster battery research, propelling us closer to a sustainable future. Moreover, there lies an opportunity to render the platform even more frienffi By integrating features like one-click battery life prediction and rolling out an intuitive user interface, BatteryML can resonate with, and cater to, an even wider audience.
BltteryML. can effectively construct more accurate label for training when RPT results available.

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Hui Zou and Trevor Hastie. Regularization and Variable Selection Via the Elastic Net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2):30--320, 03 2005. ISSN 1369- 7412. doi: 10. 11 1/j.
URL bttpp:://cc..rog///
8. 14667-80 9868 2005. 00503.x
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piot the curve Voltage over time for each cyde
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Figure 3: Voltage curves of battery in MATR1, Each curve corresponds to a cycle.
A BENCHMARK DATASET CONSTRUCTION
Table summarizes the data sources utilized in this paper. Herein, we provide detailed introduction to these data sources and elaborate the construction process of our benchmark datasets.
CALCE data source (Xing et al., 2013; He et al. 2011a) The CALCE data source is derived from the CS2 and CX2 batteries with full lifecycle data, released by the center for advanced life cycle engineering. All the batteries in this data source prismatic with Lithium Cobalt Oxide (LCC) cathode. Additionally, Energy Dispersive Spectroscopy (EDS) results revealed trace ele- ments of Manganese in these batteries. Their nominal capacity are 1100 mA. All cells in the dataset were subjected to the same charging profile, which followed a standard constant current/constant voltage protocol. This involved charging at constant current rate of 0.5C until the voltage reached 4.2V, followed by maintaining 4.2V until the charging current decreased to below 0.05A. The cutoff voltage for these batteries was set at 2.7V.
The MATR dataset comprises two groups of commercial 18650 Lithium Iron Phosphate (LiFePO,, LFP) batteries, provided respec- tively by (Severson et al. 2019) and (Attia et al. 2020). It consists of 180 cells and represents the largest publicly available dataset containing complete charge- -iitge-d cycles of batteries. These batteries were cycled in horizontal cylindrical fixtures on 48-channel Arbin LBT potentiostat, situ- ated within forced convection temperature chamber maintained at C The cells have nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V. They were subjected to the same discharge strat- egy but different fast charging strategies, until their discharge capacity decreased 80% of the rated capacity. In our benchmark study, total of 180 cells are distributed across three distinct datasets: MATRÍ, MATR2, and CLO. This categorization is result of the cells being measured in distinct batches. The MATR1 and MATR2 datasets are provided by (Severson et al. 2019). The third dataset, CLO, provided by (Attia et al. 2020p.
MATR data source (Severson al. 2019; Attia et al. 2020)
HUST data source (Ma et al. 2022) The HUST data source consists of 77 Lithium Iron Phos- phate (LFP) batteries, identical in model to those used in the MATR dataset. These cells were subjected to an identical charging protocol but employed different multi-stage discharge protocols, all conducted at constant temperature of C
HNEI data source (Devie et al. 2018) This data source comprises commercial 18650 cells that feature a graphite negative electrode and a blended positive electrode composed of Nickel Man- ganese Cobalt (NMC) and Lithium Cobalt Oxide (LCO). These cells were cycled rate of 1.5C to 100% Depth of Discharge (DOD) for over 1000 cycles, conducted at room temperature.
SNL data source (Preger et al. 2020) This data source includes commercial 18650 cells made of Nickel Cobalt Aluminum (NCA), Nickel Manganese Cobalt (NMC), and Lithium Iron Phosphate (LFP) materials. These cells are cycled to 80% capacity. with the cycling process still ongoing. The study focuses evaluating the impact of temperature, Depth of Discharge (DOD), and discharge
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current the long-term degradation of these commercial cells. Each cycling round involves capacity check, a set number of cycles under specific conditions designated for each cell, followed by another capacity check at the end. The capacity check entails three charge/discharge cycles ranging from 0 to 100% State of Charge (SOC) at a 0.5C rate.
PUR data source (Juarez-Robles et al. 2020; 2021) The data source consists of commercial pouch cells featuring graphite negative electrode and a Nickel Cobalt Aluminum (NCA) positive electrode. These cells were cycled at a rate of IC within a voltage range of 2.7-4.2V, corresponding to 0-100% State of Charge (SOC), room temperature. This cycling conducted until various levels of capacity fade, ranging from 10-20%, were reached. Additionally, the modules were cycled at rate of C/2 within voltage range of 13.7-21.0V, also corresponding to 0-100% SOC, room temperature, until they reached 20% capacity fade.
RWTH data source (Li et al., 2021) This data source comprises cycling records of 48 lithium- ion battery cells. All 48 cells, being of the same type, underwent aging under identical profiles and conditions. Before initiating the aging process, the initial performance of the cells was deter- mined through comprehensive Parameter Tests (RPT) were performed to assess the current performance of the cells. The specific cells used are Sanyo/Panasonie URI 18650E cylindrical cells. These are commercially available and are produced in large quantities using an established fabrication process. The design of these cells includes a carbon anode and Nickel Manganese Cobalt (NMC) cathode material.
Segin-OO-Life (BOL) test. Regularly scheduled Aging Reference
Benchmark dataset synthethis. We follow the original train-test split in (Severson et al., 2019) to obtain two datasets MATR1 and MATR2 from MATR data source. Notably, the charging policies of the train and test cells in MATR1 are identically distributed, while in MATR2 the charging policies of the test cells are unseen. We also create CLO dataset that randomly split the cells from the entire MATR data source. We also follow the original train-test split in the HUST data source obtain the HUST dataset for evaluation. These four datasets employ the same battery model.
We combine the cells in CALCE, RWTH, PULPUR, and HNEI data sources into dataset CRUH as the cell count of these four data sources are limited. For the cells in SNL data source, we first construct a dataset SNL by random train-test split. Then, to fully utilize the batteries with cycle life smaller than 100, we combine them with CALCE, RWTH, UL PUR, and HNEI data sources to obtain CRUSH. In CRUSH, the models are required to predict the 90% state of health point with only the first 20 eycles.
Finally, we combine the cells in all data sources to obtain the MIX dataset, which to the best of our knowledge, is the largest battery degradation dataset with complete cycling records.
B DETAILED MODEL DESCRIPTION
To facilitate the replication of existing studies, BatteryML offers range of model implementations, encompassing both traditional statistical models and neural networks. Below, we provide a summary of the currently available models. important to note that our repertoire of models is continually expanding.
Dummy Regression. This model uses the mean of the training samples as predictions, usually acting as a trivial baseline indicate the error upper bound. The DummyRegres from the scikit-leazn (Pedregosa et al. 2011) toolkit is adopted in BatteryML.
Linear Regression. A statistical method for modeling the relationship between a dependent variable and one or more independent variables using linear equation. BatteryML, utilizes the ninea r Regressi on from sci -iit-lleaeeee to support replication of linear baselines, with de- fault parameters applied our experiments.
A regularized regression method that linearly combines the LI and L2 penalties of the Lasso and Ridge methods, useful for models with many correlated features. The Flast i cNetcV from the k scikit-les toolkit is adopted in MatteryMI..
Elastic Net (Zou& & Hastie 2005)
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Gaussian Process (Williams & Rasmussen, 2006
A non-parametric approach in machine learn- ing where the data is assumed to follow a Gaussian process, allowing for predictions with uncertainty estimation. BatteryML employs the Gaus sinn r wwwesssaeeeeececo rocesseeggesssx from the scikit- Milkitteearnn toolkit, providing probabilistic model for non-linear regression tasks.
Principal Component Regression (Tipping & Bishop 1999) PCR combines principal compo- nent analysis (PCA) for dimensionality reduction with linear regression, focusing the regression on the most significant data variations. Battery utilizes a pipeline of PCA and linear regression in scikit-lea for implementation of PCR.
Partial Least Squares Regression (Geladi & Kowalski, 1986) PLSR, implemented using the P PLSRegress essignsssoonn from the scikit learn toolkit, is applied for modeling relationships be- tween input and output variables. It is statistical method that projects both the predictive variables and the response variables to a new space to improve the prediction accuracy.
Ridge Regression (Hoerl Kennard, 2000)
BatteryML incorporates the scikit-learn learn im- plementation of Ridge regression, method of estimating the coefficients of multiple-regression models in scenarios where independent variables are highly correlated, based on L.2 regularization.
Support Vector Regression (Bishop, 2006) SVR is an extension of support vector machines (SVMs) that supports regression, fitting the best line within a threshold value to the data. We still adopts the implementation of sci wiitlleam
XGBoost Regression (Chen & Guestrin 2016)
XegoostRegressor is an implementation of the gradient boosting decision trees algorithm, designed for speed and performance, that is particularly suited for regression problems, providing a robust and versatile model capable of handling a variety of data types, achieving excellent predictive performance.
LightGBM Regression (Ke et al. 2017) LightGBMRegressor is an advanced implementation of gradient boosting decision tree algorithm, developed by Microsoft, that is designed for efficiency of compute time and memory resources. It is especially suitable for handling large datasets, ex- cels in regression problems and offers superior predictive performance. LightGBM supports both continuous and categorical features.
Random Forest (Breiman 2001; Geurts et al., 2006) Random forest regression is ensemble learning method that operates by constructing multiple decision trees at training time to improve the predictive accuracy and control over-fitting BatteryML incorporates RF strong tree-based baseline for benchmarks.
class of feedforward artificial neural network that con- sists of multiple layers of nodes, each layer fully connected to the next one. Please refer to our configuration files for detailed specifications.
Multilayer Perceptron (Haykin 1994)
Long-Short-Tee Short-Termttttttt Memory (Hochreiter & Schmidhuber 1997) This is a type of recurrent neu- ral network (RNN) capable of learning long-term dependencies, widely used in sequence prediction problems. BatteryML. features a LSTM network layer followed by linear modeling layer to fit the sequential records of the eycles.
Gated Recurrent Unit (Cho et al. 2014) Gated Recurrent Unit (GRU) is a variant of the recurrent neural network (RNN), designed to adaptively capture dependencies of different time scales. Like the LSTM, GRU is also used extensively in sequence prediction problems. It features gating units that modulate the flow of information inside the unit, without having separate memory cells. They have been shown to perform comparably to LSTM with less complexity and computational cost.
Convolutional Neural Networks Krizhevsky et al. 2012) A deep learning algorithm which can take in an input image, assign importance to various aspects/objects in the image and differentiate one from the other. In BatteryML we view the number of cycles and the interpolation dimension as the height and width of image. Please refer to our implementations for details.
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Transformer (Vaswani et al., 2017) A deep learning model that adopts the mechanism of atten- tion, differentially weighting the significance of different parts of the imput data, highly effective in handling sequential data like text and time series.
C DETAILED COMPARISON ANALYSIS
In this section, we benchmark and compare the effectiveness of various machine learning models in three typical battery aging modeling applications, providing detailed comparison of different methodologies available for degradation prediction.
C.1 REMAINING USEFUL LIFE PREDICTION
Table presents the performance of various methods the task of predicting the useful life of batteries. These methods are primarily divided into four categories. The first category, Dummy Regressor, is a baseline using the mean of the training label for prediction. The second category, including the Variance, Discharge, and Full models, comprises linear regression models based on features designed by experts in the battery domain. The Variance model considers only the variance of the QdLinear feature curve. The Discharge model takes into account characteristics during the discharge period, while the Full model also incorporates temperature and charging features. Note that the Full model does not completely encompass the features of Discharge model. For more de- tailed methodologies on feature construction, refer to the (Severson et al. 2019 Attia et al. 2021) our code implementation. The third category consists of traditional statistical models, including lin- ear methods and tree models, which utilize the original QdLinear curves calculated by the difference of 100-th and 10-th cycles their input feature space. The fourth category involves neural network models, with inputs being the QdLinear curves from cycle 1 to 100, minus the data from cycle 10.
Linear Models with Hand-Crafted Features. The Variance model, which uses the variance of the incremental Qd (Vd) curve as a scalar feature, displays moderate prediction accuracy across various datasets but generally falls short when compared to the Discharge and Full models. Both the Discharge and Full models exhibit variable effectiveness with significant errors on certain datasets, indicating need for enhancements in feature design improve the accuracy of linear model fitting.
Traditional Statistical Models. These models achieve significant results by fitting on low-level features, such raw QdLinear curves. Ridge Regression displays consistent, albeit modest, per- formance across datasets, affirming a strong linear correlation between Qd (Vd) feature and battery eycle life. However, its underperformance on datasets like HUST and CRUSH, underscores the chal- lenges in linear correlation learning. Principal Component Regression(PCR and artial Least Squares Regression(PLSR) which apply principal component analysis and project cells into different sub- spaces before fitting linear regression, excel in MATR1, CRUH. and SNL datasets. This suggests that the Qd (Vd) feature exhibits a linear relationship in different subspaces with the battery life. Gaussian Process Regression (GPR) underperforms PCR and PLSR on all datasets, indicating that the pairwise kernel functions struggle to capture the effective degradation patterns for all kinds of batteries. XGBoost and Random Forest, exhibiting strong performance on CRUSH and MIX datasets, indicate that tree-based models' non-linear capabilities are adept at effectively modeling complex battery aging patterns.
Neural Network Models. Neural network models, including Multilayer Perceptron (MLP), Con- volutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer architec- tures, display significant performance variability. MLP performs well on datasets such as SNT, CLO, and CRUH, while CNN shows high sensitivity to initial conditions. LSTM, exhibiting less variabil- ity, proves its robustness and superior performance on the MATR2 dataset among the neural network methods.
In summary, there is no universally optimal method for battery modeling; all methods only excel in certain datasets and may exhibit error divergence in others. This indicates that considerable scope exists for improvement in the accuracy of early battery life prediction. Moreover, existing methods require targeted enhancements to adapt to the complex aging patterns of batteries.
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Table Evaluation Result of CNN with many random seeds for RUL task
Test RMSE
Method
sced0
sced
socd2
seed3
seed4
seed5
secd6
secd7
sccd8
seed9
CNN
76
67
64
74
60
82
65
79
367
78
Prediction Ground Truth RUL
2000
2000
1750
1500
1500
Prediction
Cycle life
1250
1000
1000
500
750
500
500
1000
1500
2000
Ground Truth
Figure 4: A showcase of the correlation between actual and predicted values in the RUL task.
C.2 STATE OF HEALTH ESTIMATION
In practical applications, the estimation of a battery's s State of Health (SOH) necessitates the predic- tion of the current discharge capacity under standardized conditions through reference performance test (RPT), utilizing the charging signal of the present cycle along with historical cycling data. How- ever, the disparity between real-world battery discharge workloads and standard conditions often poses a complication in obtaining precise ground-truth labels, persistent hurdle in the domain of battery aging modeling. To counter this, we approximate the prediction of discharge capacity under current conditions.
The SOH in this experiment quantified percentage, representing the ratio of battery's maxi- mum discharge capacity to its nominal capacity per cycle following the Formula []] For example, battery with nominal capacity of 2Ah and current maximum discharge capacity of 1.8AA equates to an SOH of Le 100 = 90%, yielding an experimental SOH range of 0 to 100.
This task leverages datasets from array of sources, including CALCE, HNEI, HUST, MATR, RWTH, SNL, and UL.PUR. Feature extraction is comprehensive, encompassing elements such as the ratio of the current cycle's maximum charge capacity to the nominal capacity, and the voltage of the initial cycle. We scrutinized variety of models, including Random Forest, Linear Regression, Ridge Regression, and Neural Networks like GRU, MIP, and I.STM. Each model was run with 10 different seeds specific data, the values in each cell of the table represent the mean and standard deviation (std) of the RMSE results from multiple seeds. The benchmark results of the SOH estimation task are delineated in Table C
The efficacy of various methods demonstrates significant variability across different datasets. Lin- models generally exhibit proficient prediction of the SOH across the majority of datasets but underperform when applied to the MATR cells. This suboptimal performance is largely attributed considerable fluctuations in charging strategies among batteries within the MATR data source.
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Table 4: Evaluation Result for SOH (State of Health) task
Test RMSE
Method
CALCE
HNEI
HUST
MATR
Linear Reg
0.45 1 0.06
0.30 1 0.01
4.74 = 4.84
252.75 1 430.58
Ridge Reg
0.46 1 0.06
0.31 + 0.01
4.62 = 4.68
255.27 + 434.10
PLSR
0.60 + 0.10
0.36 0.01
4.39 4.39
258.93 = 441.25
PCR
3.93 + 9.22
0.32 - 0.02
4.00 = 4.28
755.23 1219.55
Random Forest LightGBM
0.72 + 0.36
0.38 0.04
6.04 4.49
0.53 0.30
0.74 0.33
0.34 = 0.02
4.30 3.61
0.97 = 0.44
LSTM
16.78 + 1.41
16.90 + 0.23
9.55 1.92
1.33 + 0.59
MLP
16.73 2.00
14.36 0.86
8.62 2.05
2.89 0.72
GRU
16.77 + 1.41
16.88 + 0.23
9.25 1.78
1.43 + 0.45
Test RMSE
Method
RWTH
SNL.
UUR
Linear Reg
26.48 + 81.96
2.89 + 2.92
0.75 + 0.05
Ridge Reg
15.68 47.79
2.90 + 2.93
0.75 + 0.05
PL.SR
11.83 + 33.53
2.77 + 2.57
0.76 = 0.06
PCR
14.80 + 11.90
13.52 + 15.55
1.22 = 0.27
Random Forest LightGBM
0.17
1.80 + 1.57
1.04 = 0.17
0.17
2.11 0.97
0.99 0.13
LSTM
23.79 + 0.27
7.50 + 0.79
6.57 = 0.36
MLP
63.38 + 11.12
607. + 489.41
16.89 1.57
GRU
23.78 0.27
7.30 + 0.80
6.48 0.36
As batteries progress in their lifecycle, the variances in charging data distribution further intensify, thereby significantly compromising the model's 's capacity for generalization.
Conversely, tree-based models consistently exhibit robust performance across diverse datasets, maintaining low prediction errors, even within the MATR dataset. This robustness intimates the strong baseline that tree classifiers establish in SOH estimation tasks. However, deep learning mod- els have yet to decisively surpass traditional methods in SOH tasks. On a majority of datasets, the errors stemming from deep learning models significantly exceed those from traditional models, indicating a substantial potential for enhancement in deep SOH modeling.
It's essential to note that current methods are yet achieve full optimization, and the labels em- ployed are approximations based on Standard-worklost conditions. Accurate labeling requires the implementation of a standardized workload, methodology that contradicts real-world conditions. This discordance renders the collection of independent and identically distributed training samples, which utilize realistic workloads whilst retaining standard-workload labels, unfeasible. This inher- ent contradiction persists as challenge that the field has yet to effectively surmount.
C.3 STATE OF CHARGGE ESTIMATION
Estimating the State of Charge (SOC) is contingent upon both the discharge capacity a given mo- ment and the SOH for the current cycle. Consequently, acquiring completely accurate SOC labels as challenging as it for SOH. In our methodology, we predict the SOC under the realistic work- load of the battery. For this task, we employed datasets from CALCE, HNET, HUST, MATR, RWTH, SNL, and UURP Feature extraction incorporates estimated current, voltage, and time informa- tion post-interpolation, alongside the charge and discharge capacity curves from preceding cycles. Labels represent the proportion the battery's remaining capacity at a specific point cycle to the current full battery capacity, multiplied by 100. The specific definition is in Formula 2 The performance of the methods of SOC prediction task is displayed in T
Contrasting with the results for RUL and SOH prediction, SOC benchmark results reveal that Light- GBM consistently surpasses other methodologies in the majority of tasks, thereby positioning tree models the prevailing state-of-the-art for SOC prediction. However, all methods exhibit sub-
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Table 5: Evaluation Result for SOC (State Charge) task
Test RMSE
Method
CALCE
HNEI
HUST
MATR
Linear Reg
6.755
0.58
7.197 1 0.21
5.647 1: 4.35
2.698 1 0.25
Ridge Reg
6.755 + 0.58
7.197 1 0.21
5.646 1 4.35
2.698 1 0.25
PLSR
8,780 + 1.03
7.822 0.441
5.113 = 0.83
2.700 0.25
PCR
6,756 0.58
5.647 = 4.35
2.698 0.25
LightGBM
1.714 + 1.02
0.763 + 0.02
0.284 + 0.04
0.824 + 0.58
LSTM
46.502 + 0.85
44.318 + 0.58
27.992 1.14
35.868 + 1.08
MI.P
38.252 + 5.22
16.192 5.57
4.597 + 1.29
4.570 + 2.57
GRU
47.538 0.70
45.568 0.26
28.298 0.27
35.927 1.07
Test RMSE
Method
RWTH
SNL
UL_PUR
Linear Reg
64.498 + 11.64
12.775 0.67
2.484 = 0.19
Ridge Reg
64.500 + 11.64
12.775 + 0.67
2.483 + 0.19
PLSSR
64.504 + 11.64
14.320
2.01
3.933 + 0.29
PCR
64.498 + 11.64
12.775 + 0.67
2.485 + 0.19
LightGBM
313.757 63.76
2.616 1.83
0.844 + 0.27
LSTM
435.523 46.46
27.919 0.34
51.834 0.83
MLP
2224.028 1 3812.76
13.922 3.73
50.185 0.57
GRU
435.551 + 46.44
27.903 + 0.37
51.037 + 0.97
optimal performance on the RWTH dataset, suggesting that current approaches still grapple with adapting to the full spectrum of battery aging patterns. Additionally, linear models persistently outperform deep learning models, implying that both the input features and the network architec- ture necessitate further optimization fully exploit the potential of deep learning models in SOC prediction tasks.
D ABLATION STUDY
In this section, present ablation study of existing methods, focusing the features and hy- perparameters used in the RUL prediction task.
D.I FEATURE SPACE ABLATION
Figure 5 iillustrates the predictive performance of statistical models on the MIX dataset using differ- ent features, where the Variance, Discharge, and Full features are all derived from (Severson et al. 2019).
The predictive performance of the statistical models the MIX dataset, using various features, of- fers distinct insights. The Variance feature, acting as a reliable baseline, provides consistent results across all models. Conversely, the Discharge feature demonstrates weaker performance with linear regression, PCR, and PLSR due to its non-linear characteristics in the MIX dataset, which includes diverse batteries and aging patterns. The Full feature displays strong linear relationship with RUL, performing well across all models. Notably, both the Gaussian Process and Random Forest models yield effective results with the Discharge feature, underscoring its predictive prowess for RUL. The raw Qd (Vd) feature also performs commendably, demonstrating the models' capacity to learn di- rectly from low-level data. However, a significant performance gap exists between the Qd (Vd) and Full features when using Random Forest, indicating considerable scope for improvement in the models' ability to autonomously extract effective features from raw inputs.
D.2 HYPERPARAMETER ABLATION
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1750
"Variance" features
"Discharge" features
1500
"Full" features
QdLinear Curve
1250
RMSE
1000
750
500
250
Linear Regression
PCR
PLSR
Gaussian
Random
Process
Forest
Figure 5: Feature space ablations. The "variance", "discharge" and "full" features are designed by domain experts to capture the degradation pattern of LFP/graphite cells. The "Variance" feature refers to the Log Variance of AQ100-10 during the discharge process. The "Discharge" feature encompasses multiple features extracted from the discharge process. The "Full" feature includes features extracted from both the charging and discharging processes. QdLinear feature obtained by linear interpolation of discharge capacity with respect to voltage.
PLSR
b
Random Forest
XGBoost Regression
335
10
RMSE
238
RMSE
235
10
234
325
4
16
32
16
32
64
128
256
16
32
64
128
256
Number principle components
Number decision trees
Numbar boosters
Figure 6: Hyper parameter analysis for traditional statistical models.
Hyperparameter analysis for statistical models. Figure displays the hyperparameter analysis of traditional statistical methods. Figure of the figure shows the performance of PI.SR with vary- ing numbers of principal components. PLSSR exhibits trend towards overfitting with an increasing number principal components, necessitating cross-validation to determine the optimal number of components.
Figure 6p illustrates the impact of the number of decision trees on the performance of Random Forest. Random Forest shows stable effectiveness, indicating a preference for a moderate number of decision trees to balance performance and computational efficiency.
Figure 6 shows the performance change of XGBoost with the increasing number of boosters. An increase in the number boosters improves effectiveness, with a convergence point observed be- yond 64 boosters. This suggests the feasibility of employing higher number of boosters while maintaining computational practicality.
Hy plpeepaarmetter analysis for deep models. Figure illlustaates the impact of hidden dimensions on the performance of deep models. In Figure an increase in hidden dimensions results in higher variance without a significant change in mean prediction value, suggesting reduced robustness for larger dimensions in the MIX data source.
Figure 7p demonstrates that CNN require an optimal hidden dimension size, as extremes in size lead to high variance; dimension of 16 is found to be most effective.
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a
CNN
700
Mean error
Mean error
Min. to max. error
800
Min. to max error
RMSE
350
RMSE
500
400
300
250
8
16
32
64
128
4
16
32
64
128
Hidden
Hidden dimension
C
LSTM
Transformer
Mean
Mean
Min. to
Min. to
RMSE
RMSE
300
300
250
64
32
Hidden dimension
Hidden dimension
Figure 7: Hyper parameter analysis for deep models.
Figures 7c and 7 showcase the ablation results for LSTM and Transformer models. For sequence modeling approaches, such LSTM and Transformer models, increased model capacity enhances performance, indicating the benefit of using larger model capacities in practical battery modeling scenarios.
E FLEXIBLE EXTENSIONS BASED ON BATTERYML
E.1 PIPELINE INTERFACE FOR EFFICIENT REPLICATION
Code 1: An example configuration file for "Variance" model on MATR1,
train_test__ppitt
name: "MATRPY Tra ttetterr
ceil_data_patie (iala//pcocessee//AARR
feature:
name: Warssmeatodee
interp_dims: 1000
criticai_cysies:
uus_preccaluiated__dl. True
feature featura_trana crrrra_traaaaomtttonn
name: aatoorebataTranstommee
1abel:
RRt.Tabel Annotator! "nnotaber
naze: Deepennielltttrressfaaaaiii
exonsformetionss
name: ToggSal aDatsTrrnaformas
name: 2ScoreDat aaaiaaaaan Coaanoozaaaionn
model:
name:
TLinearfegreesionntn woddooooooooooooor...... redictor'
Almost all modules in BatteryML. can be configured using the configuration file. Code 1 demon- strates an example that specifies "Variance" model on MATR1 dataset. Here we introduce these components in detail.
This module is responsible for dividing the data into training and testing sets. Developers can specify the or implement Splitter classes and specify in the "name" field. We provided predefined Splitter classes for convenient reproduction existing methods. Please refer implementation in batteryml/tra test sp1 it for details.
Module t train_test.split
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Module feature. This module controls the feature extraction process, where not only pro- vide full freedom to users for customization, but also implemented typical features such as Vari- ance, Discharge, and Full model based on AQ)) The predefined features are organized in batteryml ffeeture.
Module label. This module calculates the training target for each cell (e.g., for RUL. predicti)))) or cycle (c.g,, for SOH prediction). BatteryML will take care of the labeling process according to the specifications provided. In the given example, the RULLabe WiILLabeAAnnter or will automatically process each cell and annotate their cycle life according to the specified end-of-life percentage. The implementations are located in battezyml /lbell.
Module feature_t raan formatio on and labeltrrans ormation. These two module is responsible for post-processing features and labels before training. Examples include normalization and data augmentation. In addition to common transformations, we also implemented a sequential transformation wrapper that allows ficxible compositions of transformations. For example, in Code transforms the label by first converting to log scale and then applying z-score normalization. The corresponding code path is batteryml Module model. This module allows users to conveniently define models and their parameters. We provide common models in MatteryML for reproducing existing studies and serving as references for implementing custom models. The model definitions are located in batteryml hooll/mmllllle Combining these modules together, researchers can employ the 'Pipeline" of BatteryML to conduct experiments efficiently, demonstrated in Code 2 Please refer to the documentation for more examples.
setatattrann format t. i ion.
Code 2: Example usage of the Pi Pipelinn API for "Variance" model training and evaluation.
from batteryml ppppllin import Pipeling
from batteryml witeeryym.vvvualll wis.aaliittion..ll
import plot_result
# Create pipeline with a config file by specifying the config_path and workspace. Developers need to modify the data, feature, model and other related seitings in the config file in advence.
pipeline Pipeline (confing
workspace= workspaces")
4 Model training, The training proceas will follow the config rile, and the checkpoints will
be saved workspace.
madel, dataset pipeline.traist (device=' (deeeiicee cuda')
Also, devalopers
previously trained models for evaluscion. The result will be saved
to warkspace.
pipeline, ( cyour checkpoint pathar)
plot result
prediction = modal. (dataset, data_ ttppsttyp=tttt") .t opu") (toppppa") ground_truth dataset, rest_dataa late_aaaaaaelltteeeee to cpop)) plct_result (ground_ predictien)
E.2 UNIFIED DATA FORMAT FOR PUBLIC AND CUSTOM DATA
BatteryML. abstracts general representation BatteryData for the diverse hattery data records in existing studics. This abstraction lies at the core of BatteryML, which organizes the arbitrary form of battery data into unified format, covering most public datasets and the output formats of typical battery cycling test equipment. Here we use MATR data source as an example for demonstration. As shown in Table(] each BatteryData represents battery cell. For each cell, meta information such as cathode material, anode material, etc., as well as charge-discharge cycle data and charge- discharge protocols are recorded. We assume that each cell is related to array of charge-discharge cycles, which we store as a list of cycling records, as shown in Table 7 Each cycle comprises time- series records that include electrical signals such as voltage and current, and may also incorporate other parameters like temperature, resistance, mass, etc. Table demonstrates the data entry method for charge-discharge protocols using a discharge protocol as an example. For instance, in the MATR dataset, the discharge is constant current, so the rate_in _ie_ii__h is fixed at 4C, the starting capacity is 1, the ending capacity is O, and the cycle is continuous. BatteryML also provides a command line interface to conveniently organize existing public datasets into Bat BatteryData, Currently BatteryML supports cell-level learning of batteries for degradation modeling or battery optimization. In the future, BatteryML will accommodate wider range of real-world applications, including higher-Level battery packages such as BMS records of electric vehicles and lower-level data such as half-cells and material properties. MatteryML. will continually foster data exchange in
shown in Code
25

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Published as a conference paper at ICLR 2024
Table Example of the battery meta information of the unified data format.
Attribute
Sample
cell id
MATR blel
form_factor
cylindrical_1866
anode material
graphite
LFP
decetrolytemmatrin
None
nominal capacity .in_Ah
cominialcapacityy... depth of charge
1.1
1.0
depth of discharge
1.0
already wwppss_eeccleesss
0
max_voltage_ limittee__iiiiiin _i__V
3.5
man.voltaeelimii.iin
2.0
max_current limit in A
4.0
wwwreet.limi....... description
0.0
cell data of MATR dataset
cycle data prottcoo
list of CycleData
list of CyclingProtocol
discharge-prout
list of CyclingProtocol
Table 7: Example of the cycling records of the unified data format. The default field primarily includes electrical data due to its high accessibility. Other types data can added as needed.
Attribute
Sample
cycle number
cycle_number
voltage_in_V
[2.0220623, 2.0347204, 2.0466299...]
current.in.A.
[0.0, 0.216028, 0.360339...
charge-capacity ii__Ah
[0.0, 1. L.0401919-066, 1.0401919--06...]
discharge capacity in Ah
[0.0, 6.3991529e-1u 6.3991529e-10...
time in temperature_in in_C
[0.0, 0.002659, 0.003086..]
[31.376623, 31.376623, 31.376623... 0.017038831
internal.resistamee resistance in in_ohm
Table 8: Example of the cycling protocol specification the unified data format. In practice,
battery may employ sequence of such cycling protocols to get fully charged or discharged.
Attribute
Sample
rate in C
4.0
current
None
voltage_in
None
power_in Lower_in_W W
None
start.voltage_ing in_
None
start_soc
1.0
end voltage V
None
end soe
0.0
the battery domain, reducing the barriers to data-driven battery modeling, thereby paving the way for future scientific research.
Code Illustrative Usage for Download Battery Data and Convert Data Format.
pip install -r raqui rementa.txt
install
batteryml download KATR tpath/to/save/zaw///
batteryml preprocess MAIR /ptth/to/ htttt////aa////////aaaoaa/aa
26

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Published as a conference paper at ICLR 2024
E.3 FEATURE INTERFACE FOR DOMAIN KNOWLEDGE INCORPORATION
BatteryML provide general interface for feature extraction, which takes the BatteryData for each cell as input and output extracted features torch. Tensor. Note that the output features of the same cell may contain multiple training instances depending on the task. For example, the feature extractor may output one feature for each cycle of the same cell.
To develop custom feature extractors, developers simply need to implement the process.cell function, which is designed to handle specific cell. BatteryML efficiently manages the iteration through the batteries and organizes the data collation into structured datasets. Code demonsttaats a practical example that calculates the Coulombic efficiency as input features.
Code 4: Example of adding feature Coulombic Efficiency as input.
battecyml/ http:y////eeatueeeeeeeeeeeeeeeee Btt.sueeeeee__eauaeeeeeeee import torch
from typing import
from prc. pre.but Idera import FEATURE EXTRACTORS
bro.data.batterr_aatt. import BatteryData from szc, src. festure.bese import esstresccurebxtaactoor
class NexFesturoEstractor warerer (RccFPatturee3ttt0ttttt def _init__eee (self,
min_eyele_index, int
max =xx_yycle_nndex int 99)
selL.min_cycle_nnne min_eycle_index self self.max_eycle_innee
pax_eyxie_index
def ) cell_data: Ratterynata) -> torch,Tensor: couLombic_efficienntne
# Loop each cycle in the cell date
for vyele_indek, eycle_data in tcell__ateell eycleedaaal__ if self.min_eycle_inneee
tycle_index self wtw..mxxxcyeeeeieeee index::
t Compute the coulembic efficiency for the current eyele
print sec.ulomiieeeficiencce.
coulombie cermiic_fffccinniiiee terch. / feature terch. tensor tensor (Itorch.nesa (coulonbic ceculobbi_efficceenciiee.. efficiencies), sorch.sté[ torch. var (esullittt__ffficinneeessss
coulembie_efficiennn
print Ifeature!
A Replace any NaN or infinite values in the feature tenaor with feature wttttorcc..isnnfee... (feanreer torch. [feature!]
# Return the final feature tengor return
def [_eycle_couu corollecoolombiie www.loobieeeeeeiiooooooeeeeeeeee weffiiieeeeiieey (Q_d, C_e): return 2_d[-1] [Q_c[-1] 1e-5)
The
registry manager will add the custom feature extractor into Bat- teryML. After we add this class into the import stack of BatteryML, coulombic efficiency feature can be used in configuration files for training, shown in Code 5
Code 5: Example configuration file that utilizes the coulombic effiency feature for training and evaluation.
model:
name: "shhearrsggsssiiionitleerddrrrrr
beain_test__plitt
name: "MATRF
ceil_data_path: x data//procsssee/MAcR
Ceature:
nane: "eeeessturBxttracoor
min_eycle_index:
max_oyale_indeey 99
label:
name: "BBLLabelAnnotator
Esstura_ttanaaomaaiionn
name: #ScoranitaTronooommeiiic label_treaffeematiion: name: transformetions:
27

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Published as a conference paper at ICLR 2024
name: LLoggsalccaatsrrenneaaaattttt
nome: 7
E.4 PREPROCESSING INTERFACE FOR CUSTOM DATA CLEANING AND AUGMENTATION
Data preprocessor in BatteryML is used for processing the feature before training and eval- uation, whose input is the output of the feature extractors. Common usage of data pre- processors include data normalization and augmentation. The implementations are organized at batterymi/datan
formation For each data processor. a trans form and or method is required. BatteryML also includes a sequential transformation API that allows users to use transformation compositions flexibly.
Code 6 showcases example of custom data preprocessor, where the input data is normalized by the minimum and maximum values. Code 7 demonstrated an configuration example that uses this nin-max-normalizan feature for learning.
Code 6: Config Setting Use the newly added preprocessor Min-Max normalization.
//t.aa/tteasoormaaaaaaooas www.://oooniinim.p.... import torch
arc.builders inport
frem src.data. rrce.data.traaee.. cra.saataaaooooiioooo..... base import decebatairransiormttiioo
IDATA_TRANSFORNATTON.. register class htw.:/aaee..aaaecmeiiiretttttttd
def _init_ (self, base: float None): self.min None
def tself, data: torch.Tensor)
Tensor] -> torch.Tenson
self.min tir Idala) self.max torch.max Idaze)
def fittet (self):
assert self.min assart salf.max is not None, 'Transformation not fitted!'
None, 'irensformation not fitted!'
Btorch.ne_grad!) def transform(self, data: Corch.Ienson -> torch. Tensor:
self.cstart_ (ittted) data Idsta sel*.min f.alx self.min
BLorch.mo_gradd)
def Laverse_.rrnsffommeeelll, data: terch.Tensor) -> Cerch.Tenson self.casertffittedd
data (self. self.min self.min return dats.
def (selff device):
self.min sel5.min, be (device)) self.max return salf
(device)
Code 7: Example configuration file that uses custom feature preprocessor for learning.
model:
nane: "AinertaeggesiinnlPPeeeeecccoo
Crain_test_splite
nane: "MATRPr htt.::/eeeeeeeeeieeeeesspppppp
cell_data_patia. fatta/processee//aal/
name: 'DischargaMode Foaturs
intesp_dims: 1000
criticsl_oysles:
99
use ase_precalcui atdd_eplddddddddd
label:
name: "BGLiabelAmootatoo" Castura_transfomatiie
name: [ihnkanhttarrenooooees
label_trensfommationn: name: transformations.
28

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Published as a conference paper at ICLR 2024
name: [Laggessleaasssrronaffaammtiii
nome: wtt.fbooaattorrrnnaoooooot.ooo
E.5 MODEL INTERFACE FOR CUSTOM BATTERY MODELS
BatteryML. provides general model interface that follows the convention of "scikit-learn' to sup- port flexible model application to different learning tasks. Traditional statistical models such as linear models, Gaussian Process Regression, tree-based models, SVMs, are required implement the fit and predict method for automatic learning process. Neural network models, the other hand, merely needs to implement the forward logic. BatteryML encapsulates the training and evaluation process of the neural network into fit and predi et methods to align with traditional statistical models for consistent behavior in the learning pipeline. Here we provide an example of using LightGBM for RUL prediction.
Since light.gbm. LGBMRegressor already supports fit and predict for predictions, we can directly inherit the batte eatteryml... mootee model. SklearnModel class and pass the hyperparame- ters of the model through configuration file. Code demonstrates the example implementation for the RUL predictor using LightGBM. In this example, BatteryML will call the fit and predict method of Li ght.UUUULLPPPP redi ctor model for the training and evaluation. Finally, the cus- tom Light GBM model is registered BatteryML. after @MODELS. register () and imported during initialization.
Code & Implementation example of custom models: utilizing LightGBM as a case study. Hyperpa- rameters are configured and passed through the configuration files.
bat balteryml/mode/ ottpppeditet bighgghb.
from lightgbm Amport LGeNRegressor
from erc. builders import MODELS
from www.moll....kla.......mm
import SklearnMode""
RNCDELS
MocDELS.registee. register
class dightgghnuuurrocccttrrr (sslerttol(()
def Lnnt (self, sargs, workspace: str None, vakwargs):
SklearnModel. incbarntodell___itt...... init__self workspace)
smoda TcRMRagresaor (*a*gs, **kwargs)
After defining the model, we can pass the hyperparameters of the RUL predictor through the con- figuration file, as shown in Code 9. The fields in the configuration file will be passed to the model as keyword arguments when initializing predictor.
Code 9: Configuration example for custom LightGBM model.
model:
name: #light glin boasting_typo 'gbde'
learning_rate 0.001
n_estimalors : 200
objective 'rregression'
train_test_splite
nane: "AAFFFiincctettttaateeesssaaaae
ceil_data_path: 'data//processed//aat""
name: wow.apcaaatteeee///atteeeeeeseee
diff_base: max_eycle_index: 98
cycles_to_keep: 98
uss_precalculatee_ddlli
label:
name: /RUTTabe Annotator!
name: "2ooorebtttaeennSSoomois
label_transfoemet
naze: Sesapemmttelllettarraeeeeoaaatts
:ransformetions:
nome: 'LogScal oPatsTrronaoormaiion
name: /SSooreDataTrana "ormmtttttt
29


Reviews:
[{"review_id": "BS2pK1ZmB8", "reviewer": "Reviewer_g7qR", "summary": "This paper presents an open-sourced unified platform and framework for battery life research, including various modules such as configuration, data processing, feature extraction, modeling, and evaluation. Specifically, the authors integrated multiple data sources under a unified data representation, allowing comprehensive comparison of different approaches on a uniform set of evaluation criteria. This work enables ML researchers to propose more interesting and complicated battery science models.", "questions": "Specifically, I could imagine the paper would be more convincing if more discussion and comparison are included. Some of the examples I could imagine include:\n- Having more comparison and discussions between the methods from different categories, such as conventional methods vs tree-based methods vs NN-based methods.\n- Within each category, it would be helpful to show the comparison as well.\n- It would be helpful to have a more detailed ablation study on the hyperparameter choices, feature selections, etc.\n- It could also be beneficial to include a transformer model as well for the NN-based method or explain why this is not a method considered in the paper.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "**Originality:** To the best of my knowledge, this work fills the gap between battery science research and ML research. As stated in the paper, the lack of such a platform is a blocker for both the battery science community and the machine learning community.\n\n**Clarity:** The paper is easy to follow in general, though it might be better to include more details on the methodology and framework with some discussions.\n\n**Significance:** The authors have done significant enough work on data cleaning and processing, feature extraction, model implementation and evaluation, metrics reporting, etc.", "weaknesses": "One of the potential improvements of this work is to include more discussions on the compared algorithms. I think it will be really helpful for people using this framework to get to know the existing approaches better, and their advantages and limitations for different tasks, then making this work more impactful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lt21tdSibk", "reviewer": "Reviewer_ZTeZ", "summary": "The paper proposes a software framework titled BatteryML that provides a unified data representation for the multitude of publicly available datasets, each with its own unique set of variables, goals and representation schemes.\nIn addition to providing an opportunity for battery science and ML experts to collaborate, the platform is meant to encourage the development of algorithms that are less specific to certain data collection schemes and more generally applicable.\nFinally, with an array of tools for feature extraction, data preprocessing and access to more modern machine learning models, BatteryML seeks to collectively advance the state of battery research.", "questions": "- Could the authors clarify how much communication, about the details of battery science and the nuances of various electro-chemical processes involved in battery science is effectively relayed to the end-user, to help formulate models that match physical phenomena? In case this is limited, could the authors justify how the framework could help machine learning researchers effectively collaborate with battery science researchers?\n- Could the authors clarify which among Normalization or Data preprocessing is the correct nomenclature, and update the manuscript to maintain consistency?\n- In addition, please make sure that all acronyms remain well defined prior to their usage.\n- The addition of an example detailing a custom data preprocessing/feature extraction technique would highlight the flexibility of the framework even further.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "- The quality of writing and clarity go a long way to contextualizing how and why the proposed framework is important.\n- The choice of long and detailed examples to highlight different portions of the software framework adds depth to the discussions about the framework.\n- Figure 1 gives a concise overview of the entire framework, that is quickly digestible and informative.", "weaknesses": "- While BatteryML puts a lot of effort into standardizing publicly available datasets and making them accessible to work with, for the wider community, I'm curious how much of the detailed battery science gets communicated to the end-user, or whether the layer of abstraction necessary to standardize the framework obfuscates the nuances to a large degree. The reason this is important is to enable the development of models that actively support/engage with physical phenomena in the correct fashion (physically plausible explanations).\n- There seems to be a mismatch between the keywords used in Figure 1 (Normalization) and subsequent descriptions, which use Data Preprocessing. \n- SOC seems to be used prior to being explicitly defined (1st occurrence in Pg. 2, Contributions while definition is in Section 3.3)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6O58byVYuu", "reviewer": "Reviewer_9hBn", "summary": "This paper describes the current state of research in machine learning applied to battery degradation. The authors claim 3 challenges exist in current research:\n1. Battery dataset are heterogenous in both their measurement techniques and data formats.\n2. Feature extraction is difficult when applying ML techniques to battery data.\n3. Adapting modern ML models to battery data cannot be done in a plug and play fashion.\n\nTo address these problems, the authors provide an open source platform called BatteryML which unifies datasets, provides tools for data preprocessing, feature extraction, model training, prediction and visualization. They also define the primary battery degradation prediction metrics including State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL). Finally, they evaluate multiple models (linear, neural nets, and random forests) on the task of predicting RUL separately on 7 different datasets as well as combinations of these datasets. They present test RMSE on the RUL prediction task and plots of different battery degradation metrics such as battery capacity vs number of charge cycles.", "questions": "- Table 2 has bolded results for the top set of rows (MATR1, MATR2, HUST, SNL), but not the second set. Could you bold the results in the second set of rows as well to highlight the best performing models on those datasets?\n\n- Some table references in the appendix are not clickable.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The paper is written clearly, it lays out the current problems well and provides a good starting point for researchers to build their experiments/methods.\n\n- The unified data format looks very useful for researchers to work with multiple datasets easily providing opportunities for future work in transfer learning and other areas.\n\n- The set of standard feature extractors for battery data is also very useful for ML researchers without battery expertise to get started quickly in battery research.\n\n- The paper provides a link to the git repository which includes more notebooks, plots, and other useful details.", "weaknesses": "- There is little discussion of which models/datasets performed best for the RUL task. Were there certain models which outperform others and if so why?\n\n- While this paper provides a practical software tool for battery data research, it does not provide novel insights or modeling techniques that advance the state of the art in battery SOH or RUL prediction. While RMSE numbers for the RUL task are presented in table 2, the results are not discussed in the text nor are the advantages/disadvantages of each method. Additionally, the paper does not propose new models or methodology to improve the prediction performance on either the RUL or SOH tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "o0iF9bppn6", "reviewer": "Reviewer_3yVC", "summary": "This paper proposes an open-source toolkit for solving battery degradation problems, which is named as BatteryML. It not only provides datasets and feature engineering tools necessary for battery degradation research, but also provides a model library that can easily implement traditional and advanced battery degradation prediction models. By unifying data preprocessing, feature extraction, and model implementation, the proposed toolkit improves the practicality and efficiency of battery degradation research. At the same time, the open-source nature of BatteryML also promotes cooperation and contribution among experts from different fields, furthering the progress of battery technology.", "questions": "no", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThis is the first toolkit that bridges the battery degradation domain and the machine learning domain. \n2.\tThe proposed toolkit unifies the data formats of diverse open datasets and comprises common models used in this domain, which is comprehensive.", "weaknesses": "1.\tThe technical and theoretical contribution of the toolkit itself is limited.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (spotlight)