Quantifying the Gap Between Laboratory Battery Test Patterns and Field Duty Profiles

Chunyang Zhao; Chresten Træholt

cs.LGAug 17, 2026

Quantifying the Gap Between Laboratory Battery Test Patterns and Field Duty Profiles

Authors: Chunyang Zhao, Chresten Træholt

Organizations: Department of Wind and Energy Systems Technical University of Denmark Kongens Lyngby, Denmark

Abstract

Laboratory battery tests provide the main empirical basis for battery performance and degradation studies, but their operating patterns do not directly represent field duty profiles. This paper quantifies the gap by comparing six accessible evidence sources covering controlled cycling, drive-cycle testing, dynamic cycling, NMC811 laboratory ageing, a real electric-vehicle charging trace, and fleet-scale electric-vehicle state-of-health (SOH) data. The analysis combines usage frequency, usage intensity, usage C-rate, and a duty-structure index (DSI) based on normalized current dispersion and ramping. The representative single-segment DSI ranges from 0.630 for the field source trace and 0.699 for NASA to 2.936 for Oxford and 2.855 for Imperial, while usage C-rate ranges from 0.14-0.40 for Imperial, NASA, Stanford, and Hyundai to 2.00 for Oxford. Long-term ageing also differs: the 80 percent retention region occurs near 351 NASA cycles, 6292 Oxford checkpoints, and 1019 Stanford cycles. In chemistry-aligned NMC/NCM evidence, Imperial retains 0.813 under standard cycling and 0.865 under drive-cycle ageing, while the field source has median SOH 0.889 with visible dispersion. Field operation further shows a median use intensity of 137.2 km/day and 56.9 percent of charges ending at or above 95 percent SOC. These results show that battery performance metrics are conditional on the duty pattern that generated them; application-oriented studies should report explicit duty-profile descriptors together with chemistry, capacity, and ageing metrics.

Figures & tables

Source	System	Chemistry	Capacity	$V$ mean [min,max] (V)	$I$ mean [min,max]	$T$ mean [min,max] ( ∘ C)	Test / operating case
NASA B0005 [ 3 ]	Cell	Li-ion	2.0 Ah	3.56 [2.63, 4.20]	-1.92 A [-2.02, 0.00] A	31.92 [23.78, 38.46]	Controlled degradation baseline; constant-current ageing with capacity and impedance follow-up
Oxford NCA Cell1 [ 4 , 5 ]	Cell	NCA	0.74 Ah	3.88 [3.60, 4.20]	-0.12 A [-5.00, 1.60] A	40.38 [39.93, 41.09]	Application-shaped bridge dataset; drive-cycle discharge in thermal-chamber testing
Imperial 21700 [ 9 ]	Cell	NMC811 / C-SiOx	4.865 Ah	3.66 [2.50, 4.17]	-0.50 A [-0.50, -0.50] A	25.93 [24.35, 26.70]	Chemistry-aligned ageing reference; standard-cycle vs drive-cycle ageing at $25\,^{\circ}\mathrm{C}$
2025 EV fleet source [ 8 ]	EV fleet	NCM	155 Ah a	4.04 [3.75, 4.25]	73.61 A [0.00, 103.50] A	31.25 b [30.00, 32.00] b	Field health and usage diversity; in-service EV operation, charging windows, and SOH statistics
Stanford cell 003 [ 7 ]	Cell	SiOx–Gr / NCA	n.r. c	3.57 [2.98, 4.20]	0.00 d [-0.10, 0.50] d	35.00 e [35.00, 35.00] e	Dynamic protocol comparison; long charge-rest-discharge sequence under dynamic cycling
Hyundai Ioniq 5 [ 10 ]	Vehicle session	SK On NCM pouch	111–121 Ah f	233.29 [75.48, 285.19]	30.69 A [0.52, 48.08] A	2.42 g [-0.50, 5.40] g	Real duty-trace example; real EV AC charging session with SOC and phase-power records

TABLE I: Focused data description for the quantified sources used in the comparison.

Fig. 1: Normalized current profiles over selected cycles, operating windows, or source-data traces. Note: The 2025 EV fleet trace occupies only segment 1 because repeated sections are not available.

Fig. 2: Voltage, normalized-current, and temperature responses for selected cell-level records in reference performance tests (RPTs)

Fig. 3: Long-term retention and SOH comparison across laboratory datasets and chemistry-aligned evidence using normalized aging progress.

Fig. 4: Field-EV evidence to characterize real operating diversity.

Source	DSI	UF	UI	UC	Degradation	Main implication
NASA	0.699	0.70	0.296	0.29	80%@351 cycles	Fastest degradation under controlled high-intensity cycling.
Oxford	2.936	0.75	0.817	2.00	80%@6292 checkpoints	Highest sustained usage C-rate with longest cycling lifetime.
Stanford	0.639	0.66	0.082	0.40	80%@1019 cycles	Smooth low-C-rate cycling with reduced operational stress.
Imperial	2.855	0.77	0.150	0.14	$\Delta$ ret. +0.052 at RPT	Highest ramp-dominated duty structure and transient variability.
Hyundai	2.268	0.84	0.132	0.26	n.a.	No degradation label; full-session $\Delta$ SOC 53.0.
EV fleet	0.630	1.00	–	0.48	SOH 50 0.889; cycles n.r.	Median field SOH; cycle count not reported.

TABLE II: Key quantified duty-structure, degradation, and operating-drift indicators across representative single segments and source records.

Lithium-Ion Batteries

Lithium-ion batteries are rechargeable energy-storage systems studied through their materials, performance, aging, safety, and recycling.

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A Repeated Measurements Approach to $SoH$ Battery Modelling of Cyclic Aged Data in a Laboratory Environment

Mark Cary, Charles Bokor

Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, UK · School of Engineering, Computing and Mathematics, Oxford Brookes University Oxford OX33 1HX, UK

Lithium-Ion Batteries Mixed-Effects Models

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This document describes the application of a first order linearised nonlinear repeated measurements approach to the analysis of battery cell ageing profiles generated under controlled conditions in a laboratory. The primary advantage of the model is it reflects the obvious structure in the data. Consequently, it is a two-component of variance model: variation within ageing profiles (measurement noise) and variation among ageing profiles (test-to-test or cell-to-cell) variation. Novel regularised iterative generalised least squares parameter identification schemes, with optimal hyper-parameter re-estimation, are used to identify the hierarchical nonlinear model. The training data comprised

SoH

profiles for 10 cells aged at various constant discharge and charge current cycles at a fixed chamber environmental temperature of 25 [

^\circ

C]. Each cell

SoH

profile is modelled using a simple power law expression, whereas the variation in ageing parameters is modelled using a single knot cubic B-spline.

SoH

is accurately predicted to

\pm 0.191\%

for

SOH \in [0,20]

Abstract

Lithium-Ion Batteries

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