T1 18650 rectangular surrogate optimization#

Tier-1 rectangular battery sizing benchmark with fixed topology representation and switchable analytic, explicit-circuit, or hybrid-thermal scoring.

See Optimization Problem Catalog for the optimization family index.

Quick Facts#

Field

Value

Problem ID

battery_18650_t1_rectangular_surrogate_opt

Problem Family

optimization

Implementation

design_research_problems.problems.optimization._battery_tiers:Battery18650T1RectangularSurrogateOptimizationProblem

Capabilities

baseline-solver, bounded-variables, statement-markdown

Study Suitability

none

Tags

optimization, battery, tiered, tier-1, rectangular, surrogate

Taxonomy#

Formulation

mixed_discrete_optimization

Convexity

nonconvex

Design Variable Type

discrete

Is Dynamic

no

Orientation

engineering_practical

Feasibility Ratio Hint

0.1

Objective Mode

single

Constraint Nature

hard

Bounds Summary

two bounded integer variables: series and parallel counts

Tags

optimization, battery, tiered, tier-1, rectangular, surrogate

Benchmark Contract#

Benchmark Question

How well do methods handle discrete rectangular pack sizing when geometry and wiring are fixed but requirements remain hard?

Physically Modeled

Canonical rectangular SxP pack sizing relations; Pack envelope, cell count, and a steady-state thermal proxy; Backend consistency checks through the shared battery cell model path

Deliberate Surrogates

Topology is fixed to a full rectangular series-parallel family; Electrical performance is summarized by analytic pack equations; Thermal behavior is represented by a one-state Joule-heating proxy

Representation Mode

rectangular

Default Evaluation Mode

analytic_surrogate

Supported Evaluation Modes

analytic_surrogate, explicit_circuit, hybrid_thermal

Validation Scope

Analytically checked surrogate consistency; Canonical-pack backend sanity checks

Solver Role

deterministic baseline search

Statement#

Optimize the lowest-freedom battery design space by selecting only series and parallel counts over canonical rectangular 18650 packs.

Electrical approximation:

  • V_pack ~= S * V_cell

  • C_pack ~= P * C_cell

  • I_limit ~= P * C_cell * C_rate_max

With the packaged 18650 constants:

  • V_cell = 3.7 V

  • C_cell = 2.5 Ah

  • C_rate_max = 10 C

Thermal proxy:

  • I_cell = I_load / P

  • Q_dot = N_cells * I_cell^2 * R_int

  • T_max = T_ambient + Q_dot / (G_passive + hA)

Objective:

  • Minimize weighted normalized volume, cell-count cost proxy, and peak

temperature, with hard-constraint violations penalized.

Problem Shape#

Field

Value

Design Variable Count

2

Bound Summary

two bounded integer variables: series and parallel counts

Total Constraint Count

8

Equality Constraint Count

0

Inequality Constraint Count

8

Variable Bounds#

Variable

Lower Bound

Upper Bound

x[0]

1

24

x[1]

1

27

Manifest Parameters#

Key

Value

ambient_temperature_c

25

cooling_coefficient_w_per_m2k

18

evaluation_mode

analytic_surrogate

load_current_a

60

max_depth_mm

500

max_height_mm

250

max_width_mm

500

maximum_temperature_c

60

minimum_capacity_ah

10

minimum_current_a

60

objective_weights

{“cost”: 0.65, “temperature”: 0.15, “volume”: 0.2}

passive_cooling_w_per_k

1

target_voltage_v

14.8

voltage_tolerance_v

0.1

Library Interface#

  • generate_initial_solution(seed=None)

  • objective(x)

  • evaluate(x)

  • solve(initial_solution=None, seed=None, maxiter=200)