T4 18650 thermal hybrid grammar#
Tier-4 thermo-topological battery grammar benchmark with analytic, explicit-circuit, and hybrid-thermal evaluator modes.
See Grammar Problem Catalog for the grammar family index.
Quick Facts#
Field |
Value |
|---|---|
Problem ID |
|
Problem Family |
grammar |
Implementation |
|
Capabilities |
|
Study Suitability |
none |
Tags |
|
Taxonomy#
- Formulation
discrete_grammar
- Convexity
not_applicable
- Design Variable Type
mixed
- Is Dynamic
no
- Orientation
engineering_practical
- Feasibility Ratio Hint
0.02
- Objective Mode
multi
- Constraint Nature
hard
- Bounds Summary
tier-3 topology/pose controls plus thermal-parameter tuning actions
- Tags
grammar,battery,tiered,tier-4,thermal-topology,hybrid
Benchmark Contract#
- Benchmark Question
How well do grammar methods co-design topology, geometry, and thermal controls when evaluator fidelity can be stepped up without changing representation?
- Physically Modeled
Tier-3 topology allocation and pose geometry; Thermal control variables for convection, passive cooling, and ambient conditions; PyBaMM-derived thermal priors and steady-state thermal-network evaluation in hybrid mode
- Deliberate Surrogates
Electrical scoring can remain surrogate or projected explicit depending on evaluation_mode; Hybrid thermal scoring still projects topology-allocation states to a canonical explicit netlist
- Representation Mode
thermal_topology- Default Evaluation Mode
hybrid_thermal- Supported Evaluation Modes
analytic_surrogate,explicit_circuit,hybrid_thermal- Validation Scope
Qualitative thermal trend validation; Mode-consistency checks across analytic, explicit, and hybrid evaluators
- Solver Role
grammar benchmark; no packaged optimizer
Statement#
Tier 4 unlocks thermal-system design variables in addition to Tier-3 topology and geometry decisions. The grammar supports topology edits, pose edits, and cooling-condition tuning actions with a PyBaMM-backed thermal evaluator.
This is the highest-freedom battery rung in the tiered ladder.
Tier-4 extends tier-3 actions with thermal-parameter tuning. In addition to topology and geometry edits, actions can adjust:
h (convective cooling coefficient)
G_passive (passive thermal conductance)
T_ambient (ambient condition)
thermal model selector (lumped or multi_node_2node)
Thermal heating term (from PyBaMM-derived resistance prior):
I_cell = I_load / P_eq
q_i = I_cell^2 * R_eff(SOC_ref)
Multi-node mode (default) solves steady-state balances over nodes [core_i, surface_i, coolant], with inter-cell conductance edges G_ij derived from geometric proximity, local cooling terms G_sc,i, and coolant-to-ambient conductance.
Reported T_max is max_i(T_core_i), not an averaged pack temperature.
BattMo/PyBattMo integration is intentionally deferred in this release to keep the core benchmark path Python-only and CI-stable.
Problem Shape#
Field |
Value |
|---|---|
State Type |
tuple |
Initial Transition Count |
16 |
Initial Rule Names |
|
Manifest Parameters#
Key |
Value |
|---|---|
ambient_temperature_bounds |
{“lower”: 5.0, “upper”: 45.0} |
cooling_coefficient_bounds |
{“lower”: 5.0, “upper”: 50.0} |
evaluation_mode |
hybrid_thermal |
imbalance_model |
min_stage |
load_current_a |
60 |
max_cell_count |
24 |
max_depth_mm |
500 |
max_height_mm |
250 |
max_width_mm |
500 |
maximum_temperature_c |
60 |
minimum_capacity_ah |
10 |
minimum_current_a |
60 |
minimum_spacing_mm |
2 |
objective_weights |
{“cost”: 0.25, “temperature”: 0.4, “volume”: 0.35} |
passive_cooling_bounds |
{“lower”: 0.1, “upper”: 10.0} |
target_voltage_v |
14.8 |
thermal_airflow_axis |
x |
thermal_contact_decay_mm |
2 |
thermal_contact_resistance_k_per_w |
2.5 |
thermal_flow_shadowing_factor |
0.25 |
thermal_model |
multi_node_2node |
thermal_neighbor_clearance_mm |
8 |
thermal_reference_soc |
0.5 |
voltage_tolerance_v |
0.1 |
Initial Transition Summary#
Rule Name |
Transition Count |
|---|---|
adjust_cell_count |
2 |
adjust_series_count |
2 |
move_cell_x |
2 |
move_cell_y |
2 |
move_cell_z |
2 |
tune_ambient |
2 |
tune_cooling_coefficient |
2 |
tune_passive_cooling |
2 |
Library Interface#
initial_state()enumerate_transitions(state)enumerate_next_states(state)evaluate(state)