Battery Thermal Runaway 4stage
ADVANTAGES2 · dim 8SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →
Single Li-ion cell with 4-stage Arrhenius thermal runaway (Hatchard-Dahn model): SEI, anode-electrolyte, cathode, and electrolyte decomposition with gas pressure and resistance tracking.
Problem definition
Canonical benchmark implementation
Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.
Show canonical RHS excerpt
def _arrhenius_rate(A, E, T):
"""Safe Arrhenius rate: A * exp(-E/(R*T)) with clamped exponent."""
arg = np.clip(E / (_R_GAS * T), 0.0, _EXP_CLAMP)
return A * np.exp(-arg)
def _four_stage_decomposition(alpha_sei, alpha_ae, alpha_ca, alpha_el, T):
"""Compute the four decomposition rates and total volumetric heat.
Returns (d_sei, d_ae, d_ca, d_el, q_dot) where q_dot is W/kg (mass-specific).
"""
k_sei = _arrhenius_rate(_A_SEI, _E_SEI, T)
k_ae = _arrhenius_rate(_A_AE, _E_AE, T)
k_ca = _arrhenius_rate(_A_CA, _E_CA, T)
k_el = _arrhenius_rate(_A_EL, _E_EL, T)
# SEI: consumed (α decreases)
d_sei = -k_sei * alpha_sei
# Anode-electrolyte: consumed fraction increases
d_ae = k_ae * alpha_ae * (1.0 - alpha_ae)
# At α_ae=0 this would give zero rate, but the seed is the
# SEI decomposition products — use a small baseline nucleation
# once SEI has started decomposing.
if alpha_ae < 1e-12 and alpha_sei < 0.15 - 1e-6:
d_ae = k_ae * 1e-6
# Cathode: decomposed fraction increases
d_ca = k_ca * (1.0 - alpha_ca)
# Electrolyte: decomposed fraction increases
d_el = k_el * (1.0 - alpha_el)
q_dot = (_Q_SEI * _W_SEI * abs(d_sei)
+ _Q_AE * _W_AE * d_ae
+ _Q_CA * _W_CA * d_ca
+ _Q_EL * _W_EL * d_el)
return d_sei, d_ae, d_ca, d_el, q_dot
def _btr_4stage_rhs(t, y):
alpha_sei = np.clip(y[0], 0.0, 1.0)
alpha_ae = np.clip(y[1], 0.0, 1.0)
alpha_ca = np.clip(y[2], 0.0, 1.0)
alpha_el = np.clip(y[3], 0.0, 1.0)
T = np.clip(y[4], 250.0, 2000.0)
Q_total = y[5]
P_gas = y[6]
R_int = y[7]
d_sei, d_ae, d_ca, d_el, q_dot = _four_stage_decomposition(
alpha_sei, alpha_ae, alpha_ca, alpha_el, T,
)
# Temperature
q_gen = q_dot * _M_CELL
q_cool = _H_CONV * _A_SURF * (T - _T_AMB)
dT = (q_gen - q_cool) / (_M_CELL * _CP)
# Cumulative heat
dQ = q_gen
# Gas pressure: gas moles proportional to electrolyte decomposition
# n_gas ≈ α_el * n_gas_max; dn/dt = n_gas_max * d_el
n_gas_max = 0.01 # mol of gas at full electrolyte decomposition
n_gas = alpha_el * n_gas_max
dn_gas = n_gas_max * d_el
dP = (dn_gas * _R_GAS * T + n_gas * _R_GAS * dT) / _V_HEAD
# Internal resistance growth
dR = _R0 * (0.5 * abs(d_sei) + 2.0 * d_ae)
dy = np.empty(8)
dy[0] = d_sei
dy[1] = d_ae
dy[2] = d_ca
dy[3] = d_el
dy[4] = dT
dy[5] = dQ
dy[6] = dP
dy[7] = dR
return dy- Parameters
- _A_AE = 2.5e+13
- _A_CA = 6.667e+13
- _A_EL = 5.14e+25
- _A_SEI = 1.667e+15
- _A_SURF = 0.000818
- _CP = 830
- _EXP_CLAMP = 80
- _E_AE = 135080
- _E_CA = 139600
- _E_EL = 274000
- _E_SEI = 135080
- _H_CONV = 10
- _M_CELL = 0.044
- _Q_AE = 1.714e+06
- _Q_CA = 314000
- _Q_EL = 155000
- _Q_SEI = 257000
- _R0 = 0.02
- _R_GAS = 8.314
- _T_AMB = 298
- _V_HEAD = 1e-06
- _W_AE = 0.5
- _W_CA = 0.25
- _W_EL = 0.217
- _W_SEI = 0.033
- Initial condition
- y(0) = [0.15, 0, 0, 0, 420, 0, 101325, 0.02]
- Horizon
- t ∈ [0, 600]
Canonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.
Fingerprint
Spread: extreme
Default noise: high
Recommendation snapshot
Clean best: SolvSRK
Noisy best: SolvSRK
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
Versions & freeze
Methodology →- Freeze
- 2026-08-13
- libsolvsrk
- 2.3.0
- SciPy
- 1.14
- SUNDIALS
- CVODE (bundled backend)
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Battery Thermal Runaway 4stage (battery-thermal-runaway-4stage)
Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.
Self-reported by Resonix Labs · not independently verified
Results matrix
Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.
Objective
Best overall trade-off of survival, precision, and speed.
Noise level
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | SolvSRK | 100% | 14.8 | 245,185 | 6.44 s | 0.972 |
| 2 | SciPy RadauSciPy | 100% | 12.3 | 686 | 32 ms | 0.911 |
| 3 | SciPy DOP853SciPy | 100% | 11.1 | 218 | 7 ms | 0.883 |
| 4 | SciPy LSODASciPy | 100% | 10.4 | 318 | 9 ms | 0.867 |
| 5 | SciPy BDFSciPy | 100% | 10.0 | 406 | 23 ms | 0.857 |
| 6 | Tsit5external | 100% | 9.9 | 264 | 756 ms | 0.854 |
| 7 | SciPy RK45SciPy | 100% | 9.8 | 272 | 9 ms | 0.853 |
| 8 | SciPy RK23SciPy | 100% | 9.8 | 794 | 27 ms | 0.851 |
| 9 | CVODE BDFexternal | 100% | 9.7 | 171 | 12 ms | 0.850 |
| 10 | CVODE Adamsexternal | 100% | 9.2 | 123 | 11 ms | 0.838 |
At Clean, best balanced arm is SolvSRK.
Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →
Cite this page
Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is - a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.
@misc{resonix_evidence_battery_thermal_runaway_4stage_2026,
title = {Resonix Evidence Portal: Battery Thermal Runaway 4stage},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/battery-thermal-runaway-4stage}},
note = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}Related
TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels
Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology
Self-reported by Resonix Labs · not independently verified · Verification status