Battery Echem Thermal Coupled
ADVANTAGES3 · dim 16SolvSRK 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 →
Electrochemical-thermal coupled battery abuse model: 4-stage Arrhenius TR + Butler-Volmer kinetics + solid-phase Li diffusion in both electrodes. Extreme stiffness from electrochemical timescales.
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 _butler_volmer(i0, eta, T):
"""Butler-Volmer current density with symmetric transfer coefficients."""
f = _F / (_R_GAS * T)
arg_a = np.clip(_ALPHA_BV * f * eta, -_EXP_CLAMP, _EXP_CLAMP)
arg_c = np.clip(-_ALPHA_BV * f * eta, -_EXP_CLAMP, _EXP_CLAMP)
return i0 * (np.exp(arg_a) - np.exp(arg_c))
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 _radial_diffusion_3node(c, D_s, r_p):
"""Spherical Fickian diffusion on 3 uniform radial nodes (center, mid, surface).
Returns dc/dt for the 3 nodes. Boundary: dc/dr=0 at center, zero-flux at surface.
"""
dr = r_p / 2.0
dc = np.zeros(3)
# center (symmetry BC): forward difference approximation
dc[0] = 6.0 * D_s * (c[1] - c[0]) / (dr * dr)
# mid-point
r_m = dr
flux_out = D_s * ((r_m + 0.5 * dr) ** 2) * (c[2] - c[1]) / dr
flux_in = D_s * ((r_m - 0.5 * dr) ** 2) * (c[1] - c[0]) / dr
dc[1] = (flux_out - flux_in) / (r_m * r_m * dr)
# surface (zero-flux BC at outer boundary for now)
r_s = r_p
flux_in_s = D_s * ((r_s - 0.5 * dr) ** 2) * (c[2] - c[1]) / dr
dc[2] = -flux_in_s / (r_s * r_s * dr)
return dc
def _echem_thermal_rhs(t, y):
# Decomposition fractions
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)
# Butler-Volmer overpotentials (treated as state-like for stiffness)
eta_a = y[5]
eta_c = y[6]
# Li concentrations (3 radial nodes each)
c_a = np.clip(y[7:10], 0.0, _CS_MAX_A)
c_c = np.clip(y[10:13], 0.0, _CS_MAX_C)
Q_total = y[13]
P_gas = y[14]
R_int = y[15]
# --- 4-stage decomposition ---
d_sei, d_ae, d_ca, d_el, q_dot_decomp = _four_stage_decomposition(
alpha_sei, alpha_ae, alpha_ca, alpha_el, T,
)
# --- Butler-Volmer kinetics ---
i_bv_a = _butler_volmer(_I0_ANODE, eta_a, T)
i_bv_c = _butler_volmer(_I0_CATHODE, eta_c, T)
# Overpotential relaxation toward equilibrium (τ ~ RC time constant)
tau_relax = 1.0 # s
# Surface concentration deviation from equilibrium drives η
theta_a = c_a[2] / _CS_MAX_A
theta_c = c_c[2] / _CS_MAX_C
# OCV approximation (simplified lithium intercalation)
U_a = 0.6 - 0.5 * theta_a
U_c = 4.2 - 0.8 * theta_c
V_cell = U_c - U_a
d_eta_a = (-eta_a + (V_cell * 0.5 - U_a)) / tau_relax
d_eta_c = (-eta_c + (U_c - V_cell * 0.5)) / tau_relax
# --- Solid-phase diffusion ---
dc_a = _radial_diffusion_3node(c_a, _DS_ANODE, _RP_ANODE)
dc_c = _radial_diffusion_3node(c_c, _DS_CATHODE, _RP_CATHODE)
# Electrode coupling: BV current consumes/produces Li at surface node
# j_n = i_BV / F (flux in mol/(m²·s))
dc_a[2] += i_bv_a / _F
dc_c[2] -= i_bv_c / _F
# --- Temperature ---
q_echem = abs(i_bv_a * eta_a) + abs(i_bv_c * eta_c) # W/m² → scale to cell
q_gen = q_dot_decomp * _M_CELL + q_echem * _A_SURF
q_cool = _H_CONV * _A_SURF * (T - _T_AMB)
dT = (q_gen - q_cool) / (_M_CELL * _CP)
# Cumulative heat
dQ = q_gen
# Gas pressure
n_gas_max = 0.01
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
dR = _R0 * (0.5 * abs(d_sei) + 2.0 * d_ae)
dy = np.empty(16)
dy[0] = d_sei
dy[1] = d_ae
dy[2] = d_ca
dy[3] = d_el
dy[4] = dT
dy[5] = d_eta_a
dy[6] = d_eta_c
dy[7:10] = dc_a
dy[10:13] = dc_c
dy[13] = dQ
dy[14] = dP
dy[15] = dR
return dy- Parameters
- _ALPHA_BV = 0.5
- _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
- _CS_MAX_A = 31370
- _CS_MAX_C = 51410
- _DS_ANODE = 3.9e-14
- _DS_CATHODE = 1e-13
- _EXP_CLAMP = 80
- _E_AE = 135080
- _E_CA = 139600
- _E_EL = 274000
- _E_SEI = 135080
- _F = 96485
- _H_CONV = 10
- _I0_ANODE = 10
- _I0_CATHODE = 1
- _M_CELL = 0.044
- _Q_AE = 1.714e+06
- _Q_CA = 314000
- _Q_EL = 155000
- _Q_SEI = 257000
- _R0 = 0.02
- _RP_ANODE = 1.25e-05
- _RP_CATHODE = 8.5e-06
- _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, 450, 0, …] [shape=(16,), min=0, max=101325]
- Horizon
- t ∈ [0, 300]
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 Echem Thermal Coupled (battery-echem-thermal-coupled)
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% | 8.7 | 7,027 | 239 ms | 0.827 |
| 2 | SciPy BDFSciPy | 100% | 7.7 | 3,521 | 288 ms | 0.802 |
| 3 | SciPy LSODASciPy | 100% | 6.8 | 2,943 | 154 ms | 0.781 |
| 4 | CVODE BDFexternal | 100% | 5.5 | 1,703 | 64 ms | 0.751 |
| 5 | SciPy RK23SciPy | 100% | 5.1 | 9,239 | 324 ms | 0.740 |
| 6 | CVODE Adamsexternal | 100% | 5.0 | 1,686 | 62 ms | 0.739 |
| 7 | SciPy DOP853SciPy | 100% | 4.6 | 3,062 | 173 ms | 0.729 |
| 8 | SciPy RK45SciPy | 100% | 4.6 | 3,164 | 177 ms | 0.729 |
| 9 | Tsit5external | 100% | 4.6 | 3,108 | 837 ms | 0.728 |
| - | SciPy RadauSciPy | 0% | - | - | - | - |
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_echem_thermal_coupled_2026,
title = {Resonix Evidence Portal: Battery Echem Thermal Coupled},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/battery-echem-thermal-coupled}},
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