Battery Echem Thermal Coupled

ADVANTAGES3 · dim 16

SolvSRK 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.

Batteries & energy storage

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

#SolverSurvivalSCDnfevWallScore
1SolvSRK
100%
8.77,027239 ms0.827
2SciPy BDFSciPy
100%
7.73,521288 ms0.802
3SciPy LSODASciPy
100%
6.82,943154 ms0.781
4CVODE BDFexternal
100%
5.51,70364 ms0.751
5SciPy RK23SciPy
100%
5.19,239324 ms0.740
6CVODE Adamsexternal
100%
5.01,68662 ms0.739
7SciPy DOP853SciPy
100%
4.63,062173 ms0.729
8SciPy RK45SciPy
100%
4.63,164177 ms0.729
9Tsit5external
100%
4.63,108837 ms0.728
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 →

SolvScout · free

Profile your problem for free

This page shows one published benchmark cell. SolvScout fingerprints your ODE, compares it to the full corpus, and recommends a solver with the same survival / precision / speed ranking you see here — including when a SciPy arm wins.

SolvSRK · 30-day trial

Run the winner on your machine

SolvSRK is the stiffness-adaptive integrator behind the SolvSRK column in these tables. Create an account, activate a machine, and take a 30-day trial — same binary you'd ship after purchase.

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://resonix.tech/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