Battery / thermal / power budget

ADVANTAGES2 · 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 →

Voltage, SoC, thermal derating feeding torque cap into A.1/A.2

Drone dynamics & autonomy

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 _pd_controller(y, sp=None, mass=None):
    """Cascaded PD: pos error → desired attitude → torques. Returns (T, tau_x, tau_y, tau_z).

    BA-2 (2026-04-29): added optional ``mass`` kwarg so the
    factory variants in ``make_rhs_A3`` / ``make_rhs_A7`` can override
    the module-global ``MASS`` for the hover-thrust feed-forward term.
    THRUST_MAX is held at the parent airframe's value (39.24 N) since it
    represents the physical thrust ceiling; airframe-mass perturbations
    in {0.8..1.2} kg stay well within this envelope.
    """
    if sp is None:
        sp = HOVER_SP
    eff_mass = MASS if mass is None else mass
    px, py, pz = y[0], y[1], y[2]
    vx, vy, vz = y[3], y[4], y[5]
    phi, theta, psi = y[6], y[7], y[8]
    p, q, r = y[9], y[10], y[11]

    ax_d = KP_POS * (sp[0] - px) + KD_POS * (sp[3] - vx)
    ay_d = KP_POS * (sp[1] - py) + KD_POS * (sp[4] - vy)
    az_d = KP_POS * (sp[2] - pz) + KD_POS * (sp[5] - vz)

    T_des = eff_mass * (G + az_d)
    phi_des = (1.0 / G) * (ax_d * np.sin(psi) - ay_d * np.cos(psi))
    theta_des = (1.0 / G) * (ax_d * np.cos(psi) + ay_d * np.sin(psi))

    tau_x = KP_ATT * np.arctan2(np.sin(phi_des - phi), np.cos(phi_des - phi)) - KD_ATT * p
    tau_y = KP_ATT * np.arctan2(np.sin(theta_des - theta), np.cos(theta_des - theta)) - KD_ATT * q
    tau_z = KP_YAW * np.arctan2(np.sin(-psi), np.cos(-psi)) - KD_YAW * r

    T_des = np.clip(T_des, 0.0, THRUST_MAX)
    tau_x = np.clip(tau_x, -TORQUE_CLIP, TORQUE_CLIP)
    tau_y = np.clip(tau_y, -TORQUE_CLIP, TORQUE_CLIP)
    tau_z = np.clip(tau_z, -TORQUE_CLIP * 0.25, TORQUE_CLIP * 0.25)
    return T_des, tau_x, tau_y, tau_z

def _body_forces(T, phi, theta, psi):
    """Thrust-to-inertial force components."""
    cp, sp = np.cos(phi), np.sin(phi)
    ct, st = np.cos(theta), np.sin(theta)
    cy, sy = np.cos(psi), np.sin(psi)
    Fx = T * (cy * st * cp + sy * sp)
    Fy = T * (sy * st * cp - cy * sp)
    Fz = T * ct * cp
    return Fx, Fy, Fz

def _euler_kinematics(phi, theta, p, q, r):
    """Euler-angle rates from body rates. Returns (dphi, dtheta, dpsi)."""
    cp, sp = np.cos(phi), np.sin(phi)
    theta_c = np.clip(theta, -1.39, 1.39)
    tan_th = np.tan(theta_c)
    cos_th = np.cos(theta_c)
    sec_th = 1.0 / cos_th if abs(cos_th) > 1e-12 else 1e12 * np.sign(cos_th)
    dphi = p + q * sp * tan_th + r * cp * tan_th
    dtheta = q * cp - r * sp
    dpsi = (q * sp + r * cp) * sec_th
    return dphi, dtheta, dpsi

def _quad12(y, T, tau_x, tau_y, tau_z, mass=None):
    """Core 12-state quadrotor dynamics. Returns d[0:12].

    BA-2 (2026-04-29): added optional ``mass`` kwarg so the
    factory variants can override the module-global ``MASS`` for the
    translational acceleration / drag terms.
    """
    eff_mass = MASS if mass is None else mass
    phi, theta, psi = y[6], y[7], y[8]
    p, q, r = y[9], y[10], y[11]
    Fx, Fy, Fz = _body_forces(T, phi, theta, psi)

    d = np.empty(12)
    d[0] = y[3]; d[1] = y[4]; d[2] = y[5]
    d[3] = (Fx - CD * y[3]) / eff_mass
    d[4] = (Fy - CD * y[4]) / eff_mass
    d[5] = (Fz - CD * y[5]) / eff_mass - G
    d[6], d[7], d[8] = _euler_kinematics(phi, theta, p, q, r)
    d[9] = (tau_x + (IYY - IZZ) * q * r) / IXX
    d[10] = (tau_y + (IZZ - IXX) * p * r) / IYY
    d[11] = (tau_z + (IXX - IYY) * p * q) / IZZ
    return d

def rhs_A5(t, y):
    body = y[:12]
    soc = y[12]
    temp = y[13]

    V_oc = _BATT_V_NOMINAL * (0.5 + 0.5 * soc)
    T_cmd, tx, ty, tz = _pd_controller(body)
    I_draw = np.clip(T_cmd / V_oc, 0.0, 40.0)

    V_terminal = V_oc - _R_PHASE * I_draw
    derating = np.clip(1.0 - 0.02 * (temp - 45.0), 0.5, 1.0)
    T_actual = T_cmd * derating

    d_body = _quad12(body, T_actual, tx, ty, tz)
    d_soc = -I_draw / (_BATT_CAPACITY * 3.6)
    P_loss = I_draw**2 * _BATT_R_INT
    d_temp = (P_loss * _THERMAL_R - (temp - _T_AMBIENT)) / _THERMAL_TAU

    return np.concatenate([d_body, [d_soc, d_temp]])
Parameters
  • CD = 0.1
  • G = 9.81
  • HOVER_SP = [0, 0, 5, 0, 0, 0]
  • IXX = 0.0082
  • IYY = 0.0082
  • IZZ = 0.0148
  • KD_ATT = 2.5
  • KD_POS = 4
  • KD_YAW = 1.5
  • KP_ATT = 8
  • KP_POS = 6
  • KP_YAW = 4
  • MASS = 1
  • THRUST_MAX = 39.24
  • TORQUE_CLIP = 2
  • _BATT_CAPACITY = 2500
  • _BATT_R_INT = 0.05
  • _BATT_V_NOMINAL = 11.1
  • _R_PHASE = 0.15
  • _THERMAL_R = 0.02
  • _THERMAL_TAU = 120
  • _T_AMBIENT = 25
  • sp = None
  • mass = None
Initial condition
y(0) = [0, 0, 5, 0, 0, 0, …] [shape=(14,), min=0, max=25]
Horizon
t ∈ [0, 60]

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: none

Recommendation snapshot

Clean best: SciPy DOP853

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 / power budget (battery-thermal-power-budget)

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
1SciPy DOP853SciPy
100%
15.4501 ms0.985
2SolvSRK
100%
15.03979 ms0.976
3Tsit5external
100%
10.830767 ms0.876
4SciPy RadauSciPy
100%
10.8512 ms0.875
5SciPy RK45SciPy
100%
10.326<1 ms0.864
6SciPy LSODASciPy
100%
8.513<1 ms0.822
7SciPy BDFSciPy
100%
8.4432 ms0.820
8CVODE Adamsexternal
100%
8.32910 ms0.817
9CVODE BDFexternal
100%
8.23112 ms0.814
10SciPy RK23SciPy
100%
8.017<1 ms0.809

At Clean, best balanced arm is SciPy DOP853 · SolvSRK survival 100%, SCD 15.0.

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 →

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SolvSRK · 30-day trial

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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_power_budget_2026,
  title        = {Resonix Evidence Portal: Battery / thermal / power budget},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/battery-thermal-power-budget}},
  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