nBMS, a Neuromorphic Battery Management System with a Silicon-Validated Spiking State-of-Charge Core for eVTOL Aircraft

Accurate state of charge estimation is critical for the success of electric vehicle battery management strategies, but it is well known that conventional estimators suffer from two fundamental shortcomings: cumulative errors that grow over time and reliance on simplified battery models that do not reflect real world dynamics. Therefore, this paper presents a novel hybrid approach combining Tucker tensor decomposition with LSTM networks, using full - lifecycle EV field data for SOC prediction. The inputs are charge status, mileage, voltage, current, cell differentials, and temporal features. Tucker decomposition is skillfully used to reduce dimensionality while maintaining the temporal structure, hence allowing a direct, fair comparison with standard LSTM. The result is unequivocal: Tucker - LSTM outperforms the baseline on all metrics, with MSE dropping 70.5% (from 21.07 to 6.22 ), MAE improving 48.7% (from 3.37% to 1.73%), RMSE falling from 4.59% to 2.49%, and $R^2$ rising from 0.918 to 0.976. Since the experimental results demonstrably demonstrate that tensor decomposition compresses high-dimensional battery data very well without loss of predictive fidelity, this paper naturally opens up a new direction for tensor-based analytics in electric vehicle battery management.

Flapping-Wing Micro Aerial Vehicles (FWMAVs) provide exceptional maneuverability and aerodynamic efficiency but pose significant challenges for onboard control due to nonlinear dynamics and stringent Size, Weight, and Power (SWaP) constraints, as exemplified by a butterfly-inspired robot less than 30 gram. To this end, we present a hierarchical neuromorphic control framework that enables fully onboard, closed-loop flight on a widely available, resource-constrained ESP32 microcontroller with a unit cost of approximately $5. Specifically, our method deploys two lightweight Spiking Neural Networks (SNNs) onboard: one for state estimation from raw sensory feedback and another for control via modulation of a Central Pattern Generator (CPG) for wing actuation. Trained by imitation learning, the system achieves stable pitch and heading angle tracking during untethered real-world flight. Experimental results further reveal that the SNN-based controller reduces latency by 36% (1059us to 680us) and power by 18% (0.033W to 0.027W) for inference compared to the conventional Artificial Neural Network (ANN) baseline, demonstrating the viability of spike-based computation without specialized hardware. To the best of our knowledge, this work constitutes the first demonstration of fully onboard neuromorphic control for autonomous flight of a FWMAV, highlighting the potential of SNNs to enable energy-efficient autonomy under stringent SWaP constraints. Visual abstract: http://bit.ly/4nI8ECY Code: https://anonymous.4open.science/r/Espikify-76E3/

Satellite onboard computing faces an irreducible tension: mission intelligence requirements grow monotonically while power, mass, and radiation budgets remain fixed. This paper advances the state of the art on three fronts. First, we develop a formal mathematical model of spiking neural computation for satellite workloads, deriving closed-form expressions for energy consumption as a function of network sparsity, firing rate, and synaptic fan-out. Second, we propose three original algorithms -- a Spiking Kalman-Complementary Filter (SKCF) for attitude estimation, a Temporal Contrast Anomaly Detector (TCAD) for spacecraft health monitoring, and a Spike-Based Adaptive Modulation and Coding (SAMC) scheme for cognitive satellite links -- each with pseudocode and analytical complexity bounds. Third, we introduce a quantitative Energy-Accuracy-Latency (EAL) trade-off surface and populate it with numerical estimates derived from published silicon measurements of Loihi and SpiNNaker, enabling direct comparison with FPGA and GPU baselines. A taxonomy of four radiation-aware neuromorphic architecture classes is refined with fault-rate models. Continual on-orbit learning is formalized through a constrained Spike-Timing Dependent Plasticity (STDP) rule that provably bounds weight drift. Case studies for a 12U CubeSat Earth observation mission, a GEO communications payload, and an autonomous deep-space probe provide concrete, quantified design points. Our analysis shows that neuromorphic implementations can achieve 8 to 47 times energy reduction over GPU baselines while maintaining inference accuracy within 2.1% for representative satellite workloads.