Machine learning (ML) is increasingly being integrated into battery management systems (BMS) to improve state of charge (SOC) and state of health (SOH) estimation accuracy. However, BMS hardware often operates under strict resource constraints, including limited memory, processing power, and energy availability. Lightweight ML architectures, such as TinyML and quantized neural networks, provide a viable solution by enabling efficient inference on low-power microcontrollers. These approaches reduce computational overhead while maintaining performance, making them suitable for real-time onboard processing without relying on cloud connectivity.

Traditional BMS implementations frequently depend on lookup tables (LUTs) and equivalent circuit models (ECMs) for SOC and SOH estimation. While these methods are computationally inexpensive, they lack adaptability to dynamic operating conditions and battery aging effects. ML-based approaches, in contrast, can capture complex nonlinear relationships in battery behavior but often require significant computational resources. Lightweight ML bridges this gap by applying model compression and optimization techniques to deploy compact yet accurate models on embedded BMS chips.

**Model Compression Techniques for BMS Deployment**
Three primary methods are used to reduce ML model size and computational demands: pruning, quantization, and knowledge distillation.

Pruning removes redundant neurons or weights from a neural network, significantly reducing its size without substantial accuracy loss. Structured pruning eliminates entire channels or layers, while unstructured pruning targets individual weights. Research indicates that pruning can reduce model parameters by up to 90% while retaining acceptable prediction accuracy for SOC estimation.

Quantization converts high-precision floating-point weights and activations into lower-bit integer representations, decreasing memory usage and accelerating inference. For example, 8-bit quantization reduces memory footprint by 75% compared to 32-bit floating-point models while maintaining near-equivalent performance. Binary and ternary quantization push this further but may require architectural adjustments to mitigate accuracy degradation.

Knowledge distillation trains a smaller student model to replicate the behavior of a larger teacher model. The student model benefits from the teacher’s learned representations, achieving comparable accuracy with fewer parameters. Studies show distilled models for SOC estimation can achieve over 95% of the teacher model’s performance at a fraction of the computational cost.

**Latency and Energy Efficiency Benchmarks**
Deploying ML models on BMS hardware requires careful consideration of inference latency and energy consumption. Benchmarking experiments on popular microcontroller units (MCUs) demonstrate the feasibility of lightweight ML for real-time applications.

For instance, a pruned and quantized convolutional neural network (CNN) for SOC estimation running on an ARM Cortex-M4 processor exhibits inference times below 10 milliseconds, meeting real-time requirements for most BMS applications. Energy consumption measurements reveal that such models consume less than 1 millijoule per inference, making them suitable for battery-powered systems.

Comparatively, traditional ECM-based SOC estimation typically requires less than 1 millisecond per calculation but suffers from reduced accuracy under variable load conditions. Lightweight ML models strike a balance, offering improved accuracy with only a modest increase in computational overhead.

**Over-the-Air (OTA) Update Mechanisms**
Maintaining model accuracy over a battery’s lifespan necessitates periodic updates to account for aging and usage patterns. OTA updates enable remote deployment of improved ML models without physical access to the BMS.

Delta updates, which transmit only the differences between the existing and new model parameters, minimize bandwidth usage. Compression algorithms further reduce transmission size, ensuring compatibility with low-power wireless protocols like Bluetooth Low Energy (BLE) or Narrowband IoT (NB-IoT). Secure boot and cryptographic verification mechanisms prevent unauthorized modifications, preserving system integrity.

**Applications in SOC and SOH Estimation**
Lightweight ML models excel in onboard SOC and SOH estimation by leveraging real-time sensor data, including voltage, current, and temperature. Unlike LUT-based methods, which rely on predefined mappings, ML models dynamically adjust to battery degradation and environmental variations.

Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are commonly used for sequential data processing in SOC estimation. Optimized versions of these architectures, such as depthwise separable convolutions or grouped LSTMs, reduce computational complexity while preserving temporal modeling capabilities.

For SOH estimation, ensemble methods combining multiple lightweight models improve robustness. Gradient boosting machines (GBMs) with fixed-depth decision trees provide high interpretability and low inference latency, making them suitable for embedded deployment.

**Contrast with Traditional Lookup-Table Approaches**
LUT-based SOC estimation relies on precomputed mappings between voltage, current, and SOC under specific conditions. While fast, these methods struggle with aging effects, temperature variations, and irregular discharge patterns. Linear interpolation between table entries introduces errors, particularly in nonlinear regions of battery discharge curves.

ML models inherently generalize across diverse operating conditions, reducing the need for exhaustive calibration. Adaptive algorithms, such as online learning techniques, further enhance accuracy by continuously refining predictions based on incoming data. However, ML approaches require careful validation to prevent overfitting and ensure reliability across battery chemistries.

**Challenges and Future Directions**
Despite advancements, challenges remain in deploying lightweight ML on BMS hardware. Model robustness under noisy sensor data, memory constraints for over-the-air updates, and certification for safety-critical applications require further research. Hybrid approaches combining ML with physics-based models may offer a compromise between accuracy and computational efficiency.

Future developments in neural architecture search (NAS) could automate the design of optimal tinyML models for specific BMS applications. Edge computing frameworks that support federated learning may enable collaborative model improvement across fleets of batteries without centralized data collection.

In summary, lightweight ML architectures enable accurate, adaptive SOC and SOH estimation on resource-constrained BMS hardware. Through model compression, efficient deployment, and OTA updates, these methods outperform traditional LUT-based approaches while maintaining feasibility for embedded systems. Continued advancements in tinyML and edge AI will further enhance the capabilities of next-generation BMS solutions.