Embedded software in Battery Management Systems (BMS) plays a critical role in ensuring the safety, efficiency, and longevity of battery packs. The integration of machine learning (ML) algorithms into BMS software has opened new possibilities for advanced functionalities such as predictive state of charge (SOC) and state of health (SOH) estimation, anomaly detection, and adaptive cell balancing. These capabilities enhance battery performance, reduce degradation, and improve reliability. However, deploying ML on resource-constrained BMS hardware presents challenges, including computational limitations, real-time inference requirements, and model training on edge devices. This article explores these applications, challenges, and solutions, with a focus on ML frameworks optimized for embedded BMS environments.

Predictive SOC and SOH Estimation
Accurate SOC and SOH estimation is essential for optimizing battery performance and preventing overcharge or deep discharge. Traditional methods, such as coulomb counting and equivalent circuit models, suffer from drift and inaccuracies under varying conditions. Machine learning offers a data-driven alternative by leveraging historical and real-time battery data to improve estimation accuracy.

Supervised learning models, such as neural networks and support vector machines, can predict SOC by training on voltage, current, temperature, and impedance data. Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are particularly effective for capturing temporal dependencies in battery behavior. For SOH estimation, ML models analyze capacity fade and impedance rise over cycles, enabling early detection of degradation trends.

A key advantage of ML-based SOC/SOH estimation is adaptability to different battery chemistries and aging conditions. However, these models require substantial training data and careful feature selection to avoid overfitting. Edge deployment further complicates the process due to limited memory and processing power.

Anomaly Detection for Fault Prevention
Battery failures, such as internal short circuits or thermal runaway, can have catastrophic consequences. ML algorithms enhance fault detection by identifying subtle deviations from normal operating conditions that traditional threshold-based methods might miss. Unsupervised learning techniques, such as autoencoders and clustering algorithms, are particularly useful for anomaly detection because they do not require labeled fault data.

Autoencoders learn a compressed representation of normal battery behavior and flag deviations as anomalies. Similarly, one-class support vector machines (OC-SVMs) can detect outliers in operational data. These models must be lightweight to run in real-time on BMS hardware while maintaining high sensitivity to early warning signs.

Adaptive Cell Balancing
Cell imbalances in battery packs reduce capacity and accelerate degradation. Passive balancing dissipates excess energy as heat, while active balancing redistributes energy among cells. ML improves balancing strategies by predicting imbalance trends and optimizing balancing actions. Reinforcement learning (RL) algorithms can adaptively adjust balancing parameters based on real-time cell conditions, minimizing energy loss and extending pack life.

Challenges in ML Deployment for BMS
Computational Constraints
BMS hardware typically operates with limited processing power, memory, and energy budgets. High-complexity ML models may exceed these constraints, leading to latency or excessive power consumption. Optimizing models through quantization, pruning, and knowledge distillation reduces their footprint without significant accuracy loss.

Real-Time Inference
BMS applications require low-latency inference to respond swiftly to changing conditions. ML models must be optimized for deterministic execution times to meet real-time deadlines. Techniques such as fixed-point arithmetic and hardware acceleration (e.g., using DSPs or MCU-optimized libraries) improve inference speed.

Model Training on Edge Devices
Training ML models on edge devices is challenging due to limited resources. Federated learning allows decentralized training across multiple BMS units while preserving data privacy. TinyML frameworks enable on-device training with minimal memory usage, though they often sacrifice model complexity.

ML Frameworks for BMS
Several ML frameworks are optimized for embedded deployment in BMS:

TensorFlow Lite: A lightweight version of TensorFlow designed for microcontrollers and edge devices. It supports model quantization and hardware acceleration for efficient inference.

Edge Impulse: Provides tools for developing and deploying ML models on embedded systems, including feature extraction and model optimization tailored for sensor data.

STM32Cube.AI: Converts pre-trained ML models into optimized code for STM32 microcontrollers commonly used in BMS applications.

ARM CMSIS-NN: A collection of efficient neural network kernels for ARM Cortex-M processors, enabling high-performance inference on low-power devices.

These frameworks facilitate the deployment of ML models on BMS hardware while addressing computational and latency constraints.

Conclusion
Integrating machine learning into BMS embedded software enhances battery management through accurate SOC/SOH estimation, proactive anomaly detection, and adaptive cell balancing. However, deploying ML on resource-constrained hardware requires careful optimization of models and frameworks to meet real-time and computational demands. Advances in TinyML and edge AI are enabling more sophisticated BMS functionalities, paving the way for smarter and more reliable energy storage systems. The continued evolution of ML tools tailored for embedded systems will further expand the capabilities of BMS in industrial, automotive, and grid-scale applications.