In the era of electrification and digitalization, the health of batteries has emerged as a critical factor influencing the performance and longevity of various applications, particularly electric vehicles (EVs) and industrial batteries. Battery health, or the state of health (SoH), is a measure that indicates the level of degradation and remaining capacity of a battery, typically represented as a percentage of its initial capacity.
As the world increasingly relies on these power sources, the ability to monitor and enhance battery health has become increasingly important. This article aims to explore the application of machine learning in battery health management, focusing on how it can aid in the charging and depletion of electric vehicles and other industrial batteries.1 We will explore the challenges of implementing such technologies and how Edge Impulse, a leading platform for embedded machine learning, can address these issues.
The impact of battery health on the operational effectiveness and lifespan of industrial batteries and electric vehicles emphasizes its importance. The number of cycles, temperature, and depth of discharge all impact how slowly batteries' storage capacity declines as they age and go through charge-discharge cycles. 1 This degradation can affect the performance of the devices they power, making battery health a crucial aspect of their maintenance and management.
Battery health, particularly in the context of electric vehicles and industrial batteries, refers to the capacity of a battery to store and deliver energy efficiently over its lifespan. It is a critical factor in the performance and longevity of electric vehicles and industrial applications that rely on battery power. Degradation in battery health can lead to reduced range in electric vehicles, frequent recharging, and battery replacement.2
Machine learning, a subset of artificial intelligence, can be applied to monitor and improve battery health. It involves using algorithms and statistical models to perform tasks without explicit instructions, relying on patterns and inference. In battery health, machine learning can be used to analyze data from the battery and predict its health and lifespan.3
The application of machine learning in battery health management is a rapidly evolving field. Machine learning algorithms can analyze vast amounts of data generated by batteries in real time, identifying patterns and making predictions that would be impossible for humans to do manually. This can lead to more accurate predictions of battery life, allowing for more efficient use of batteries and potentially extending their lifespan.
Furthermore, anomaly detection can help identify potential issues before they become serious problems. Identifying unusual patterns in battery data allows it to flag potential issues and address them proactively, preventing further damage and extending the battery's useful life.4
Predictive maintenance takes this further, using data to predict when a battery may fail or need servicing. 5 This allows maintenance to be scheduled at opportune times, minimizing downtime and potentially extending the battery's life.6 Integrating these machine learning concepts into battery health management represents a significant advancement in the field.
Implementing machine learning for battery health monitoring and improvement is challenging. These technical and practical challenges can significantly impact the effectiveness of machine-learning applications in this field.
One of the primary challenges is the accurate prediction of battery life. This is a critical aspect of the business case for electric vehicles, stationary energy storage, and emerging applications such as electric aircraft. Existing methods for battery life prediction are based on relatively small but well-designed lab datasets and controlled test conditions. However, incorporating field data is crucial to understanding how cells age in real-world situations. This comes with additional challenges because end-use applications have uncontrolled operating conditions, less accurate sensors, data collection and storage concerns, and infrequent access to validation checks.7
Another challenge is the variability in battery usage conditions. In a lab setting, the cycling pattern of batteries can be closely controlled, and regular reference performance tests can be performed to quantify health. However, field data from real-world applications exhibits irregular cycling patterns, varying operating conditions, and path-dependent degradation mechanisms, making reliable predictions difficult. This setting is extremely relevant for industrial needs, such as predicting the remaining useful life of a customer’s electric vehicle or compliance with warranty conditions for grid storage systems; prognostics using real-world data remains an open research challenge.
Moreover, implementing machine learning for battery health also faces challenges related to data requirements. The "curse of dimensionality" refers to the data needed to capture all combinations of operating conditions growing quickly with the number of investigated conditions. This is compounded by the relatively slow rate at which battery lifetime data can be acquired, taking several months or years of experiments for each change in chemistry, form factor, or manufacturing process.7
While machine learning is promising for improving battery health monitoring and prediction, significant challenges remain. Overcoming these challenges will require innovative approaches that effectively combine machine learning techniques with physical models and leverage lab and field data.
Edge Impulse is a leading platform in embedded machine learning, providing a comprehensive solution to the challenges faced in implementing machine learning for battery health monitoring and improvement. The platform is designed to handle real-world sensor data, making it particularly suited for battery health applications.
Edge Impulse addresses these challenges by providing a platform that optimizes models for any Edge device. This means that regardless of the specific hardware used in a battery system, machine learning models can be tailored to perform efficiently on that device. This flexibility is crucial in overcoming the technical challenges of implementing machine learning in diverse real-world scenarios.8
Furthermore, Edge Impulse provides tools for advanced data analysis that can help build high-quality sensor datasets and quickly detect data quality issues. This is particularly relevant in battery health, where accurate and high-quality data are crucial for effective monitoring and prediction.
Edge Impulse has been used in various industries and applications to leverage machine learning for improved operational efficiency. For instance, Oura, a health technology company, used Edge Impulse to boost sleep-scoring accuracy by 17%.9 While this example is not directly related to battery health, it demonstrates the potential of Edge Impulse's machine-learning solutions in real-world applications.
The predictive maintenance solutions offered by Edge Impulse could be used to predict when a battery might fail or need servicing, allowing for timely intervention and potentially extending the battery's life. Similarly, asset tracking and monitoring solutions could be used to monitor the health of batteries in real time, identifying potential issues before they become serious problems.
Edge Impulse offers a promising solution to the challenges of implementing machine learning for battery health. By providing a flexible platform that can handle real-world sensor data and optimize models for any edge device, Edge Impulse is paving the way for more effective and efficient battery health management.
Battery health is crucial for electric vehicles and industrial applications' performance and longevity. Machine learning offers a promising solution for battery health management, despite challenges in accurate life prediction, variable usage conditions, and data requirements. Edge Impulse, a leading platform in embedded machine learning, addresses these challenges by providing a flexible platform for real-world sensor data and model optimization. As these technologies continue to evolve, they hold significant potential for improving battery performance and lifespan, marking a substantial advancement in the field.
 Dunn J. Battery State of Health – What is It? Why is It Important? The Equation. 2022 [cited 2023 Jul 27]. Available from: https://blog.ucsusa.org/jessica-dunn/battery-state-of-health-what-is-it-why-is-it-important/
: Battery University. (2023). How to Prolong Lithium-based Batteries. Retrieved from https://batteryuniversity.com/learn/article/how_to_prolong_lithium_based_batteries
: Zhang, S., Zhang, C., Xiong, R., & He, H. (2020). Review the management of battery aging based on the digital twin driven by artificial intelligence. Journal of Energy Storage, 30, 101634. doi:10.1016/j.est.2020.101634
: Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58. doi:10.1145/1541880.1541882
: Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier.
: Sinha Roy, A. (2023). Predictive Maintenance: A New Way Ahead for Battery Management. Retrieved from https://www.spiceworks.com/tech/hardware/guest-article/battery-management-electric-vehicles/
 Samad NA, Kim Y, Siegel JB. The challenge and opportunity of battery lifetime prediction from field data. Joule. 2021;5(8):1-32. doi:10.1016/j.joule.2021.06.005
: Edge Impulse. (2023). Optimize AI for the edge. Retrieved from https://www.edgeimpulse.com/
: Edge Impulse. (2023). Learn how Edge Impulse helped Oura to boost sleep-scoring accuracy by 17%. Retrieved from https://assets-global.website-files.com/618cdeef45d18e4ef2fd85f3/628795dc61c12d55b0f4e769_Go-Deeper-On-Deep-Sleep-Oura.pdf