Harnessing AI to Revolutionize Automotive Battery Management Systems in Electric Vehicles

The Role of AI in Automotive Battery-Management Systems
March 3, 2025
Dr. Veronika Wright, a globally recognized consultant in automotive electrification, offers insights into automotive battery-management systems (BMS) and how emerging AI technologies can enhance state of health, state of charge, and range prediction in electric vehicles (EVs).

Key Takeaways

• Challenges of Traditional EV BMS Architectures: Understanding the limitations of existing systems.
• Types of AI in the Automotive Sector: Exploring the various applications of AI.
• AI’s Role in Optimizing Battery Metrics: How AI improves state of charge, state of health, and range prediction.
• Real-World Applications: Examples from Electra Vehicles and Texas Instruments.

In the realm of electric vehicles, where concerns about range anxiety and battery longevity prevail, a quiet revolution is happening: Artificial intelligence (AI) is becoming central to EV technology, particularly in battery-management systems (BMS). The pressing question is: How is AI applied to automotive batteries, and can it facilitate accurate EV range predictions throughout the vehicle’s lifespan?

EV Batteries: An Ideal Environment for AI

EV batteries are perfect for AI due to their complexity and the extensive datasets they generate. A lithium-ion battery pack consists of hundreds or thousands of individual electrochemical cells, each reacting sensitively to various factors. This sensitivity pertains not only to manufacturing defects but also to real-world conditions such as electrical fluctuations during charging, environmental influences, and mechanical vibrations.

As batteries charge and discharge, they physically change, leading to volume fluctuations of up to 15% in certain all-solid-state battery types. These changes, influenced by both manufacturing and operational conditions, affect battery capacity (range) and power over time, which are critical for consumers but often poorly understood by the industry.

At a microscopic level, these complexities stem from lithium-ion intercalation processes in the battery cells, the formation of solid-electrolyte interphase (SEI) layers, and ionic diffusion pathways. In commercial EVs, the only measurable signals usually include pack current, cell voltages, and a few temperature sensors. Bridging the gap to understand all relevant phenomena requires tackling the challenge through two approaches: physics-based electrochemical modeling at the sub-cell level and big-data-driven machine-learning algorithms at the vehicle fleet level. This raises the question: Is there untapped potential for AI?

The Importance of an Automotive BMS

If battery chemistry is the heart of an EV, then the BMS serves as its brain. The BMS manages the behavior of individual battery cells, their interaction with the drivetrain, and their response to charging systems. Given the diversity of EV battery designs, cell formats, and chemistries, a one-size-fits-all BMS is impractical. Instead, each BMS is customized for its specific battery architecture, incorporating hardware and software components like battery-monitoring units and thermal management systems.

The primary functions of an automotive BMS include:

• Safety Monitoring: The BMS continuously tracks battery temperature, voltage, and current to prevent hazardous conditions such as overcharging or overheating.
• State Estimation: Accurately determining the battery’s state of charge (SOC) and state of health (SOH) is vital, especially for assessing resale value, as approximately 30% of an EV’s net cost is tied to its battery.
• Cell Balancing: To prolong battery life and performance, the BMS ensures uniform charging and discharging of all cells within the pack.
• Thermal Management: The BMS controls cooling systems to maintain an optimal battery temperature, which is crucial for performance and longevity.
• Data Communication: The BMS interfaces with other vehicle systems, providing critical battery information to both the driver and control units.

A particularly promising area for AI is in state estimation. The range of an EV, which is often emphasized, is not a directly measured value but rather an estimation. Traditionally, state estimation has relied on predefined algorithms and lookup tables developed through extensive lab testing. However, with the variability introduced by real-world driving conditions and battery aging, is this traditional approach sufficient?

Limitations of Traditional BMS Approaches

Designing a BMS is a resource-intensive endeavor closely linked to the unique architecture of each battery pack. The calibration of the BMS is often the most costly and time-consuming aspect, requiring extensive lab testing of the battery.

Cells, modules, and packs undergo rigorous testing under various conditions to map their electrochemical behavior. This data forms the basis for battery models and static lookup tables that underpin traditional BMS algorithms. These tables serve as pre-computed datasets linking parameters, such as open circuit voltage (OCV) to actual SOC across different temperatures.

To utilize these tables in vehicles, traditional BMS algorithms combine parametrized battery models with techniques like Kalman filters. These filters dynamically incorporate data on voltage, current, and temperature to estimate SOC and SOH. However, they rely on precise cell voltage measurements, especially in batteries like lithium-iron-phosphate (LFP) that have flatter voltage profiles. This makes small measurement inaccuracies lead to significant prediction errors.

These methods are limited by static models and are susceptible to cumulative errors over time, particularly as batteries age or operate under unusual conditions. This is where AI offers dynamic, data-driven solutions.

AI’s Potential in EV BMS Systems

The limitations of traditional BMS approaches underscore the need for smarter, more adaptive systems. AI presents opportunities in three key areas:

1. Enhancing SOC and SOH Estimation Accuracy: AI can leverage real-world battery data through machine learning, enabling BMS systems to adapt in real-time. Neural networks can be embedded into vehicle chips, continuously learning from how batteries are used under specific conditions. This allows for recalibration steps that transform BMS into intelligent systems capable of self-learning. Electra Vehicles exemplifies this by using neural network algorithms to analyze data from various sources, achieving remarkable precision with under 1% error in SOC estimation and less than 3% in SOH.

2. Improving Safety and Predictive Maintenance: AI can identify patterns in temperature, voltage, and current variations to detect potential safety issues or maintenance needs. This capability is particularly valuable for commercial fleet operators, allowing them to predict battery usage cycles before maintenance is necessary.

3. Reducing Cell Testing and BMS Development Time: While AI cannot yet entirely eliminate the need for battery lab testing, it can significantly decrease the number of required tests. Collaborations within the ecosystem can lead to substantial reductions in testing needs, as demonstrated by Monolith, which has shown how AI can reduce aging tests by 40% and cell repetitions by 75%.

Conclusion: The Future of AI in EV Battery Development and Management

Artificial intelligence is revolutionizing how electric vehicle batteries are managed, enhancing accuracy, safety, and efficiency throughout a battery’s lifecycle. Despite lingering skepticism about AI’s capabilities, particularly regarding long-term predictive maintenance, its transformative potential is undeniable. As the industry continues to evolve, the integration of AI into BMS will play a pivotal role in shaping the future of electric mobility.