from chemistry to pack
Current Sensor Drift and SOC Estimation Error Propagation This is a worked example accompanying Failure Mode and Effects Analysis (FMEA) in Battery Design. It is the System FMEA example in the set, and deliberately chosen because it shows something the cell-level and pack-level examples cannot: a failure that originates in one subsystem (a sensor) and … Read more
Virtual temperature sensors (also referred to as “soft sensors” or “sensorless estimators”) are software-based algorithms that estimate the temperature of battery cells without relying on a direct, physical hardware sensor at the measurement location [2]. Instead of measuring heat directly, these virtual sensors act as real-time state reconstruction algorithms running within the Battery Management System … Read more
The exact number of temperature sensors you need depends on the size of your battery pack, its physical configuration, rate of charge/discharge (C-rate), and the required level of thermal observability. In commercial Battery Management Systems (BMS), it is common practice to use one temperature sensor for every 4 to 20 cells, depending on the specific … Read more
The selection of a physical sensor for a battery pack involves balancing accuracy, cost, response time, and robustness to electromagnetic interference (EMI). While dozens of sensors may be embedded in a single automotive battery pack, the methodology of their placement and the technology chosen fundamentally limit the capability of the BMS. Temperature gradients exist across … Read more
Author: Krishna S Hanamaraddi Developing a generalized AI model for State of Health (SOH) prediction is a “boss level” challenge in battery management. While standard Neural Networks (LSTMs/GRUs) are excellent at capturing temporal patterns for a specific batch, they often fail to generalize across diverse chemistries (LFP, NMC, NCA), variable C-rates, and fluctuating temperatures. They … Read more
Engineering Advances and Technical Insights By Dr Ugur Yavas, Head of AI @Eatron Technologies “AI-powered BMS” is a term we hear more and more frequently. In the broader AI world, we see the landscape evolving through distinct phases – most recently captured by Andrej Karpathy as Software 1.0 (hand-coded logic), Software 2.0 (deep learning), and … Read more
AION Engineering INC In lithium-ion battery technology, optimizing cell performance and safety requires careful consideration of multiple parameters. Among these, the cut-off voltage is critical for balancing energy density and ensuring cell longevity and stability. While cathode chemistry often guides cell classification, variations in anode materials significantly impact the appropriate cut-off voltage setting. Influence of … Read more
What would we expect to see on a battery analytics dashboard? This depends to some extend on the application of the battery and the data access level. Here it would be good to put all of this to one side and think about the ideal set of data and data rates. When thinking about the … Read more
Whilst electric vehicles and renewable energy storage systems gain traction in the realm of battery system development, ensuring functional safety is paramount. Development Process Assessment Models The Software Process Improvement and Capability dEtermination (SPICE) model offers a structured approach to software development that aligns seamlessly with the safety-critical nature of battery systems. SPICE, formally known … Read more
Enhancing Lithium-Ion Battery Management with Advanced Kalman Filter Tuning summarises the work conducted by Jasper Knox, Mark Blyth and Alastair Hales (all University of Bristol). Lithium-ion batteries are integral to modern electric vehicle development, requiring advanced battery management systems (BMS) for effective battery pack operation. A critical task for these systems is accurately estimating state … Read more