Browse Topic: Battery management systems (BMS)
Current lithium-ion batteries should generally only be charged above 0 °C, as charging below this temperature can promote lithium plating and irreversible degradation. However, conventional pack-level heating elements increase system mass and design complexity. In addition, heat is transferred from outside into the cell, causing the temperature inside the cell to rise slowly. This study evaluates internal Joule heating of cylindrical Li-ion cells using a zero-mean square-wave current excitation and quantifies the associated aging impact. LG INR21700-M50L cells were tested at 0 °C, −10 °C, and −20 °C with three excitation frequencies (50 Hz, 1 Hz, 10 mHz) at 5 A amplitude. Each cycle consisted of 30 min heating followed by 60 min cooling; reference capacity-based state of health (SOH) was assessed every 50 cycles up to 400 cycles. A maximum surface temperature rise of 14.3 K was achieved, with larger temperature rise at lower ambient temperature and lower excitation frequency. Capacity fade remained below approximately 1% for most conditions; however, at −20 °C and 10 mHz a pronounced SOH decrease to 87% was observed, indicating a critical operating regime. The results provide practical guidance for pulse-heating parameter selection and highlight the need for safeguards and further diagnostics in extreme low-frequency excitation at very low temperatures. This heating approach is particularly suitable for simpler battery-electric applications without thermal management, such as e-bikes or power tools. However, it may also be relevant for applications with existing thermal management systems, as it simplifies battery pack design.
From material selection to system-level performance Transportation's shift toward electric power - whether cars, planes, or big trucks - has made battery engineering a pretty wild, multidisciplinary puzzle. It's not just about coming up with a prototype anymore. To hit the right mix of energy density, power, safety, cost, and longevity, teams need to rethink how they design these systems. Enter simulation and modeling tools. Engineers now use these digital tools to blend electrochemistry, thermal management, materials science, and whole-system design. Instead of jumping straight to building, they try out battery ideas in the virtual world first, speeding up how long it takes to figure out what works and what doesn't and boosting the reliability of those batteries in the real world. Today, battery simulation spans multiple scales from the behavior of active materials within electrodes to the thermal dynamics of the entire battery pack as integrated into a vehicle.
Improving the energy efficiency of electrified vehicles remains a central objective in modern electric powertrains. Multi-level converters (MLCs) are widely recognised for lowering conversion losses relative to two-level inverters and improving total harmonic distortion (THD) in the sinusoidal supply to motors with a consequent reduction in motor losses. Despite this, sustained production-oriented validation at the integrated system level remains limited. This work introduces a multi-level converter architecture of the Battery Integrated Modular Multi-Level Converter (BIMMC) topology using Cascaded H-Bridge (CHB) architecture. It offers improvements in all key metrics of performance, cost, package size, mass and robustness compared to the current state-of-the-art two-level inverter system with distributed functions for charging available in the market today. The overall solution is highly functionally integrated. It supports four major functions required in electric vehicles without the need for additional hardware. Firstly, supply to and control of a three-phase electric motor without the need for a separate, standalone inverter. Secondly, all usual battery management system (BMS) functionality including energy and State of Charge (SOC) management to module level enabling usable energy and robustness improvements. Thirdly, the ability to charge from both alternating current (AC) (single phase and three-phase) and direct current (DC) sources without the need for separate on-board charger (OBC) hardware whilst also enabling an innovative pulse charging approach which benefits both charging time and battery ageing compared to conventional DC charging. Finally, the ability to deliver a controlled DC supply to non-traction loads on the vehicle with high efficiency and redundancy. The BIMMC topology proposed has been designed, built at prototype level and tested in order to collect performance data to empirically validate empirical study of the performance and functional benefits of the approach for traction motor drive, battery stored energy management and charging. Measured results demonstrate that improved inverter waveform quality correlates with lower motor harmonic losses and measurable drive-cycle efficiency gains, consistent with prior MLC assessments. Battery SOC depletion can be managed actively within the complete battery yielding increased usable energy and further driving range gains. Pulse charging shortens charge time whilst maintaining battery health metrics within acceptable limits, aligning with experimental evidence on pulse-based fast charging. The topology has also demonstrated the ability to pulse charge cells in a complete battery pack whilst consuming incoming DC supply current from a standard commercially available DC charger (Electric Vehicle Supply Equipment - EVSE). This potential to offer the benefits associated with pulse charging without requiring change to existing deployed charging infrastructure. Overall, proposed CHB-BIMMC architecture offers a practical blueprint for next-generation electric vehicles (EVs), and is compatible with ongoing integration trends that converge traction, charging and battery management functions within a unified power electronics and control platform.
As the utilization of lithium-ion batteries in electric vehicles expands, monitoring the usable cell capacity (UCC) is essential for ensuring accurate state-of-health (SOH) estimation. Battery performance degradation is influenced by temperature and constraints. Capacity tests in laboratory settings are typically conducted at low C-rates to approximate equilibrium conditions, whereas in real vehicle applications, charging currents are often much higher. This discrepancy in rates frequently results in deviations between laboratory characterization and on-board Battery Management Systems (BMS) capacity estimation. To investigate how C-rate of diagnostic Reference Performance Test (RPT) modulates aging effects under temperature and mechanical loading, we conducted long-term cycling tests on lithium iron phosphate/graphite pouch cells at 25°C and 45°C under different constrained conditions. The cycling protocol is a tiered multi-rate protocol. Cells were aged at Block1 under 1C, and UCC evolution was quantified after each block. The result shows battery aging can be divided into three stages: a decelerated, steady, and accelerated aging stage. The degradation of LFP cells is dominated by loss of lithium inventory (LLI), and elevated temperature accelerates the degradation. By combining differential voltage analysis (DVA), direct current internal resistance, electrochemical impedance spectroscopy, and ultrasonic testing, we found that under 45°C free condition, accelerated aging is consistent with intensified SEI growth and electrolyte decomposition, accompanied by increased LLI, gas-generation, and increased resistance. These signals emerge earlier than the apparent capacity divergence and may serve as early indicators for predicting the onset of rapid degradation. Appropriate constrain mitigates aging, and its influence becomes more pronounced when using higher-rate RPTs. At 25°C, high-rate RPTs exhibit an apparent capacity recovery. DVA analyses indicate the recovery originates from gradual activation of lithium. Overall, these findings illustrate and explain the degradation characteristics and capacity recovery phenomenon, providing a reference for connecting laboratory standard tests with on-board BMS capacity estimation.
Reliable monitoring of the internal state of lithium-ion batteries (LIBs) is crucial for mitigating potential safety hazards. The incorporation of a reference electrode (RE) within the battery constitutes a vital approach for achieving single-electrode monitoring and understanding changes in electrode state during cycling. Among these, the lithium-copper reference electrode (Li-Cu RE) is particularly cost-effective and straightforward to prepare, being fabricated by depositing lithium onto a copper wire. However, Li-Cu RE exhibits a relatively short effective lifespan during long-term cycling, thereby limiting its practical application. In this work, based on a self-fabricated three-electrode single-layer pouch cell, the microstructural changes before and after failure of the Li-Cu RE were characterized and analyzed, revealing its failure evolution process. Post-failure microstructures observations exhibit marked porosity in the electrode, attributed to substantial depletion of surface lithium metal. Concurrently, the copper wire's elevated potential dominantly influences the overall Li-Cu RE potential, causing its potential to rise and destabilize. This induces a sharp decline in the measured electrode's potential curve. Furthermore, comparative analysis of key factors influencing Li-Cu RE lifespan were investigated. In the static state, the theoretical failure time of Li-Cu RE differed by only approximately 9 hours from that in the cycling state. Crucially, isolating the test electrode from the Li-Cu RE nearly doubled its lifespan, revealing that current generated by the potential difference between the test electrode and Li-Cu RE is the primary cause of failure under low-rate cycling. This paper systematically elucidates the observed failure behavior of the Li-Cu RE and comprehensively analyzes the various factors, which aids in further understanding the failure mechanism of the Li-Cu RE and identifying targeted solutions.
Battery Thermal Management Systems (BTMS) play a critical role in ensuring the longevity, safety, and efficient operation of lithium-ion battery packs. These systems are designed to better dissipate the heat generated by the cells during vehicle operation, thereby maintaining a uniform temperature distribution across the battery modules, preventing overheating and mitigating the chances of thermal runaway. However, one of the primary challenges in BTMS design lies in achieving effective thermal contact between the battery cells and the cooling plate. Non-uniform or excessive application of Thermal Interface Materials (TIMs) without ensuring robustness and uniformity can increase interfacial thermal resistance, leading to significant temperature variations across the battery modules, which may trigger power limitations via the Battery Management System (BMS) and these thermal changes can cause inefficient cooling, ultimately affecting battery performance and lifespan. In this paper, a real-world testing was conducted on the battery pack with uneven TIM application and unoptimized distribution patterns, which resulted in significant temperature variations across the pack. In contrast, the application of uniformly optimized TIM thickness reduced these temperature differences by up to 70%, demonstrating the critical impact of consistent interface design on thermal performance. To validate and further understand these findings, combined conduction-convection heat transfer model was developed using ANSYS Fluent to simulate the thermal changes of the battery pack with different TIM thicknesses alongside the unoptimized distribution patterns. The results confirmed that uneven TIM distribution contributes significantly to thermal non-uniformity within the battery pack, whereas optimizing the thickness improves overall thermal performance. Additionally, the optimized application led to a significant reduction in weight of the thermal paste (TIMs) usage, resulting in cost savings and more efficient material utilization.
Over-the-Air (OTA) update technology has come forth as a transformative aider in the domain of automotive technology, allowing Original Equipment Manufacturers (OEMs) and Tier-1 suppliers of Electric vehicles (EVs) to frequently make software modifications, enhancements, and bug fixes that are essential to optimize the performance of powertrain components such as the motor controller unit (MCU), Battery Management System (BMS), and Vehicle Control Unit (VCU). This facilitates them to remotely supply updates to the vehicle firmware and software by giving inputs of calibration data without requiring physical access to the vehicle. However, as OTA updates have a direct impact on vehicle’s performance, safety and cybersecurity, a stringent validation methodology is of prime importance prior to deployment process. This paper explores the integration of Hardware-in-Loop (HIL) simulation into the OTA validation pipeline as a means to ensure reliability, safety, and functional correctness of updates before they are applied in the field. We present a structured approach of combining HIL systems with OTA workflows, wherein a virtual vehicle environment is simulated in real time to reproduce a replica of the actual operating conditions for the target ECU under test. The OTA update is injected through a simulated or physical OTA backend and transmitted to the control unit interfaced within the HIL loop. This setup facilitates the emulation of update scenarios including firmware re-flashing, configuration updates, security checks, and rollback mechanisms under controlled, observable conditions. Key advantages include the ability to inject faults, monitor system response, verify communication integrity, and perform automated regression tests without risking physical prototypes or production vehicles. This integration of OTA and HIL not only enhances pre-deployment validation but also lays the foundation for continuous development and in-field update strategies in connected EV platforms. The proposed framework can be scaled to multiple ECUs and integrated with CI/CD pipelines for automated nightly testing. Future work will explore combining this setup with Digital Twin environments and Machine Learning-based anomaly.
Engineers looking for a new way to simulate battery cells as they develop new battery management systems might be interested in the latest PXI battery simulator modules from Pickering Interfaces. The new single-slot simulators can be 2- or 4-channel and are capable of supplying up to 8 volts and 5 Amps per channel. and the ground (1000V isolation) and, as a result, series connections can simulate batteries in a stacked architecture. The company said the channels are fully isolated from each other (750V isolation channel to channel). The names of the new modules - 41-754 (PXI) and 43-754 (PXIe) - give away one of Pickering's attitudes when it comes to introducing new products: don't abandon the old stuff.
New approaches to make SoC and SoH parameters more accurate will be required as battery demand keeps growing in the coming years. As the demand for accurate, reliable, and intelligent battery management systems continues to grow, overcoming state of charge (SoC) and state of health (SoH) estimation errors becomes more relevant than ever. The battery performance topic is getting especially critical, as electric vehicles, renewable energy storage systems, and portable electronics are now commonplace. This growing demand puts additional pressure on battery performance while also reinforcing the need for accurate SoC and SoH parameters. However, precisely estimating SoC and SoH parameters remains challenging, as their accuracy depends on several factors. Among these are hardware malfunctions and data quality issues that stand in the way of accurate SoC and SoH estimation.
Modern battery management systems, as part of Battery Digital Twin, include cloud-based predictive analytics algorithms. These algorithms predicts critical parameters like Thermal runaway events, state of health (SOH), state of charge (SOC), remaining useful life (RUL), etc. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. This is a very critical and safety function and delay is not acceptable, but automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud and similarly delay in return of the detected warning to the driver back in the vehicle. As a result, the inherent lag in data transfer between the cloud and vehicles challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes application of Federated Learning and applying to a thermal runaway model in low-cost microcontroller as a strategy to reduce transmission and processing costs and delays. Furthermore, this will ensure safety assured, rapid and efficient client experience, and other long term, history and huge data based algorithms running in cloud, giving OEMs a competitive advantage in the digital technology arena.
The problem of monitoring the parametric failures of a traction electric drive unit consisting of an inverter, a traction machine and a gearbox when interacting with a battery management system has been solved. The strategy for solving the problem is considered for an electric drive with three-phase synchronous and induction machines. The drive power elements perform electromechanical energy conversion with additional losses. The losses are caused by deviations of the element parameters from the nominal values during operation. Monitoring gradual failures by additional losses is adopted as a key concept of on-board diagnostics. Deviation monitoring places increased demands on the information support and accuracy of mathematical models of power elements. We take into account that the first harmonics of currents and voltages of a three-phase circuit are the dominant energy source, higher harmonics of PWM appear as harmonic losses, and mechanical losses in the rotor and gearbox can be many times greater than electrical losses in high-speed modes of traction machines. The paper pays special attention to monitoring the three-phase circuit of a traction machine by a measuring observer consisting of six current and voltage sensors. Algorithms for data processing using the generalized energy flow technology are presented. They allow obtaining comprehensive information on the energy state of a three-phase circuit with a time delay of no more than one millisecond. The three-phase circuit monitoring data are taken as a basis for monitoring electrical and polarization losses of the battery, electrical and switching losses of the voltage inverter, electrical, magnetic, harmonic and mechanical losses of the motor and gearbox.
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