Automotive
Melexis has the solution for EV battery current sensing
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elexis has announced the MLX91230 and the MLX91231 for the automotive current sensing market. They offer access to
shunt or Hall type current sensor technologies. These ASIL compliant devices can measure voltage, current and temperature. According to the company, customers will be provided with flexibility and design reusability in their electric vehicle (EV) power systems. The MLX91230, which is a conventional Hall sensor, is complemented by the MLX91231 shunt interface current sensor IC. These devices target high-voltage (HV) battery functions. Such functions include current monitoring in battery management system (BMS), battery disconnect units (BDUs), also known as battery junction boxes (BJBs), and power relay assemblies (PRAs).
The MLX91230 exhibits below 1 per cent sensitivity drift and 100mA offset over temperature and operational lifespan. The MLX91231 has a sub-0.5 per cent sensitivity drift with a 50 mA offset. By having one product family offering both shunt and Hall based current sensing, tier1s can decide which combination is the best fit for each vehicle OEM. They can easily switch from one configuration to the other if requirements change. OEMs can integrate different combinations into their vehicle models. This reusability has the benefit of minimizing the engineering effort. Designed according to the ISO 26262 functional safety standard, both current sensor devices offer full support for ASIL integration. The MLX91230 is naturally isolated, while the MLX91231 needs external isolation. Each sensor has an integrated
temperature sensor as well as a dedicated safety rated overcurrent detection pin. They are housed in compact 8-pin SOIC packages. “The new MLX91230/MLX91231 current sensor family is meeting our customers’ demands for a versatile modular BMS development strategy. It allows them to achieve much more with less engineering resources,” stated Bruno Boury, product line
manager at Melexis.
Testing samples (B-sample) and evaluation kits are already available on demand. The MLX91230 Hall-based current sensor will be released in Q2 2023, with the MLX91231 shunt device following soon after.
www.melexis.com/current-sensor-hall- and-shunt
AI-powered cloud-connected battery management system for electric vehicles
XP Semiconductors has developed a solution to connect its high- voltage battery management system (HVBMS) through its S32G GoldBox vehicle networking
reference design to the cloud to leverage an artificial intelligence (AI) powered battery digital twin. By utilizing Electra Vehicles, Inc.’s EVE-Ai 360 Adaptive Controls technology, NXP unleashes the power of digital twin models in the cloud to better predict and control the physical BMS in real time, to improve battery performance, battery state of health of up to 12 per cent and enable multiple new applications, such as electric vehicle (EV) fleet management. As batteries continue to be the costliest element in an EV, AI-powered digital twin cloud services have a high potential to improve estimations of the battery’s state of health (SOH) and state of charge (SOC) for improved efficiency, lifetime and cost. Battery digital twins adapt to ongoing changes in battery health due to operating conditions and provide updated figures back to the BMS for continuously improving control decisions. Carmakers can use the technology to provide driver insights, such as range and
20 November 2022
speed recommendations. In addition, adaptive battery control can improve the battery’s performance and safely extend its lifespan, reducing warranty costs for the carmaker. Another potential application is EV fleet management, providing fleet operators with invaluable usage insights, such as vehicle charging times and battery predictive diagnostics. Battery care centres can also use this in-depth information to reduce downtimes with rapid diagnostics, and EV charging station operators can effectively optimize their charging service and energy efficiency.
As the EV market grows, so will the supply of second-life batteries. Although they may have reached the end of their ‘automotive life’, these batteries have a significant residual capacity of up to 80 per cent. Tapping into that remaining useful battery life (RUL) in energy storage systems (ESS) for homes has the potential to reduce homeowners’ energy bills.
The AI-powered battery digital twin solution with Electra Vehicles, Inc. is said to offer a performance advantage thanks to highly accurate sensors, real-time closed-loop
Components in Electronics
control of the BMS and network connectivity combined with predictive algorithms. The solution comprises three elements. NXP’s S32K3-based HVBMS Reference Design offers high precision and accuracy to extend battery life safely. Having precise measurements of the battery’s SOH and SOC leverages the full potential of the battery and thus maximizes driving range with accurate diagnostics. The second element is NXP’s S32G-based vehicle networking processing solutions. NXP GoldBox offers safe high-performance computing capacity and real-time network performance with secure cloud connectivity for data-driven cloud-based automotive services. NXP is collaborating with Electra Vehicles, Inc., a leader in AI-based onboard battery controls, data analytics and design, for the third and final element of the digital twin solution, which is to implement its EVE-A adaptive digital twin and connect its electrification solutions seamlessly to the cloud.
“NXP’s contribution to the digital twin technology lies in the access to accurate sensor
data, real-time closed-loop control of the BMS, high-performance in-vehicle processing and secure connectivity to the cloud for services and over-the-air (OTA) updates. By integrating Electra’s EVE-Ai architecture, we address the two main challenges associated with the digital twin approach,” said Dr. Andreas Schlapka, director & segment manager battery management systems, NXP Semiconductors. “These are coping with the abundance of data from our electrification solutions, which requires cleansing and appropriate feature selection, and the variance of use cases, which requires model selection and adaptive training.”
www.nxp.com
www.cieonline.co.uk
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