Breaking the shackles of traditional BMS, iBMS conducts beneficial exploration based on big data

For BMS technology, chip manufacturers face many challenges. As one of the core Electronic control components of new energy vehicles, BMS inevitably faces many challenges such as cost, quality, and delivery. On the core proposition of “better battery management”, there is a lot of mature traditional automotive electronics experience for improvement in cost, quality, and delivery. However, as a relatively young emerging application product, automotive power lithium battery There are still many new technical fields to be further researched and explored in terms of efficient cycle management, such as battery models, parameter calibration, management algorithms and strategies, etc. Chip manufacturers need to make further improvements in terms of reliability, acquisition accuracy and computing power, in order to ‘will Lithium batteries are better managed ‘provide a more solid physical foundation and resource guarantee.”

Peng Yongjun, founder and general manager of Anhui Youdan Technology Co., Ltd.

Key points for BMS to improve range and charging time

Compared with fuel vehicles, short cruising range and long charging time have always been the pain points of electric vehicles. In what ways can BMS improve the cruising range and charging time of electric vehicles? In response to this problem, Peng Yongjun analyzed the improvement of BMS on the cruising range of electric vehicles from three aspects:

First, improve the balance efficiency of the battery. Everyone knows that a battery system consists of many energy-saving cells and follows the barrel effect, that is, the performance of the battery system largely depends on the cell with the worst performance, improving the battery balancing efficiency and improving the consistency of the cells. It can effectively extend the cruising range of the vehicle;

Second, improve the energy recovery efficiency during braking or vehicle downhill. A good energy feedback control algorithm needs to give full play to the battery charging performance as much as possible under the premise of ensuring the safety of battery application, and obtain the most efficient current feedback power strategy through real-time calculation of SOC, temperature, instantaneous charging power, and continuous charging power;

Third, improve the estimation accuracy of SOC. This can also be understood as improving the cruising range from another dimension, that is, the more accurate the user’s grasp of the real power, the more confident they can use a wider range of SOC ranges.

Regarding how to shorten the charging time, he believes, “The charging time can be shortened by increasing the charging power while ensuring safety. The effective way to increase the charging power is a good thermal management strategy. According to the thermal model of the battery system, the temperature is affected. To make predictive adjustments to keep the battery at optimal temperature conditions throughout the charging process, thermal management strategies need to be continuously adjusted as the battery ages.”

Preventing battery abuse is the premise of ensuring safety

The safety of electric vehicles has always been the focus of the industry and the foundation for the healthy development of the industry. At present, most of the safety issues of electric vehicles are directly or indirectly related to the battery system. As the brain of the battery system, BMS is mainly to prevent the abuse of battery power. It can be protected from the following aspects:

First, temperature protection. BMS has a clear operating temperature threshold setting, and there are the highest and lowest temperature limits for charging and discharging. If the set limit is exceeded, the system must not be turned on or the power must be reduced to run;

Second, voltage protection. For the risk of overcharge and overdischarge, the BMS is set with the highest and lowest charging and discharging voltage thresholds to ensure that the system automatically stops running when the thresholds are touched;

Third, current protection. Accurately estimate the battery state through high-precision battery models and algorithms, calculate safe and efficient available power, monitor the current of the battery, and prevent risks such as battery deterioration and dendrites caused by overcurrent.

Peng Yongjun introduced to Yufei.com reporters, “Highly reliable, high-precision signal sampling and software and hardware stability are the basis of battery system safety, and battery models and algorithms are the core of battery system safety. Udan Technology BMS monomer voltage acquisition error ± 1mV, temperature acquisition error ±1°C, current acquisition error ±0.5%, provide accurate acquisition of analog signals; strictly follow the automotive electronics development and verification process in the software and hardware development process, and apply the MBD development mode to ensure system stability; high-precision battery charging The discharge model, power pool model, etc. are implemented with highly robust algorithms to ensure accurate battery state estimation.”

Battery life has always been an important issue faced by electric vehicles. Almost all battery manufacturers are extending battery life through technology research and development, thereby reducing user costs. According to Peng Yongjun’s analysis, the factors and countermeasures affecting battery life are as follows: First, the chemical system largely determines the life of lithium batteries. Battery factories can improve battery life by improving battery chemistry; second, temperature affects battery life. During the charge-discharge cycle, the higher the temperature, the faster the battery life decays. In order to reduce the impact of temperature on life, the thermal field balance needs to be considered when batteries are grouped, and a reasonable BMS thermal management strategy should be formulated to make the battery work in a suitable temperature region to the greatest extent. Third, current affects battery life. Excessive charge and discharge current will lead to irreversible chemical and physical reactions inside the battery, resulting in irreversible attenuation of the battery capacity. For this part of the impact, power prediction can be performed by optimizing the SOP algorithm in the BMS to ensure that the charging and discharging power is within the acceptable range of the battery. Depth of discharge affects battery life. Shallow charging and shallow discharging will help prolong battery life. In view of the influence of the depth of discharge, the battery pack can be designed to appropriately reserve capacity to avoid deep discharge.

Breaking the shackles of traditional BMS, iBMS conducts beneficial exploration based on big data

With the increasing requirements for lean management of lithium batteries, traditional BMS is facing many challenges. Lithium battery is like a living body, and its state is affected by multiple influencing factors in the whole life cycle. It is difficult to have a “perfect” model that can characterize all its changing characteristics once and for all; in addition, BMS parameter calibration based on laboratory sample data , it cannot accurately match the whole process of battery application, and must be continuously corrected in the process; in addition, in the face of complex algorithms, the storage and computing capabilities of the SoC are limited, or the expansion cost is high. “There are no two identical leaves in the world”, but traditional BMS manages different lithium batteries whose status changes all the time in batches with the management algorithms, parameters and strategies set uniformly at the factory. The management efficiency can be imagined.

Udan Technology is the first in the industry to propose the concept of iBMS, an intelligent battery management system, advocate a cloud-based collaborative management strategy based on big data, and make useful explorations. The iBMS terminal module integrates a two-way wireless data transmission unit, which uploads the battery operating data to the cloud in real time while monitoring and managing the battery. Using the cloud server’s nearly unlimited storage capacity and computing power, it can target non-stationary changes and multi-dimensional massive battery data based on machine tools. The learning method builds a big data analysis model, continuously iteratively matches the management parameters of the latest state of the battery and distributes it wirelessly, thereby realizing more powerful cloud collaborative management, full life cycle management and personalized management of lithium batteries.

At present, Udan Technology has completed technological breakthroughs and realized productized cloud collaborative management strategies as follows:

First, joint estimation of cloud state. Achieve high noise immunity, low error, high robustness, fast convergence battery state of charge (SOC), state of energy (SOE), state of power (SOP), state of health (SOH), remaining useful life (RUL), etc. Combined cloud-based estimation of battery status improves battery application experience and prolongs battery life.

Second, the cloud is balanced all the time. In view of the overall performance decline and health deterioration caused by the inconsistency of large-capacity battery packs, the charging data curve of each cell is fitted based on the massive cloud data, and then the capacity difference between cells is calculated and the equilibrium time is accurately estimated. The server sends control commands to the terminal module for balanced execution, which improves the balance efficiency by more than 80% compared with the traditional BMS.

Third, cloud active security management. According to multi-source information such as path planning, ambient temperature, real-time road conditions, etc., the battery charging and discharging power management strategy and thermal management (heating or cooling) strategy are adjusted in real time at the edge of the cloud platform to ensure the safe and reliable operation of the power battery system; based on cloud data tracking The evolution trajectory of battery input and output characteristics and battery performance under different working conditions is used to establish a battery life cycle fault diagnosis mechanism; the cloud platform assists in judging the trend of high-voltage circuit contact resistance and analyzing the thermal field change of the battery system to determine the sub-health status of the battery. Provide timely warning and intervention.

Regarding the future integration and optimization space of BMS, Peng Yongjun believes, “Broadly speaking, BMS can be understood from the two dimensions of hardware and software. Hardware is usually composed of sensors, calculators, actuators and other components. With the improvement of hardware capabilities, especially chip capabilities , to provide the possibility for BMS to integrate with other electronic control components of the vehicle in the hardware category to achieve higher integration and better cost. For example, the concept of domain controller proposed by many OEMs is based on this kind of thinking. In the future, BMS may It will not exist as an independent hardware module. However, no matter how the hardware form evolves, the core essence of BMS in the software category will not change. The robust state management algorithm will always be the soul of realizing the safe, accurate and efficient management of batteries.”

For BMS technology, chip manufacturers face many challenges. As one of the core Electronic control components of new energy vehicles, BMS inevitably faces many challenges such as cost, quality, and delivery. On the core proposition of “better battery management”, there is a lot of mature traditional automotive electronics experience for improvement in cost, quality, and delivery. However, as a relatively young emerging application product, automotive power lithium battery There are still many new technical fields to be further researched and explored in terms of efficient cycle management, such as battery models, parameter calibration, management algorithms and strategies, etc. Chip manufacturers need to make further improvements in terms of reliability, acquisition accuracy and computing power, in order to ‘will Lithium batteries are better managed ‘provide a more solid physical foundation and resource guarantee.”

Peng Yongjun, founder and general manager of Anhui Youdan Technology Co., Ltd.

Key points for BMS to improve range and charging time

Compared with fuel vehicles, short cruising range and long charging time have always been the pain points of electric vehicles. In what ways can BMS improve the cruising range and charging time of electric vehicles? In response to this problem, Peng Yongjun analyzed the improvement of BMS on the cruising range of electric vehicles from three aspects:

First, improve the balance efficiency of the battery. Everyone knows that a battery system consists of many energy-saving cells and follows the barrel effect, that is, the performance of the battery system largely depends on the cell with the worst performance, improving the battery balancing efficiency and improving the consistency of the cells. It can effectively extend the cruising range of the vehicle;

Second, improve the energy recovery efficiency during braking or vehicle downhill. A good energy feedback control algorithm needs to give full play to the battery charging performance as much as possible under the premise of ensuring the safety of battery application, and obtain the most efficient current feedback power strategy through real-time calculation of SOC, temperature, instantaneous charging power, and continuous charging power;

Third, improve the estimation accuracy of SOC. This can also be understood as improving the cruising range from another dimension, that is, the more accurate the user’s grasp of the real power, the more confident they can use a wider range of SOC ranges.

Regarding how to shorten the charging time, he believes, “The charging time can be shortened by increasing the charging power while ensuring safety. The effective way to increase the charging power is a good thermal management strategy. According to the thermal model of the battery system, the temperature is affected. To make predictive adjustments to keep the battery at optimal temperature conditions throughout the charging process, thermal management strategies need to be continuously adjusted as the battery ages.”

Preventing battery abuse is the premise of ensuring safety

The safety of electric vehicles has always been the focus of the industry and the foundation for the healthy development of the industry. At present, most of the safety issues of electric vehicles are directly or indirectly related to the battery system. As the brain of the battery system, BMS is mainly to prevent the abuse of battery power. It can be protected from the following aspects:

First, temperature protection. BMS has a clear operating temperature threshold setting, and there are the highest and lowest temperature limits for charging and discharging. If the set limit is exceeded, the system must not be turned on or the power must be reduced to run;

Second, voltage protection. For the risk of overcharge and overdischarge, the BMS is set with the highest and lowest charging and discharging voltage thresholds to ensure that the system automatically stops running when the thresholds are touched;

Third, current protection. Accurately estimate the battery state through high-precision battery models and algorithms, calculate safe and efficient available power, monitor the current of the battery, and prevent risks such as battery deterioration and dendrites caused by overcurrent.

Peng Yongjun introduced to Yufei.com reporters, “Highly reliable, high-precision signal sampling and software and hardware stability are the basis of battery system safety, and battery models and algorithms are the core of battery system safety. Udan Technology BMS monomer voltage acquisition error ± 1mV, temperature acquisition error ±1°C, current acquisition error ±0.5%, provide accurate acquisition of analog signals; strictly follow the automotive electronics development and verification process in the software and hardware development process, and apply the MBD development mode to ensure system stability; high-precision battery charging The discharge model, power pool model, etc. are implemented with highly robust algorithms to ensure accurate battery state estimation.”

Battery life has always been an important issue faced by electric vehicles. Almost all battery manufacturers are extending battery life through technology research and development, thereby reducing user costs. According to Peng Yongjun’s analysis, the factors and countermeasures affecting battery life are as follows: First, the chemical system largely determines the life of lithium batteries. Battery factories can improve battery life by improving battery chemistry; second, temperature affects battery life. During the charge-discharge cycle, the higher the temperature, the faster the battery life decays. In order to reduce the impact of temperature on life, the thermal field balance needs to be considered when batteries are grouped, and a reasonable BMS thermal management strategy should be formulated to make the battery work in a suitable temperature region to the greatest extent. Third, current affects battery life. Excessive charge and discharge current will lead to irreversible chemical and physical reactions inside the battery, resulting in irreversible attenuation of the battery capacity. For this part of the impact, power prediction can be performed by optimizing the SOP algorithm in the BMS to ensure that the charging and discharging power is within the acceptable range of the battery. Depth of discharge affects battery life. Shallow charging and shallow discharging will help prolong battery life. In view of the influence of the depth of discharge, the battery pack can be designed to appropriately reserve capacity to avoid deep discharge.

Breaking the shackles of traditional BMS, iBMS conducts beneficial exploration based on big data

With the increasing requirements for lean management of lithium batteries, traditional BMS is facing many challenges. Lithium battery is like a living body, and its state is affected by multiple influencing factors in the whole life cycle. It is difficult to have a “perfect” model that can characterize all its changing characteristics once and for all; in addition, BMS parameter calibration based on laboratory sample data , it cannot accurately match the whole process of battery application, and must be continuously corrected in the process; in addition, in the face of complex algorithms, the storage and computing capabilities of the SoC are limited, or the expansion cost is high. “There are no two identical leaves in the world”, but traditional BMS manages different lithium batteries whose status changes all the time in batches with the management algorithms, parameters and strategies set uniformly at the factory. The management efficiency can be imagined.

Udan Technology is the first in the industry to propose the concept of iBMS, an intelligent battery management system, advocate a cloud-based collaborative management strategy based on big data, and make useful explorations. The iBMS terminal module integrates a two-way wireless data transmission unit, which uploads the battery operating data to the cloud in real time while monitoring and managing the battery. Using the cloud server’s nearly unlimited storage capacity and computing power, it can target non-stationary changes and multi-dimensional massive battery data based on machine tools. The learning method builds a big data analysis model, continuously iteratively matches the management parameters of the latest state of the battery and distributes it wirelessly, thereby realizing more powerful cloud collaborative management, full life cycle management and personalized management of lithium batteries.

At present, Udan Technology has completed technological breakthroughs and realized productized cloud collaborative management strategies as follows:

First, joint estimation of cloud state. Achieve high noise immunity, low error, high robustness, fast convergence battery state of charge (SOC), state of energy (SOE), state of power (SOP), state of health (SOH), remaining useful life (RUL), etc. Combined cloud-based estimation of battery status improves battery application experience and prolongs battery life.

Second, the cloud is balanced all the time. In view of the overall performance decline and health deterioration caused by the inconsistency of large-capacity battery packs, the charging data curve of each cell is fitted based on the massive cloud data, and then the capacity difference between cells is calculated and the equilibrium time is accurately estimated. The server sends control commands to the terminal module for balanced execution, which improves the balance efficiency by more than 80% compared with the traditional BMS.

Third, cloud active security management. According to multi-source information such as path planning, ambient temperature, real-time road conditions, etc., the battery charging and discharging power management strategy and thermal management (heating or cooling) strategy are adjusted in real time at the edge of the cloud platform to ensure the safe and reliable operation of the power battery system; based on cloud data tracking The evolution trajectory of battery input and output characteristics and battery performance under different working conditions is used to establish a battery life cycle fault diagnosis mechanism; the cloud platform assists in judging the trend of high-voltage circuit contact resistance and analyzing the thermal field change of the battery system to determine the sub-health status of the battery. Provide timely warning and intervention.

Regarding the future integration and optimization space of BMS, Peng Yongjun believes, “Broadly speaking, BMS can be understood from the two dimensions of hardware and software. Hardware is usually composed of sensors, calculators, actuators and other components. With the improvement of hardware capabilities, especially chip capabilities , to provide the possibility for BMS to integrate with other electronic control components of the vehicle in the hardware category to achieve higher integration and better cost. For example, the concept of domain controller proposed by many OEMs is based on this kind of thinking. In the future, BMS may It will not exist as an independent hardware module. However, no matter how the hardware form evolves, the core essence of BMS in the software category will not change. The robust state management algorithm will always be the soul of realizing the safe, accurate and efficient management of batteries.”

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Author: Yoyokuo