Difficulties in testing BMS battery data for new energy electric vehicle safety performance

According to the safety performance test of new energy electric vehicles, the vehicle frame number and BMS battery data must be collected for corresponding evaluation. Domestic electric vehicles are mainly BYD, Tesla, GAC Aion, Wuling New Energy, Changan New Energy, Volkswagen, Ideal, Weilai, Nezha and other mainstream brands. Unlike traditional fuel vehicles, electric vehicles do not need to comply with the OBD2 standard because they have zero emission properties.

In the safety performance testing of new energy electric vehicles, the frame numbers and BMS battery management system data of these models and brands have become personalized data collection under non-standardization. According to the traditional OBD2 standard under the ISO protocol of 15031-5, the frame number collection of fuel vehicles only accounts for the majority, and there will be great differences in new energy vehicles. The actual situation is that in order to ensure the needs of licensing, many new energy vehicle manufacturers can obtain the frame number (VIN) through the fuel vehicle diagnostic protocol, but in the BMS management system, it is provided by well-known domestic and foreign large companies such as Joyson Electronics, Yineng Electronics, BYD, Xinwanda, Bosch, Hella, Wanxiang, Panasonic, etc. for electric vehicle BMS battery management systems.

Under various cooperation modes of mutual matching, combination, joint R&D and cross-cooperation between new energy vehicle manufacturing and core automotive components, the data and DBC control strategies of the new energy vehicle BMS battery management system will become varied. This is the core difficulty. If it is from the perspective of after-sales, or mainly to meet the safety performance testing of new energy electric vehicles, it is necessary to collect these BMS data, then there will inevitably be a high data threshold in the process of data collection, and there will also be a lot of R&D investment.

From the perspective of actual vehicles, if the frame number and BMS signal in the safety performance test of new energy electric vehicles are to be collected to meet the needs, then time, efficiency, model coverage, and technical capabilities will all be huge barriers. New energy electric vehicles are very different from traditional fuel vehicles. The complexities of technical architecture, parts suppliers, DBC control strategy, collection difficulty, etc. will lengthen the implementation project cycle and R&D investment. Suruide Technology, a leading domestic company, has accumulated data on more than 270 models in 7 years. Because new energy vehicles are updated too quickly, this also includes data on many models that have been discontinued. It can be seen that whether the collected data can be used is still a potential problem.

Because new energy vehicles are updated too quickly, all implementation standards are updated, whether from the frame number or the BMS battery management system, and the endurance, there are huge differences. In addition, there are differences in network structure, such as the standard CAN protocol, CANFD protocol, Ethernet protocol and other complex combination protocols, such as BMW and Audi's BSD protocol and SENT protocol.

Overall, the safety performance testing project of new energy electric vehicles is large, with many models, complex protocols, long collection period, high technical requirements, and wide coverage of models. These are the overall difficulties of the project. To solve these problems, companies need to have long-term precipitation and planning, and have dedicated personnel to collect the interaction of the DBC protocol layer of various vehicles. In addition, senior technicians and professional collection equipment are also needed to meet such projects. Business conditions are secondary, because there is no minimum, only lower. Combined with the information data collection and management of charging piles, this is a huge project.

In addition, typical issues related to product and R&D management of Chinese enterprises:

1. Lack of systematic and correct product management concepts: This is mainly manifested in the cook doing the driver's work, the car driver doing the truck driver's work, and the core suppliers outsourcing the product work.

2. Lack of foresight and effective product planning: do whatever is available and whatever makes more money, resulting in separation from the core business, inability to find the market positioning accurately, deviating from the main line when the profit is good, and doing anything with low profit.

3. Lack of systematic business decision review throughout the product life cycle: Failure to correctly view the project cycle and company development path, resulting in conflicts between execution and exploration.

4. The organizational structure with obvious functional characteristics hinders cross-departmental and cross-industry collaboration: for example, the lack of coordination between sales and R&D, the wrangling and buck-passing between engineering and quality, etc.

5. Irregular, inconsistent, and serial product development process: product and R&D, code confusion

6. Weak project management (including schedule, cost, quality, risk, etc.)

7. Technology development and product development are separated, and there is a lack of a linkage operation mechanism: Superficial understanding leads to project delays. For example, the logic of waveforms and voltage were originally used for judgment (empiricism), but it was not until the actual vehicle was installed that it was realized that it was wrong from the beginning.

8. Lack of ability to organize shared modules: R&D modules cannot be reused, codes are scattered, and a lot of repetitive R&D is done.