The importance of intelligent lithium battery equipment is far greater than electrification!

by:CTECHi     2021-08-25
2020-2022 is another golden cycle of lithium battery equipment, and the output value will reach about twice that of previous years. Considering that the 20-year epidemic has caused the capital expenditure of battery factories to be postponed, we expect that 21 years will usher in a big year. Therefore, we judge that the importance of intelligence in the future will be far greater than that of electrification. (1) For OEMs, software is more important than hardware. At present, the differences between the OEMs of the three power technologies are gradually decreasing, and the maturity of advanced autonomous driving technology is still early, and the smart cockpit has become a breakthrough point for differentiated competition. As for OEMs, if they lose control of automotive software, they will basically lose the core competitiveness of their products, and their position in the industry chain will decline; (2) The importance of data is prominent. The value of data lies in realization. The current data can be used to optimize the training of intelligent driving algorithms. In the future, new applications and services can be built on the data. If the data cannot be mastered, the OEM will lose the ability to monetize the data. Based on the above analysis, we believe that the impact of intelligence on OEMs is far greater than that of electrification, and that OEMs do not make power lithium batteries that will not affect user stickiness and brand value. However, if the OEM loses intelligence and data, it will eventually become a pure processing factory. We believe that vehicle intelligence is divided into three stages: computing power convergence, model training, and ecological construction. (1) Convergence of computing power: There are 50-100 MCUs in traditional cars, and the distribution of computing power on the car is divergent. The first step of intelligentization is to concentrate computing power; (2) Model training: enough Cars continue to run, training, reasoning and iterating in the cloud to improve the level of intelligence; (3) Ecological building: After the base becomes larger, new applications and services can be developed based on massive data. At present, most car companies are in the first stage, only the first-tier car companies have the strength to develop EE electronic and electrical architecture, and Tesla is already in the second stage.
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