Enhanced safety and lifetime of e-assisted bikes with Panasonic
Tire pressure monitoring solution to improve rider safety and convenience.
Panasonic, a leading producer of e-assisted bikes in Japan, offers a wide variety of products for various uses to the Japanese market. The company has implemented a tire pressure monitoring system for their TiMO A e-assisted bike, which combines the STM32F3 microcontroller and STM32Cube.AI.
Based on information from the motor and the bicycle speed sensor, the system generates a warning to inflate the tires if necessary. Their solution leverages an advanced AI function that simplifies tire air-pressure maintenance, enhances rider safety, and prolongs the life of tires and other cycle components.
Based on information from the motor and the bicycle speed sensor, the system generates a warning to inflate the tires if necessary. Their solution leverages an advanced AI function that simplifies tire air-pressure maintenance, enhances rider safety, and prolongs the life of tires and other cycle components.
By combining the STM32F3 MCU with STM32Cube.AI, we were able to implement the innovative AI function without the need to change hardware. We will continue to increase the range of models with AI functions and strive to fulfill our mission by leveraging STs edge AI solutions.
Hiroyuki KAMO
SW Dev Section Manager at Panasonic Cycle Technology
Approach
The STM32F3 MCU adopted for the TiMO A is based on the Arm Cortex-M4 and features 128 Kbytes of flash memory, along with various high-performance analog and digital peripherals optimal for motor control. In addition to the new inflation warning function, the MCU also determines the electric assistance level and controls the motor.
Panasonic used STM32Cube.AI to reduce the size of the neural network (NN) model they developed, optimizing memory allocation throughout the development of this AI function. The tool enabled Panasonic to quickly and easily optimize their NN model for implementation in the flash memory, which has a limited capacity.
Panasonic used STM32Cube.AI to reduce the size of the neural network (NN) model they developed, optimizing memory allocation throughout the development of this AI function. The tool enabled Panasonic to quickly and easily optimize their NN model for implementation in the flash memory, which has a limited capacity.
Sensor
Motor current and wheel speed sensing.
Resources
Model optimized with STM32Cube.AI
A free STM32Cube expansion package, X-CUBE-AI allows developers to convert pretrained AI algorithms automatically, such as neural network and machine learning models, into optimized C code for STM32.
Running on STM32F3 Series
The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.
