Guangzhou Automobile Group Co., Ltd.

General Electric Company

Jiucai Zhang

Robert August Kaucic

Charles Clarence Hicks, Jr

Sam T Liu

Leng Mao

Yi Wang

Jin Shang

Jun Wang

Chao Liu

Qiang Ren

Sheng Hui

Sichao Guo

Ao Mei

Dadiao Ning

Herman Wiegman

David James

Mert Geveci

Bozhi Yang

Meng Yao

Qin Yang

Xiao Lin

Jinchi Zhang

Xiaosheng Shi

Donghua Zhu

Guorong Li

The vehicle control method includes: acquiring vehicle parameters of a target vehicle and scene parameters of a scene where the target vehicle is located; searching for a target strategy corresponding to the vehicle parameters and the scene parameters in a strategy library, if the vehicle parameters are within a preset vehicle parameter range and the scene parameters are within a preset scene parameter range; inputting the vehicle parameters and the scene parameters into a pre-training model to generate the target strategy, if the vehicle parameters are not within the preset vehicle parameter range or the scene parameters are not within the preset scene parameter range; and controlling the target vehicle based on the target strategy.

Vehicle control method, electronic device and medium

A system and method for recognizing traffic signs during autonomous driving comprises a camera module obtaining a first recognition, a sensor obtains behavior information of the host vehicle and of nearby vehicles and a connected training module generates parameters for traffic sign recognition according to information as to the behavior of the host vehicle and the nearby vehicles. A recurrent neural network module is connected with the training module and the camera module, and outputs a second traffic sign recognition according to the parameters and the first recognition. Training parameters of the second recognition comprise the traffic sign recognition parameters and the first recognition, the accuracy of the training result being improved by combining other sensors to perform traffic sign recognition training.

System, method, and vehicle for recognition of traffic signs

A method and apparatus for optimizing a battery management system (BMS) are provided. The method includes: obtaining training data, wherein the training data comprises data from a target vehicle and data from auxiliary vehicles, and the auxiliary vehicles are vehicles mounted with a same or similar battery system as the target vehicle; optimizing a BMS related algorithm for the target vehicle by performing transfer learning based on the training data, wherein the BMS related algorithm comprises one or a combination of: a battery model, parameters for the battery model, a battery management algorithm, and parameters for the battery management algorithm.

Method and apparatus for optimizing battery management system

Disclosed are a vehicle control method, a vehicle control apparatus, and a storage medium. The method includes acquiring real-time road condition information about an ego vehicle. The real-time road condition information can be used to indicate whether the ego vehicle arrives at an intersection. Vehicle state information can be acquired when the ego vehicle arrives at the intersection. Once a prediction result is obtained based on the vehicle state information, the ego vehicle can be controlled according to the prediction result.

Vehicle control method and apparatus, storage medium, and electronic device

An adaptive processing method for new scenes in autonomous driving, comprising: obtaining scene data corresponding to new scene of vehicle driving, wherein the scene data describes vehicles state and driving operations in the new scene; obtaining a test set of the new scene based on processing the scene data by a preset distribution; updating parameters of a pre-training model by inputting the test set, and obtaining a scene model adapted to the new scene based on gradient iteration of general model parameters of the pre-training model, wherein the scene model is configured to output an autonomous driving strategy for the vehicle in the new scene. Therefore, the autonomous driving vehicle transforms a new scene to a known scene, and no longer be troubled by unpredictable new scenes, and greatly enhance the reliability and stability of autonomous driving.

Adaptive processing method for new scenes in autonomous driving, autonomous driving method and system

Provided are a method and a system for optimizing a BMS model. The method includes: creating a BMS model based on test data of a battery; selecting sample vehicles from real driving data of vehicles equipped with this type of battery through active learning approach, and giving a corresponding weight to each of the selected sample vehicles; optimizing the BMS model based on the data of the selected sample vehicles.

Method and system for optimizing BMS model, storage medium and electric vehicle

Provided is a battery heating system, a battery device and an electric vehicle. The battery heating system includes: a battery pack having multiple battery cells; a plurality of heating resistors, each heating resistor is connected to a corresponding battery cell and is configured to heat the connected battery cell; a switching circuit, which is connected to each battery cell and each heating resistor; a controller, which is connected to the switch circuit and is configured to select one or more battery cells for discharging and one or more battery cells to be heated from the plurality of battery cells according to the temperature and/or energy of each battery cell, and control switches in the switching circuit to switch on/off, so that the current of the one or more battery cells for discharging flows through the corresponding heating resistor to heat the one or more battery cells to be heated.

Battery heating system, battery device and electric vehicle

The present disclosure is directed to a system and method for calibrating state-of-charge (SOC) of an energy storage device. The method includes monitoring, via a battery management system, at least one of an accumulated capacity of the energy storage device after a first SOC calibration or a time period after the first SOC calibration. Another step includes determining, via the battery management system, whether at least one of the accumulated capacity or the time period is above a predetermined threshold. If at least one of the accumulated capacity or the time period is above a predetermined threshold, then the method includes charging, via the battery management system, the energy storage device for a specified duration until the energy storage device reaches a second SOC calibration.

System and method for calibrating battery state of charge

Systems and methods for charging a plurality of battery strings are provided. The individual string current of each of the individual battery strings can be monitored and used to regulate a charging output provided by the charger. For instance, the output voltage of charger can be controlled as part of a closed loop control system based on the individual string current of the battery string under the constraint of an individual string current limit and/or a charging voltage limit. As the charging voltage of the charger increases as a result of the closed loop control based on the individual string current, other battery strings can be coupled to the charger when the charging voltage provided by the charger exceeds a battery string voltage associated with the battery string.

String current limited battery charging control

The present disclosure is directed to a system and method for controlling an energy storage device by more accurately detecting an end-of-discharge voltage of the energy storage device. More specifically, in one embodiment, the method includes determining an end-of-discharge voltage threshold for the energy storage device. Another step includes filtering the end-of-discharge voltage threshold via a filter. The method also includes adjusting a time constant of the filter based on at least one voltage-current condition. Still a further step includes comparing the filtered end-of-discharge voltage threshold and a terminal voltage of the energy storage device. Based on the comparison, the method includes determining a change of state of the energy storage device. Thus, the energy storage device can be controlled based on the change of state.

Growing community of inventors

Arvada, CO, United States of America

Jiucai Zhang