Analysis of individual differences in controller workload

As lithium-ion batteries are widely used in the industry represented by electric vehicles, their collision-induced safety problems have aroused widespread concern in the industry and society. Under the collision condition of an electric vehicle, on the one hand, the deformation of the battery will lead to direct fire and explosion. On the other hand, the unknown deformation of the battery caused by the collision will bring safety risks to subsequent use. For the unknown deformation of batteries after the collision, abnormal batteries can only be perceived by physical signals such as voltage, temperature, and current, and there is no direct monitoring method for battery deformation. To bridge this gap, this paper uses small piezoelectric plates and realizes deformation and collision monitoring of lithium-ion batteries based on ultrasonic guided waves. An experimental platform for different loads of lithium-ion batteries was built, and quasi-static and micro-collision experiments were carried out. The experimental results were analyzed and discussed to clarify the change law of ultrasonic signals under different loads. The results show that in the quasi-static battery experiment, the ultrasonic amplitude signal was negatively correlated with the deformation degree of the battery. When the battery was subjected to a gradually increasing load and the deformation became more serious, the amplitude would gradually decrease; when the battery was deformed and failed, the amplitude would drop instantaneously. In the ball-dropping experiment, the impact deformation will affect the change of amplitude and energy integration of the ultrasonic signal, which can be used as a basis to judge whether the battery collision occurs. Finally, a mapping relationship between ultrasonic signal and battery deformation for failure monitoring under large deformation is established, and the criteria for collision deformation based on ultrasonic sensors is proposed. The results suggest a new method for the safety monitoring of lithium-ion batteries, which is expected to be applied in electric vehicles and other fields.