# Braking Intention Identification Strategy of Electric Loader Based on Fuzzy Control
###### Abstract  
As a widely-used construction machinery, the electric loader has the potential to recover braking energy due to its large mass and frequent starts and stops. Identifying braking intention accurately is the foundation of braking energy recovery. The typical braking condition of an electric loader is analyzed; the braking intention is divided into sliding brake, mild braking, moderate braking, and emergency braking. A large number of braking data were collected under different braking intentions, which are used as the basis for fuzzy control variable partitioning, fuzzy controller parameter setting and fuzzy control rule formulation. The control strategies of deceleration intention identification based on accelerator pedal, braking intention identification based on brake pedal and sliding brake intention identification are proposed in this paper, respectively. This paper takes the hydraulic brake pressure as a feedback parameter, even if there is hysteresis or failure based on the intention of pedal identification, the system can still provide sufficient braking strength to ensure the braking safety. The AMESim(R12)-Matlab/Simulink co-simulation model and prototype are built to verify the feasibility of the control strategy under different braking intention identification. The results show that the braking strength under stronger braking intention can satisfy the braking demand whether at a speed of 2 m/s or 12 m/s, which ensures the safety of emergency braking, and the electric loader can provide stable braking strength under different braking intention and different speed. This has good braking stability. electric loader; fuzzy control; braking intention identification; regenerative braking +
Footnote †: journal: Applied  
Article  
Braking Intention Identification Strategy of Electric Loader Based on Fuzzy Control  
same motor [3]. [PERSON] et al. proposed a braking intention identification strategy of the acceleration pedal and brake pedal to distinguish different braking intentions. Their research showed that the proposed strategy could improve the braking energy recovery up to 71.64% and the brake process was smooth when the loader switched between different braking modes [4]. But, the research was many on energy saving, the discussion for braking strength identification is less. To recover braking energy, it is important to identify the braking intention. Many scholars have conducted in-depth research on braking intention identification for electric vehicles. The accurate identification of the braking intention is the basis for formulating the braking strategy [5]. To classify the braking conditions, [PERSON] et al. proposed a target oil pressure identification algorithm based on the T-S fuzzy neural network model and built the braking conditions classification algorithm based on the sampled braking intention data. According to the braking conditions classification algorithm, the data were divided into the emergency braking condition data and the general braking condition data. The results show that fuzzy PID control could achieve precise control braking force [6]. [PERSON] et al. built a braking intention identification model using fuzzy c-means clustering algorithm. This method could distinguish between the moderate braking intention and gentle braking intention [7]. The researches can distinguish the different braking process, which needs to be identified for further control. [PERSON] et al. investigated the braking intention identification model based on the neural network for commercial vehicles. And, a multi-condition identification model with respect to four typical braking conditions was developed using the fuzzy logic. The experimental results demonstrated that the proposed strategy could make good use of driver braking intention and promote vehicle safety and braking performance [8]. To identify the braking intention accurately, [PERSON] et al. proposed an identification model based on gated recurrent unit network with attention mechanism.