## 1 Introduction
In the process of energy recovery of electric vehicles, many scholars will choose an I curve as the distribution criterion of braking force, but this distribution curve will change with the vehicle's mass and centroid position because the loader will change the centroid position when it makes turns, lowering and lifting the boom. It cannot obtain real-time I-curves to maximize the use of a road adhesion coefficient. [PERSON] et al. proposed an adaptive braking torque distribution method for front wheel drive based on the real-time estimation of a road adhesion coefficient. When the braking strength increases to the set line, the mechanical braking force of the rear axle is involved, and the braking force of the front and rear wheel is distributed according to the I curve [9]. Based on the energy management control strategy, the flow and distribution of energy among multiple power sources are mainly managed [10]. For electric vehicles, the research focuses on the on-board composite power supply system with the combination of a battery and supercapacitor and the composite energy storage system with a battery and flywheel [11]. [PERSON] et al. proposed a fuzzy energy management strategy applicable to accumulated supercapacitors, which can match the power requirements under different driving modes and coordinate with the proposed genetic algorithm-based intelligent torque and speed control of traction motors to minimize the energy pulsation of vehicles [12]. [PERSON] et al. proposed an optimized energy recovery strategy based on braking safety and efficient recovery to enhance the energy recovery rate and shorten the braking distance. A torque optimization strategy to minimize the energy loss of the regenerative braking system is proposed to improve energy recovery, which can increase the energy recovery by 3.35% under WLTC cycles compared with the average distribution strategy [13]. In terms of driver intention recognition, many scholars have conducted in-depth research on electric vehicles. [PERSON] et al. proposed a hierarchical control strategy of braking energy recovery that considers braking intention recognition and electropneumatic brakingcompensation to maximize energy recovery and improving braking sense, which is used to improve the economic performance and ride comfort of electric vehicles [14]. [PERSON] et al. divided braking intention into moderate deceleration, emergency braking and pressure following, and they adopted a neural network for the online recognition of braking intention to achieve the purpose of improving driver comfort [15]. [PERSON] et al. adopted an LQV neural fuzzy system to establish the braking intention recognition model and improve the accuracy of braking intention recognition [16]. [PERSON] et al. proposed an online recognition method of driver's braking intention based on artificial bee swarm support vector machine (ABC-SVM), which can effectively screen out irrelevant features and make braking intention recognition more accurate and fast [17]. [PERSON] et al. established a braking intention identification model for an electronic braking system (EBS) in commercial vehicles based on the neural network. This model can be used to promote vehicle safety and braking performance [18]. There are also more research studies using hybrid brain-computer interfaces to identify the driver's driving intention [19; 20]. This is mostly used in intelligent driving. In the research of a regenerative control strategy and braking intention recognition, most of the application objects are electric vehicles. Electric loaders and electric vehicles have important differences, such as load characteristics, working conditions, and driving environment. Due to the harsh working environment, varied working scenes and complex working conditions of the loader, the relatively mature regenerative control strategy and braking intention recognition on electric vehicles cannot be directly applied to electric loaders.