# Research on Brake Energy Recovery Strategy Based on Working Condition Identification
## 1 Introduction
[18] proposed an adaptive fuzzy control strategy, wherein a genetic algorithm was used to determine the optimal allocation parameters, and the strategy also incorporated the driver's influence on the weight factor to achieve dynamic control. [PERSON] et al. [19] optimised the torque distribution across the regenerative braking system, and the mechanical braking system was optimised using a particle swarm optimisation algorithm. In summary, research on brake energy recovery technology for the passenger car sector has shown some progress, but there is still limited-application research in the commercial vehicle sector. Due to the large mass variation in commercial vehicles, which are primarily rear-driven, the established recovery strategies are relatively simplistic, leading to low energy utilisation, poor braking smoothness, and little consideration of the effects of working conditions and loads on braking safety and energy recovery efficiency. As a result, these strategies cannot be effectively applied to scenarios encountered in the actual driving process. In real driving situations, emergencies sometimes occur, and the existing strategies are ambiguous regarding whether energy recovery should be performed when braking is intense. The safety of braking should be fully considered during emergency braking, when all of the braking force needs to be provided by the mechanical brake, without regenerative braking, to ensure that the car can stop quickly. Aiming to address the above problems in brake energy recovery strategies for commercial vehicles, this paper proposes a strategy based on the identification of working conditions. Firstly, taking into account the effect of braking in the whole vehicle load state, the strategy of distributing the braking force of the front and rear axles in the empty-, half and full-load states is proposed, according to the braking intensity magnitude and load state. Secondly, according to the GPS data on the historical driving data of commercial vehicles, and according to the performance parameter indicators of the vehicle, and the time-velocity sequence of data pre-processing, the use of pre-processed data, through the Coati optimisation algorithm (COA), was used for the optimisation of the back propagation neural network (BPNN) to construct the working condition identifier. Finally, a genetic algorithm was used to determine the optimal control parameters of the three loads under each working condition category, and the COA-BPNN working condition identifier identified the current working condition category to retrieve the corresponding optimal control parameters in the offline parameter library. The paper is structured as follows: In Section 2, the whole-vehicle model of a hybrid commercial vehicle is introduced, and its construction in AVL-Cruise v.2019 software is described. In Section 3, the data processing, state classification, and state recognition construction are presented. In Section 4, the development of front and rear axle brake power distribution strategies, based on different loads and energy recovery strategies based on state recognition, is described, as is the construction of the strategy model in Simulink. In Section 5, the simulation of the strategies is described, and the results are studied. In Section 6, conclusions are drawn.