# Research on Brake Energy Recovery Strategy Based on Working Condition Identification
## 5 Simulation Analysis
### Comparative Analysis of Simulation under C-WTVC Cycle  
The simulation was carried out at room temperature (20 \({}^{\circ}\)C) with a road adhesion coefficient of 0.7 and windless environment. During the simulation, the COA-BPNN identified the characteristic information of the working conditions for 10 s of history, and since the first identification occurred in less than 10 s, the simulated velocity was set to 0 for the first 10 s. Figure 16 shows the results of the working condition identification for the C-WTVC, the red line is the category of a moment identified by the COA-BPNN, where 1, 2, 3, and 4 correspond to highway, suburban, urban smooth, and urban congestion conditions, respectively; the blue line is the vehicle velocity; and the accuracy of the identification results is 97.3%, indicating an almost perfectly accurate recognition.  
Figure 17 shows the electric motor working points during braking under three loads under C-WTVC conditions, from which it can be clearly seen that the regenerative braking strategy based on the condition identification in this paper is obviously more clustered in the medium- and high-efficiency range during braking, with fewer low-speed and low-torque working points. On the other hand, the electric motor working points are more scattered for the fuzzy control strategy and the fixed-ratio strategy. The period in which the electric motor efficiency is higher than the 89% contour is referred to as the high-efficiency interval. Within this interval, the proportions of the regenerative brake strategy based on condition detection under empty-, half- and full-load states are 89.2%, 88.1%, and 89.6%, respectively; for the fuzzy control strategy, the proportions are 67.5%, 68.6%, and 69.1%, respectively; and for the fixed-ratio strategy, they are 67.3%, 67.9%, and 68.8%, respectively. It can be seen that the regenerative braking strategy based on the identification of the working conditions can effectively limit the electric motor to work in the high-efficiency zone and increase the recovery efficiency.  
Figure 18 shows a comparison of the total energy consumed and energy recovered from braking for the three load states under the C-WTVC cycle, and it can be clearly seen that, as the load mass of the vehicle rises, the total energy generated during braking increases, and the energy recovered from regenerative braking thus also increases. It is also clear from the graph that the strategy outlined in this paper recovers more energy, regardless of the load.  
Figure 16: C-WTVC cycle category recognition effect.  
Figure 17: Comparison of electric motor working point distribution under C-WTVC conditions.  
Figure 18: Comparison of energy consumed and energy recovered under C-WTVC conditions.  
Table 7 shows the simulation results for three kinds of loads under the C-WTVC cycle, from which it can be seen that under empty-, half- and full-load states, the energy recovery rate of this study's strategy is improved by 34.6%, 36.9%, and 36%, respectively, compared to that observed with the fuzzy control strategy, and the energy recovery rate with the fixed-ratio strategy is improved by 175%, 186.3%, and 197.6%. This indicates that the regenerative braking strategy based on the identification of working conditions can effectively improve the energy recovery rate of the whole vehicle, by ensuring that the braking effect of the whole vehicle is used.