## 4 Simulations and Analysis
### Simulation Results and Analysis
#### 4.2.3 Ice  
The tracked vehicle will face an even lower adhesion coefficient when running on an icy ground than on snow. Under this track-ground condition, the largest adhesion coefficient was only a little more than 0.1. The simulation results and the value of indicators are shown in Figure 8 and Table 4. The abbreviations are the same as braking on a muddy road or snow. As we can see in Figure 3, the \(\mu-\lambda\) curve of track-ice was flatter when slip ratio exceeded 0.2 than any other one shown in the same figure, which means the difference of the adhesion coefficient whether the track was locked or not was rather small, which could weaken the advantage of using an antilock braking method. Even though, the deviation of stopping distance and its percentage between these three antilock methods and full braking were still significant. However, the difference on the control effect of a vehicle using TABS and an SMSC method became larger due to the track-ground adhesion being worse. According to Figures 6-8, we can find that there was an overshoot of slip ratio \(\lambda\) at the start of braking when using an SMSC method in the process of tracking \(\lambda_{d}\). Thanks to the ability of providing positive torques with PMSMs involved in the braking process, the convergence of \(\lambda\) using SMSCR was faster than SMSCM, which led to a shorter stopping distance and better braking performance. Thus, reduced energy recovery was negligible compared to better tracking of the slip ratio. Through the simulations above, we can conclude that although using a traditional antilock braking strategy can enhance braking performance and it even can stand in a better place compared to using an SMSC method without a motor when track-ground adhesion is rather high, the poor performance on the extremely low adhesion track-ground condition with limited maximum deceleration constrains its application. The SMSC method without a motor performed much more stable than full braking. However, its performance was worse than TABS when the track-ground adhesion was rather high, not to mention that it was always worse than the proposed strategy due to the rather poor dynamic response of the mechanical brake than PMSM and the lack of ability to produce a positive torque to accelerate the convergence of sliding mode in the process of braking. As a comparison, the SMSC method with the use of a motor proposed in this article was advantageous in whatever track-ground conditions compared to others, demonstrating a favorable value of application. As shown in Table 5, when the vehicle ran on a muddy road, snow, and ice, the stopping distance reduced by 15.49%, 11.91%, and 9.35%, respectively, and the slip ratio deviation decreased by 99.69%, 99.92%, and 99.96%, respectively. Even compared with TABS, the proposed strategy still had an advantage of reducing by 2.20%, 4.15%, and 4.70%, respectively, the stopping distance and decreasing by 80.88%, 93.62%, and 99.70%, respectively, the slip ratio deviation when running on the same ground as mentioned above, while it also performed better than the SMSC method without using the PMSM. The experiment system consisted of a TCU with the proposed emergency braking control strategy, a driver input device, a dSPACE SCALEXIO HIL experiment system running the real-time model of a DDTV, and a host computer logging and plotting the data of the experiments. The TCU received control inputs from the driver input device and vehicle status signals from dSPACE, then output control instructions to dSPACE to run the real-time model. The TCU, driver input device, and dSPACE were interconnected through a high-speed CAN 2.0 network. Data transmission between dSPACE and the host PC was via Ethernet. The experiment parameters were the same as the simulations shown in Table 1. The experiments were conducted under a muddy road, snow, and ice, respectively.