# Research on Multi-Mode Braking Energy Recovery Control Strategy for Battery Electric Vehicles
## 5 Conclusions  
This paper proposes a multi-mode switching brake energy recovery control strategy based on fuzzy control to improve the energy recovery efficiency of battery electric vehicles and increase the range of the vehicles, taking battery electric vehicles as the research object. The proposed control strategy includes three brake energy recovery modes, single-pedal, conventional braking, and coasting, and uses the accelerator pedal and brake pedal signals as the switching conditions between modes so that the vehicle can switch to different modes in real time according to the driving conditions. In the single-pedal mode and conventional braking mode, the electric and hydraulic braking forces are distributed by fuzzy control. A genetic algorithm is added to iteratively optimize the affiliation function of the fuzzy control in the conventional braking mode, which effectively improves the recovered braking energy. Under the premise of ensuring safe and stable braking, the energy generated during braking is recovered as much as possible to improve the range of the vehicle.  
Through the joint simulation analysis of Cruise R2019.2 software and Matlab/Simulink R2021b software, the braking energy recovery rate is 30.6%. The range contribution rate is 49.1% under the NEDC cycle, which is 4% and 9.6% higher than that of the single-mode  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimization Algorithm** & **Maximum Recovered Energy** & **Number of Iterations** \\ \hline Unoptimized & 2050 kJ & - \\ GA Optimization & 2299 kJ & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Maximum recovered energy comparison.  
Figure 20: Optimization of energy recovery curves by genetic algorithms.  
braking energy recovery control strategy, and the range increases by 22.5 km and 114.5 km compared with the no-braking energy recovery control strategy. In the CLTC-P cycle, the brake energy recovery rate is 40.5%. The range contribution rate is 41.6%, 8.7%, and 5.5% higher than the single-mode brake energy recovery control strategy. The range is increased by 13 km compared with the single-mode brake energy recovery control strategy. After adding the genetic optimization algorithm, the energy recovered in regular braking mode is 2299 kJ, 249 kJ more than the energy recovered without algorithm optimization. In summary, the proposed multi-mode brake energy recovery control strategy based on fuzzy control can further improve battery electric vehicles' braking energy recovery efficiency. Braking energy recovery efficiency can effectively increase battery electric vehicles' range.  
The control strategy proposed in this paper was only co-simulated with software to verify its effectiveness. However, the strategy's actual effectiveness has yet to be verified. Therefore, future work will further validate the control strategy's applicability to actual vehicles.  
Conceptualization, B.L. and G.L.; methodology, B.L.; software, S.W. and B.L.; validation, B.L. and G.L.; formal analysis, B.L.; investigation, S.W.; resources, G.L.; data curation, S.W.; writing--original draft preparation, B.L.; writing--review and editing, B.L. and G.L.; visualization, B.L. and S.W.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. and B.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the General Program of the Natural Science Foundation of Liaoning Province in 2022 (2022-MS-376) and the Natural Science Foundation joint fund project (U22A2043).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no conflicts of interest.