# Research on Brake Energy Recovery Strategy Based on Working Condition Identification
## 6 Conclusions  
In this paper, we propose a regenerative braking strategy based on condition recognition. First, the whole-vehicle model was built in AVL-Cruise. Second, based on the historical driving data, the historical velocity information was divided into four categories of working conditions with obvious differences, and the COA-BPNN online working condition recognition model was constructed. Third, a GA was used to find the optimal parameters in each category, and the offline parameter library was built; additionally, the COA-BPNN was used to identify the online working conditions and retrieve the corresponding parameters. This strategy considered the influence of the overall vehicle load state and driving conditions on braking, making it more adaptable to working conditions and overall vehicle load, and solved the problem of large changes in geographical and commercial vehicle loads. The constructed COA-BPNN working condition recogniser improved the recognition accuracy by 7.6% compared to the BPNN. The simulation results under C-WTVC and synthetic working conditions show that the energy recovery rate of the strategy proposed in this paper reached up to 69.65%, which is much better than the rates achieved with the fixed-ratio strategy and fuzzy control strategy. In this study, brake force distribution coefficients for different loads were proposed, but the real-time identification of vehicle mass was not considered, so the load change needs to be judged by the driver autonomously, and adaptive mass identification can be added for future research.  
W.Z.: Conceptualisation, Methodology, Data Curation, and Writing--Review and Editing. H.L.: Conceptualisation, Methodology, Simulation, Software, Validation, Investigation, Resources, Data Curation, Writing--Original Draft, and Writing--Review and Editing. J.L.: Conceptualisation, Methodology, and Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the central guidance for local scientific and technological development funds, grant number Guike ZY23055014; the Innovation-Driven Development Special Fund Project of Guangxi, grant number Guike AA22068060; and the Science and Technology Planning Project of Liuzhou, grant numbers 2022 AAA0102 and 2022 AAA0104.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Payloads** & **Projects** & **Condition** & **Fuzzy Control** & **Fixed-Ratio** \\  & & **Recognition** & **Strategy** & **Strategy** \\ \hline \multirow{4}{*}{Empty} & Proportion of high efficiency (\%) & 91.3 & 69.4 & 67.6 \\  & Energy consumption (kJ) & 7110.21 & 7110.21 & 7110.21 \\  & Energy recovery (kJ) & 4486.54 & 3342.51 & 1464.7 \\  & Energy recovery rate (\%) & 63.1 & 47.01 & 20.6 \\ \hline \multirow{4}{*}{Half} & Proportion of high efficiency (\%) & 93.5 & 68.3 & 69.7 \\  & Energy consumption (kJ) & 8428.35 & 8428.35 & 8428.35 \\  & Energy recovery (kJ) & 5664.69 & 4121.46 & 1778.38 \\  & Energy recovery rate (\%) & 67.21 & 48.9 & 21.1 \\ \hline \multirow{4}{*}{Full} & Proportion of high efficiency (\%) & 93.4 & 68.9 & 66.8 \\  & Energy consumption (kJ) & 9641.8 & 9641.8 & 9641.8 \\ \cline{1-1}  & Energy recovery (kJ) & 6681.76 & 5090.87 & 2248.47 \\ \cline{1-1}  & Energy recovery rate (\%) & 69.3 & 52.8 & 23.32 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of simulation results under synthetic conditions.