## 4 Conclusions  
During actual driving, the engine, as a range extender of EREV, frequently switches between efficient operating and shutdown conditions, generating many start-stop events that sharply increase pollutant emissions. Considering the limits on real driving emissions in energy management strategies is crucial. To this end, the interpolated-startup-corrected emission models are proposed, which approximate the real driving emissions under the non-start-stop condition by interpolating the engine steady-state performance maps and statistically infer the abnormal engine start-up emissions based on the recorded emissions in the real driving emission tests. The resulting interpolated-startup-corrected emission models are verified to agree well with the tested real driving emissions. The optimization problem is constructed with fuel consumption as the objective and real driving emissions as the constraints and is transformed into an unconstrained one by the penalty function method. To solve the optimization problem, the DP algorithm combined with the interpolated-startup corrected emission models is developed by inventing multiple interpolation operations in the value-space and action-space domains. The DP algorithm provides start-stop reduction strategies and condition migration strategies. The start-stop reduction strategies suggest gradually reducing the engine start-stop events as the emission limits are tightened. On this basis, the condition migration strategies indicate how to relocate the engine operating points to compromise the fuel economy and pollutant emission targets. Several DP solutions are interpreted reasonably, confirming the developed DP algorithm's correctness and effectiveness. Based on a new approach that has never been attempted before, this study constructs the emission models from real driving emission test data and then integrates the emission models into the DP optimization of EREV energy management strategies while considering the control of pollutant emissions. It is confirmed that the simultaneous optimization of energy consumption and emissions brings significant overall economic and social benefits to EREVs. Although the DP algorithm cannot be directly implemented in real time, it can be used to obtain theoretically optimal control results and as a benchmark for evaluating the control performance resulting from other real-time energy management strategies. The study's next step will consider combining such real driving emission models that can reflect the dynamic effects of operating conditions with real-time optimization algorithms, such as DRL algorithms, to promote the development of EREV energy management strategies. Conceptualization, H.X. and L.Z. ; methodology, H.X. and L.Z. ; software, Y.C. and G.C. ; validation, H.X. and L.Z. ; formal analysis, Y.C. and C.L. ; investigation, Y.C., G.C. and Q.Z. ; resources, L.Z. and Q.Z. ; data curation, H.X. and J.H. ; writing--original draft preparation, H.X. and J.H. ; writing--review and editing, L.Z. and J.H. ; visualization, H.X. and C.L. ; supervision, L.Z. ; project administration, L.Z. ; funding acquisition, L.Z. and Q.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Fundamental Research Funds for the Central Universities (Grant Number 2022 CDJDX-004), the Chongqing Technology Innovation and Application Development Project (Grant Numbers CSTB2022 TIAD-KPX0139 and CSTB2022 TIAD-KPX0050), and the Research Project of State Key Laboratory of Mechanical Transmission for Advanced Equipment (Grant Number SKLMT-ZZKT-2024Z08). Not applicable. **Data Availability Statement**: The data presented in this study are available on request from the corresponding author. **Conflicts of Interest**: Author [PERSON] was employed by China Automotive Engineering Research Institute Co., Ltd. Author [PERSON] was employed by Chongqing Changan Automobile Co., Ltd.