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@ARTICLE{Caspari:905801,
      author       = {Caspari, Adrian and Fahr, Steffen and Mitsos, Alexander},
      title        = {{O}ptimal {E}co-{R}outing for {H}ybrid {V}ehicles {W}ith
                      {P}owertrain {M}odel {E}mbedded},
      journal      = {IEEE transactions on intelligent transportation systems},
      volume       = {23},
      number       = {9},
      issn         = {1524-9050},
      address      = {New York, NY},
      publisher    = {Inst. of Electrical and Electronics Engineers},
      reportid     = {FZJ-2022-01021},
      pages        = {14632 - 14648},
      year         = {2022},
      abstract     = {Exploiting the full potential of hybrid electric vehicles
                      (HEVs) requires suitable (i) route selection and (ii) power
                      management. Due to coupling of the two subproblems, an
                      integrated optimization problem is desired, i.e., optimizing
                      simultaneously the route selection and the split between
                      combustion engine and electric motor over the entire route
                      selection. The resulting optimal route and vehicle operation
                      can be used as a basis for a subordinate vehicle controller.
                      We present an eco-routing approach that embeds a hybrid
                      (mechanistic/data-driven) model of the HEV powertrain in an
                      integrated routing and power management optimization
                      problem. Formulating the integrated routing problem with the
                      hybrid model yields a mixed-integer bilinear program which
                      we reformulate and solve a mixed-integer linear program
                      using a state-of-the-art solver. The results show the
                      validity of the developed hybrid powertrain model and
                      demonstrate that the eco routing approach with the
                      powertrain model embedded can be applied to large-scale
                      problems. We consider optimization for minimal travel time
                      and minimum fuel consumption. The latter results in fuel
                      demand reductions up to 70 $\%.$ Alternatively, we minimize
                      the fuel consumption while constraining the travel time to a
                      maximum value resulting in up to 50 $\%$ fuel demand
                      reductions. The highest fuel demand reductions are achieved
                      in urban environments. The entire framework is written in
                      python and provided as an open-source version (MIT License)
                      under
                      https://git.rwth-aachen.de/avt-svt/public/optimal-routing
                      that can readily be applied.},
      cin          = {IEK-10},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000732220300001},
      doi          = {10.1109/TITS.2021.3131298},
      url          = {https://juser.fz-juelich.de/record/905801},
}