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MAPCASTE: A smart energy management framework for extending driving range in electric vehicles

H. Miri et al.

Simulation Modelling Practice and Theory2026https://doi.org/10.1016/j.simpat.2026.103270article
AJG 2
Weight
0.50

What the paper says

Battery electric vehicles (BEVs) are gaining popularity globally due to their contribution to reduce greenhouse gas emissions (GHG) and promote environmental sustainability. However, range anxiety is one of the biggest barriers to the widespread adoption of BEVs. Accurate estimation of the remaining driving range (RDR) of a BEV can help mitigate this issue by indicating to drivers the right timing and energy required to power their BEV to meet their needs, especially during long distance trips. In this paper, we present MAPCASTE (Measuring, Analyzing, Predicting, foreCASTing, and Executing), a smart framework for in-vehicle energy management, with an application to BEV speed control. The framework components have been developed and deployed in the Eclipse MOSAIC co-simulator. With its modular architecture, the MAPCASTE combines a Proportional-Integral-Derivative (PID) controller and a hybrid state of charge (SoC) prediction approach to optimize speed profiles, reduce energy consumption, and extend driving range. Key findings indicate the ability of the MAPCASTE framework to minimize energy consumption and extend the driving range by up to 3 Km on urban roads. MAPCASTE demonstrates promising potential as a practical solution for enhancing the range of BEVs and operating efficiency in real-world conditions by employing predictive analytics, co-simulation, and scalable design to be both cross-environmentally applicable, meaning it can be adapted to areas such as E-mobility and industrial systems, and scalable to accommodate increasing volumes of data and complexity of systems in any environment.

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https://doi.org/https://doi.org/10.1016/j.simpat.2026.103270

Or copy a formatted citation

@article{h.2026,
  title        = {{MAPCASTE: A smart energy management framework for extending driving range in electric vehicles}},
  author       = {H. Miri et al.},
  journal      = {Simulation Modelling Practice and Theory},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.simpat.2026.103270},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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