Adaptive regenerative braking for electric vehicles with an electric motor at the front axle using the state dependent riccati equation control technique

Sven Jansen, Mohsen Alirezaei, Stratis Kanarachos

Research output: Contribution to journalArticle

2 Citations (Scopus)


In this paper a novel adaptive regenerative braking control concept for electric vehicles with an electric motor at the front axle is presented. It is well known that the “phased” type regenerative braking systems of category B maximize the amount of regenerative energy during braking. However, there is an increased risk of maneuvering capability loss especially during cornering. An integrated braking controller which determines - in a single step - the desired yaw moment and allocates the braking demand between hydraulic brakes and electric motor during cornering is designed using the State Dependent Riccati Equation (SDRE) method. A unique method for deriving the State Dependent Coefficient (SDC) formulation of the system dynamics is proposed. Soft constraints are included in the state dynamics while an augmented penalty approach is followed to handle hard constraints. The performance of the controller has been evaluated for different combined cornering-braking scenarios using simulations in a Matlab/Simulink environment. For this an eight degrees of freedom (DOF) nonlinear vehicle model has been utilized. The numerical results show that the controller is able to optimize (locally) the amount of regenerative braking energy while respecting system’s constraints such as tire force saturation, vehicle yaw rate and slip angle errors.

Original languageEnglish
Pages (from-to)424-437
Number of pages14
JournalWSEAS Transactions on Systems and Control
Issue number1
Publication statusPublished - 2014
Externally publishedYes



  • Optimization
  • Regenerative braking and cornering
  • Stability
  • State dependent riccati equation controller
  • State estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mathematics(all)
  • Artificial Intelligence

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