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<p>Regenerative braking provides a cost-effective means of extending the driving range in electric vehicles (EVs). However, its control strategy plays a crucial role in optimizing both energy regeneration and vehicle dynamic stability during braking. Hitherto conventional braking strategies such as regenerative energy maximization strategy (<i>REMS</i>) or <i>I-curve</i> based brake force distribution strategy (<i>I-BFDS</i>) are limited by either violation of <i>ECE R13/H</i> stability regulations or substantial loss of recuperation potential, respectively. Present research proposes an advanced and cohesive recuperative braking methodology that amalgamates the tenets of <i>REMS</i> and <i>I-BFDS</i> by synthesizing real-time brake intention recognition (<i>BIR</i>) within a drivetrain-specific, dynamically modulated co-operative brake torque allocation amongst front and rear wheels. Furthermore, the work contributes to a comprehensive mathematical formulation of the EV drivetrain including braking dynamics integrated within a dual-loop hierarchical control interface comprising three dedicated PI controllers governed by the proposed recuperative control scheme. Simulation studies under <i>WLTP</i> drive cycles and urban braking scenarios validate the model against manufacturer benchmarks with an overall driving range extension of ~ 6.5%. Comparison with <i>REMS</i> establishes the efficacy of the proposed strategy towards energy recuperation potential, while degree of stability is benchmarked in collation to <i>I-BFDS</i>, satisfying axle-torque precedence requirements. Results demonstrate the proposed algorithm achieves upto 16.92% (Front Wheel Drive) and 15.31% (Rear Wheel Drive) improvement in energy recovery without compromising dynamic stability or unsafe axle torque bias. The findings substantiate the feasibility of the proposed context-aware recuperative braking strategy in achieving a balanced trade-off between energy ergonomics, braking performance, and dynamic stability, providing a scalable pathway for next-generation autonomous EV braking systems.</p>

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Recuperative control strategy based on brake intention recognition in electric vehicles to augment energy ergonomics

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Indranil Banik,
  • Arup Kumar Nandi

摘要

Regenerative braking provides a cost-effective means of extending the driving range in electric vehicles (EVs). However, its control strategy plays a crucial role in optimizing both energy regeneration and vehicle dynamic stability during braking. Hitherto conventional braking strategies such as regenerative energy maximization strategy (REMS) or I-curve based brake force distribution strategy (I-BFDS) are limited by either violation of ECE R13/H stability regulations or substantial loss of recuperation potential, respectively. Present research proposes an advanced and cohesive recuperative braking methodology that amalgamates the tenets of REMS and I-BFDS by synthesizing real-time brake intention recognition (BIR) within a drivetrain-specific, dynamically modulated co-operative brake torque allocation amongst front and rear wheels. Furthermore, the work contributes to a comprehensive mathematical formulation of the EV drivetrain including braking dynamics integrated within a dual-loop hierarchical control interface comprising three dedicated PI controllers governed by the proposed recuperative control scheme. Simulation studies under WLTP drive cycles and urban braking scenarios validate the model against manufacturer benchmarks with an overall driving range extension of ~ 6.5%. Comparison with REMS establishes the efficacy of the proposed strategy towards energy recuperation potential, while degree of stability is benchmarked in collation to I-BFDS, satisfying axle-torque precedence requirements. Results demonstrate the proposed algorithm achieves upto 16.92% (Front Wheel Drive) and 15.31% (Rear Wheel Drive) improvement in energy recovery without compromising dynamic stability or unsafe axle torque bias. The findings substantiate the feasibility of the proposed context-aware recuperative braking strategy in achieving a balanced trade-off between energy ergonomics, braking performance, and dynamic stability, providing a scalable pathway for next-generation autonomous EV braking systems.