What question did this study set out to answer?

The research aims to design a torque-split controller for effective energy recovery in BEVs during braking.

April 11, 2026Open Access

Regenerative–Frictional Brake Blending in Electric Vehicles Considering Energy Recovery and Dynamic Battery Charging Limit: A Reinforcement Learning-Based Approach

FNFarshid NaseriAalborg University BNBjartur Ragnarsson a NordiAalborg University KSKonstantinos Spiliotopoulos

Key Points

The research aims to design a torque-split controller for effective energy recovery in BEVs during braking.
Developed an RL-based controller using a DDPG agent.
Incorporated State-of-Power calculations to manage battery charging limits.
Trained the agent in a MATLAB/Simulink environment simulating different braking scenarios.
Evaluated performance across various State-of-Charge conditions and driving cycles.
Achieved energy recovery of up to ~98% of total braking energy in WLTP type 3 driving cycle.
Controller operated effectively at the battery's State-of-Power limits.
Demonstrated capability for adaptive, constraint-aware energy management.

Abstract

This paper presents the design, development, and evaluation of a Reinforcement Learning (RL)–based torque-split controller for the regenerative braking system (RBS) in battery electric vehicles (BEVs). The controller employs a Deep Deterministic Policy Gradient (DDPG) agent to distribute the braking demand between regenerative and frictional braking systems with the aim of maximizing energy recovery while adhering to the physical and operational constraints. To capture the charging limitation of the battery, a State-of-Power (SoP) calculation mechanism is incorporated, providing a time-varying bound on the regenerative charge power. The agent is trained in a MATLAB/Simulink environment representing the digital twin of a BEV drivetrain, and considers a mix of different braking scenarios, i.e., light braking, medium braking, hard braking, and emergency braking. The RL’s reward shaping promotes efficient utilization of the SoP-limited regenerative capability while discouraging constraint violations and aggressive control behavior. Across a range of State-of-Charge (SoC) conditions and driving cycles, including the Worldwide Harmonized Light–Vehicle Test Procedure (WLTP) and synthetic random-rich driving cycle, the RL controller consistently delivers promising performance, yielding energy recovery of up to ~98% of the total braking energy available on WLTP type 3 driving cycle while being able to operate closely to the battery SoP limit. The results demonstrate the proposed controller’s capability for adaptive, constraint-aware energy management in BEVs and underline its potential for future intelligent braking strategies.

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