Regenerative Braking Optimization using AI

Regenerative Braking Optimization using AI

Mr. Vivek Govindrao Thigle

Lecturer In Mechanical Engineering Government Polytechnic Beed Maharashtra,India

Abstract – The increasing adoption of Electric Vehicles (EVs) has intensified research into energy-efficient technologies that extend driving range and improve vehicle performance.Regenerative braking systems recover kinetic energy during deceleration and convert it into electrical energy stored in the battery.However,conventional regenerative braking systems often operate using fixed control strategies,resulting in suboptimal energy recovery under varying driving conditions.This paper presents an Artificial Intelligence (AI)- based regenerative braking optimization framework that dynamically adjusts braking torque distribution based on real- time vehicle parameters.Machine Learning (ML) algorithms including Artificial Neural Networks(ANN),Reinforcement Learning (RL),and Adaptive Fuzzy Logic Controllers are investigated for maximizing energy recovery while maintaining vehicle stability and passenger comfort. Simulation results demonstrate significant improvements in energy recuperation efficiency compared to traditional braking methods.The proposed system contributes to increased EV range, reduced energy consumption, and enhanced sustainability in transportation systems.

Keywords – Electric Vehicles, Regenerative Braking, Artificial Intelligence, Machine Learning, Energy Optimization, Neural Networks, Reinforcement Learning.

1. INTRODUCTION

   Global concerns regarding fossil fuel depletion, environmental pollution, and climate change have accelerated the transition from internal combustion engine vehicles to electric vehicles. One of the major challenges faced by EVs is limited driving range due to battery capacity constraints.

   Regenerative braking technology addresses this challenge by converting kinetic energy into electrical energy during vehicle deceleration. Conventional regenerative braking systems employ predetermined control rules that cannot effectively adapt to varying traffic conditions, road gradients, battery states, and driver behaviors.

   Artificial Intelligence offers a promising solution by enabling intelligent decision-making based on real-time operating conditions. AI algorithms can learn optimal braking strategies from historical data and continuously improve energy recovery efficiency.

   This research proposes an AI-based regenerative braking optimization framework capable of:

   • Maximizing energy recovery.

   • Maintaining braking safety.

   • Reducing battery degradation.

   • Enhancing overall vehicle efficiency.

     In addition to improving energy efficiency, optimized regenerative braking contributes to enhanced vehicle stability and passenger comfort. Sudden transitions between regenerative and friction braking may create undesirable vehicle responses and negatively affect driving experience

2. PROBLEM STATEMENT

   Current regenerative braking systems suffer from several limitations:

   1. Fixed braking control strategies.

   2. Limited adaptability to dynamic road conditions.

   3. Reduced efficiency during urban driving.

   4. Inability to predict driver behavior.

   5. Battery charging constraints during high regenerative power.

   These limitations result in significant energy losses and reduced vehicle range.

   Therefore, there is a need for an intelligent regenerative braking system capable of dynamically optimizing braking force distribution using AI techniques.

3. OBJECTIVES

   1. Primary Objective

      Develop an AI-driven regenerative braking optimization system for electric vehicles.

   2. Specific Objectives

      1. Maximize regenerative energy recovery.

      2. Improve EV driving range.

      3. Predict driver braking intentions.

      4. Optimize motor braking torque.

      5. Protect battery from excessive charging currents.

      6. Enhance passenger comfort.

      7. Maintain vehicle stability under varying conditions.

4. LITERATURE SURVEY

   Author	Year	Contribution
   Zhang	2022	Deep Learning based regenerative braking control
   Kim	2021	Neural network optimization of EV braking
   Wang	2023	Reinforcement learning for energy management
   Chen	2024	AI-enabled predictive braking systems
   Patel	2025	Intelligent battery- aware regenerative braking

   Research Gaps

   Existing studies focus either on energy recovery or safety. Few studies integrate:

   1 Driver behavior prediction

   1 Battery health management

   1 Real-time AI optimization Within a single framework.

5. PROPOSED METHODOLOGY

   AI-Based Regenerative Braking Architecture

6. SYSTEM ARCHITECTURE

   1. Overview of Proposed System Architecture

      The proposed AI-Based Regenerative Braking Optimization System is designed to maximize energy recovery during vehicle deceleration while ensuring safety, passenger comfort, battery protection, and vehicle stability. The architecture integrates advanced sensing technologies, machine learning algorithms, optimization techniques, motor control systems, and battery management functions into a unified intelligent framework.

      The system operates through multiple interconnected layers that continuously collect vehicle data, analyze operating conditions, predict braking requirements, optimize regenerative torque, and store recovered energy in the battery pack. The architecture follows a real-time closed-loop control approach, enabling adaptive decision-making under varying driving conditions.

      The complete architecture consists of the following major modules:

      1. Vehicle Sensors Layer

      2. Data Acquisition Layer

      3. AI Prediction Engine

      4. Optimization Controller

      5. Brake Torque Distribution Unit

      6. Motor Control Unit

      7. Energy Recovery System

      8. Battery Storage and Management System

   2. Vehicle Sensors Layer

      The Vehicle Sensors Layer forms the foundation of the proposed architecture. It continuously monitors vehicle operating parameters and supplies real-time information to the AI engine.

      1. Functions

         • Measure vehicle speed.

         • Monitor wheel rotational speed.

         • Detect brake pedal position.

         • Monitor battery temperature.

         • Measure motor temperature.

         • Detect road inclination.

         • Collect traffic condition information.

      2. Sensors Used

         1. Vehicle Speed Sensor

            Measures the instantaneous speed of the vehicle. Output:

            [

            V=Vehicle\Speed\(km/h)

            ]

            Importance:

            • Determines available kinetic energy.

            • Supports braking torque calculation.

         2. Wheel Speed Sensors

            Installed on each wheel to measure wheel rotation speed. Functions:

            • Slip detection.

            • Stability monitoring.

            • Anti-lock braking integration.

         3. Brake Pedal Position Sensor

            Measures driver braking demand. Range:

            0% 100%

            Purpose:

            • Driver intention prediction.

            • Deceleration estimation.

         4. Battery SOC Sensor

            Measures battery charge level. Range:

            0% 100%

            Purpose:

            • Prevent battery overcharging.

            • Optimize regenerative current.

         5. Temperature Sensors

            Monitor:

            • Motor temperature

            • Battery temperature Purpose:

            • Thermal protection.

            • Safe regenerative operation.

         6. Road Gradient Sensor

         Determines road slope. Outputs:

         • Uphill condition

         • Downhill condition

         • Flat road condition Purpose:

         • Predict potential energy changes.

         • Optimize energy recovery.

   3. Data Acquisition Layer (DAQ)

      The Data Acquisition Layer collects, preprocesses, filters, and normalizes sensor information before sending it to AI modules.

      Components

      1. Sensor Interface Module Interfaces with:

         • CAN Bus

         • ECU

         • IoT Gateway

         • Vehicle Communication Network Functions:

         • Data collection

         • Data synchronization

         • Communication management

      2. Signal Conditioning Module Raw sensor signals may contain:

         • Noise

         • Missing values

         • Outliers

           Therefore, signal conditioning performs:

         • Filtering

         • Smoothing

         • Calibration Methods:

         • Kalman Filtering

         • Moving Average Filtering

         • Digital Signal Processing

      3. Feature Extraction Module Extracts meaningful features:

         Examples:

         • Vehicle acceleration

         • Braking intensity

         • Driving patterns

         • Battery discharge rate Generated Feature Vector:

           [ X=[V,SOC,T_b,T_m,P_b,G]

           ]

           Where:

           V = Vehicle Speed SOC = State of Charge

           Tb = Battery Temperature Tm = Motor Temperature Pb = Brake Pedal Position G = Road Gradient

   4. AI Prediction Engine

      The AI Prediction Engine acts as the intelligence center of the proposed system.

      It predicts future vehicle states and determines optimal braking behavior.

      Major Subsystems

      1. Driver Intent Prediction Module

         Purpose:

         Predict driver’s braking intentions before actual braking occurs.

         Input Parameters:

         • Accelerator position

         • Brake pressure

         • Vehicle speed

         • Historical driving behavior Output:

         • Mild braking

         • Moderate braking

         • Emergency braking Techniques:

         • Artificial Neural Networks

         • Deep Learning

         • LSTM Networks Benefits:

         • Faster braking response

         • Better energy recovery planning

      2. Road Condition Analysis Module

         Purpose:

         Evaluate road environment conditions. Inputs:

         • Road slope

         • Surface condition

         • Weather information

         • Traffic density Outputs:

         • Dry road

         • Wet road

         • Slippery road

         • Congested traffic Benefits:

         • Stability enhancement

         • Safe regenerative torque application

      3. Battery State Analysis Module

         Purpose:

         Evaluate battery condition in real time. Parameters:

         • SOC

         • SOH

         • Temperature

         • Charging limits Outputs:

         • Available charging capacity

         • Maximum regenerative current Benefits:

         • Battery protection

         • Increased battery lifespan

   5. Optimization Controller

      The Optimization Controller determines the optimal braking torque distribution.

      1. Objectives

         Maximize:

         • Energy recovery

         • Driving range Minimize:

         • Battery degradation

         • Energy losses

         • Passenger discomfort

      2. Machine Learning Module

         Artificial Neural Network receives: Inputs:

         • Speed

         • SOC

         • Brake pressure

         • Gradient Output:

         • Optimal regenerative torque

      3. Reinforcement Learning Agent

         RL continuously learns optimal braking strategies. Components:

         State:

         [ S=(V,SOC,G,P_b)

         ]

         Actions:

         • Increase regenerative torque

         • Reduce regenerative torque Reward:

           [

           R=Energy\Recovery-Penalty

           ]

           Benefits:

         • Self-learning capability

         • Continuous improvement

      4. Multi-Objective Optimization

      Simultaneously optimizes:

      1. Energy Recovery

      2. Safety

      3. Comfort

      4. Battery Health

      Maximize(E_r)

      ]

      Subject To:

      [

      SOC<SOC_{max}

      ] [

      T_{regen}<T_{max}

      ]

   6. Brake Torque Distribution Unit

      This module determines how braking force is shared.

      1. Inputs

         • Driver demand

         • AI recommendations

         • Vehicle dynamics

      2. Outputs

         1. Regenerative Torque

            Generated by electric motor. Benefits:

            • Energy recovery

            • Reduced brake wear

         2. Friction Torque

         Generated by mechanical brakes. Benefits:

         • Emergency stopping

         • Backup braking

      3. Torque Blending Logic

      Total Braking Torque:

      [

      T_{total}=T_{regen}+T_{friction}

      ]

      Condition	Regen	Friction
      Light Braking	90%	10%
      Medium Braking	70%	30%
      Emergency Braking	20%	80%

      The system dynamically adjusts torque ratios. Example:

   7. Motor Control Unit

      The Motor Control Unit converts braking commands into electrical energy.

      1. Components

         1. Torque Command Generator Generates braking torque commands.

         2. Inverter Controller Converts:

            DC AC

            Controls motor operation using PWM.

         3. Motor Drive Typically:

         • PMSM

         • BLDC Motor Functions:

         • Regenerative generation

         • Torque production

   8. Energy Recovery Unit

      This unit converts recovered kinetic energy into usable electrical energy.

      1. Kinetic Energy Capture Available Energy:

         E_k=\frac{1}{2}mv^2

      2. Power Conversion

         Converts generated AC power into DC power. Methods:

         • Rectification

         • DC Link Control

      3. Energy Quality Control Ensures:

         • Voltage regulation

         • Current regulation

         • Power stabilization Benefits:

         • Improved charging efficiency

         • Battery protection

   9. Battery Storage and Management System

      The recovered electrical energy is stored within the EV battery pack.

      1. Battery Pack Stores recovered energy.

         Technologies:

         • Lithium-Ion

         • NMC

         • LFP

      2. Battery Management System (BMS) Functions:

         • Cell balancing

         • Thermal protection

         • Overcharge prevention

         • SOC estimation

      3. Charge Management Controller Controls:

         • Charging current

         • Charging voltage

         • Regenerative charging limits Benefits:

         • Enhanced battery life

         • Improved safety

   10. Overall System Operation

       The complete workflow operates as follows:

       1. Sensors collect vehicle data.

       2. DAQ preprocesses information.

       3. AI engine predicts driver intent and road conditions.

       4. Optimization controller computes optimal torque.

       5. Brake controller distributes braking force.

       6. Motor generates electrical energy.

       7. Energy recovery system converts and regulates power.

       8. Battery stores recovered energy.

       9. BMS updates SOC and health parameters.

       10. AI receives feedback and continuously improves decisions.

   The proposed architecture creates a fully intelligent, adaptive, and energy-efficient regenerative braking ecosystem capable of maximizing EV efficiency while ensuring safety, comfort, and battery longevity.

7. MATHEMATICAL MODEL

   The kinetic energy available during braking is: Ek=12mv2E_k=\frac{1}{2}mv^2Ek=21mv2 Where:

   • mmm = Vehicle mass

   • vvv = Vehicle velocity Recovered energy:

     Er=EkE_r = \eta E_kEr=Ek where:

   • ErE_rEr = Recovered energy

   • \eta = Regenerative efficiency

   Optimization objective: max(Er)\max(E_r)max(Er) Subject to:

   SOCminSOCSOCmaxSOC_{min}\leSOC\le SOC_{max}SOCminSOCSOCmax TregenTmotor,maxT_{regen}\leT_{motor,max}Tregen

   Tmotor,max

8. MACHINE LEARNING MODEL

   1. Artificial Neural Network

      1. Inputs

         • Vehicle speed

         • SOC

         • Brake pressure

         • Road slope

      2. Hidden Layers

         • Dense Layer 1 (64 neurons)

         • Dense Layer 2 (32 neurons)

      3. Output

         • Optimal regenerative torque

      4. Activation Functions

         • ReLU

         • Sigmoid

9. SYSTEMS UML DIAGRAMS

10. PYTHON IMPLEMENTATION

    IMPORT NUMPY AS NP

    from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential()

    model.add(Dense(64,

    ACTIVATION=’RELU’, INPUT_SHAPE=(4,)))

    model.add(Dense(32,

    activation=’relu’)) model.add(Dense(1,

    ACTIVATION=’SIGMOID’)) MODEL.COMPILE(

    OPTIMIZER=’ADAM’, LOSS=’MSE’

    )

    PRINT(MODEL.SUMMARY())

11. Experimental Setup

    1. Hardware

       • Electric Vehicle Model

       • Battery Pack (48V400V)

       • BLDC/PMSM Motor

       • Embedded AI Controller

    2. Software

       • MATLAB/Simulink

       • Python

       • TensorFlow

       • OpenAI Gym (RL Training)

    3. Dataset

       Parameters collected:

       • Speed profiles

       • SOC variations

       • Urban driving cycles

       • Highway driving cycles

       • Brake usage paterns

12. PROPOSED SYSTEM DASHOARD

13. GRAPHS

    1: Energy Recovery Efficiency Comparison

    2: EV Driving Range Improvement

    3: Battery State of Charge During Urban Drive Cycle

    4: Braking Torque Distribution

    5: Vehicle Stability Index

    6: Overall Performance Comparison

14. APPLICATIONS

    1. Automotive Industry

       • Passenger EVs

       • Electric Buses

       • Electric Trucks

    2. Smart Transportation

       • Autonomous Vehicles

       • Connected Vehicles

       • Intelligent Mobility Systems

    3. Industrial Vehicles

       • Forklifts

       • Mining Vehicles

       • Automated Guided Vehicles

15. FUTURE SCOPE

    Future research can integrate:

    • Deep Reinforcement Learning

    • Digital Twin Technology

    • Vehicle-to-Grid (V2G)

    • Federated Learning

    • Edge AI Controllers

    • Quantum Optimization Techniques

16. CONCLUSION

This paper presents an AI-driven regenerative braking optimization framework for electric vehicles. By leveraging machine learning, reinforcement learning, and adaptive control techniques, the proposed system significantly enhances energy recovery efficiency while ensuring safety and passenger comfort. Experimental analysis demonstrates notable improvements in vehicle range and battery utilization. The proposed approach offers a practical pathway toward next-generation intelligent electric mobility systems.

REFERENCES

[1]. Electric Vehicle Technology Explained, Wiley, 2024.

[2]. Institute of Electrical and Electronics Engineers, IEEE Transactions on Vehicular Technology, 2023.

[3]. Society of Automotive Engineers, EV Energy Recovery Standards, 2024.

[4]. Zhang, Y., et al., “Deep Learning Based Regenerative Braking Optimization,” IEEE Access, 2023.

[5]. Wang, H., et al., “Reinforcement Learning for Electric Vehicle Energy Management,” Applied Energy, 2024.

[6]. Chen, L., et al., “AI-Assisted Predictive Braking Control,” Energy Reports, 2025.

[7]. Patel, R., et al., “Battery-Aware Intelligent Braking Systems for EVs,” Sustainable Transportation Journal, 2025.