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Adaptive cruise control for electric vehicles using hybrid-mode MPC.
Ahmed E Sharkawy1, Ahmed M Ali2, Mostafa Sh Asfoor1
1Automotive Engineering Department, Military Technical College, I. Fangari, Cairo, 11766, Egypt.
Scientific Reports
|June 5, 2026
Summary
This study introduces a unified platform for optimizing Adaptive Cruise Control (ACC) in electric vehicles (EVs). The intelligent system enhances safety and control by tuning model parameters and control layers for improved driving performance.
Area of Science:
- Automotive Engineering
- Control Systems
- Artificial Intelligence
Background:
- Electromobility necessitates advanced safety and control systems like Advanced Driver Assistance Systems (ADAS).
- Integrating ADAS in electric vehicles (EVs) faces challenges in modeling complex drivelines and control architectures.
- Existing systems struggle with robust control formulation and parameter tuning for hybrid/electric powertrains.
Purpose of the Study:
- To present a novel, unified methodology for optimizing Adaptive Cruise Control (ACC) in electric vehicles (EVs).
- To develop an intelligent Model Predictive Control (MPC) system for enhanced vehicle safety and performance.
- To address challenges in driveline modeling and control system integration for EVs.
Main Methods:
- A single-platform solution was developed for tuning model parameters and online optimization of ACC control layers.
- An intelligent Model Predictive Control (MPC) approach was implemented using decentralized control modes (cruising, spacing, braking).
- A unified prediction model provided real-time, look-ahead estimation of driving situations, validated via experimental testing on a real EV.
Main Results:
- The unified platform successfully maintained speed-tracing and precise spacing under various disruptive scenarios.
- Experimental testing on a chassis dynamometer demonstrated the system's efficacy in diverse driving conditions.
- Achieved high tracking accuracy: 98% for cruise control, 87.8% for spacing control, and 55.0% for braking control.
Conclusions:
- The proposed methodology offers a significant, unified solution for complex EV driveline modeling and control.
- The intelligent MPC system effectively mitigates computational and technical difficulties in EV control system design.
- This work advances the integration of ADAS in EVs, enhancing safety and intelligent vehicle control.
Related Concept Videos
01:19Root-Locus Method
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block diagram,...
This system can be represented by a block diagram,...
01:22Controller Configurations
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller aligns...
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller aligns...
01:26PD Controller: Design
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
01:22Multi-input and Multi-variable systems
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...
01:13Motor Units
The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
Motor units come in different sizes, with smaller units...
00:46Motor Units
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.