Optimal Regenerative Braking Research Articles

Optimal Regenerative Braking Control Strategy for Electric Vehicles Based on Braking Intention Recognition and Load Estimation

Optimal Regenerative Braking Control Strategy for Electric Vehicles Based on Braking Intention Recognition and Load Estimation

Model based integrated control strategy for effective brake energy recovery to extend battery longevity in electric two wheelers

This study aims to achieve optimal regenerative braking performance in the form of a reduced decline in battery SOC for a BLDC electric machine with peak torque of 10 Nm for use in electric two-wheelers. This is conducted via a comparison of control algorithms based on Direct Look-up table, Fuzzy Logic and their combination with PID control. The whole vehicle model and the energy recovery control strategies are designed using MATLAB Simulink by benchmarking the design with the parameters of the Ola Electric S1. A physical motor-dynamometer test bench is utilised to obtain a complete motor operating range to derive a realistic efficiency map that is used in the model. WLTP Class 2 and NYCC standard drive cycles are implemented for vehicle simulation. Two live-recorded driving patterns are also used to validate the model to analyse the adaptability of the control strategies. After obtaining the required motor speed, torque values and the range by matching the theoretical drive cycle profile, the control strategy is further optimised using the PID auto-tuning toolbox in Simulink. Using physical testbench data, the effect of various regenerative braking control strategies on overall vehicle performance is more accurately realised. The Fuzzy PID control strategy exhibits the most optimal gains in terms of energy recovery for electric two-wheelers, allowing for the highest battery SOC levels of 41.88% and average motor regenerative torque of 7.25 Nm for the standard drive cycles. An analogous trend is observed for the on-road driving pattern as Fuzzy PID provides highest battery SOC and average motor regenerative torque of 48.34% and 7.5 Nm respectively. For the driving scenarios aforementioned, this provides a 17% and 44% increase in SOC respectively when compared to a non-regenerative braking-based system.

Design of LQR-based FLC for the Optimal Regenerative Braking Controlling of Solar PV-based Electric Vehicle System

The article presents an analysis of the optimal control of speed and regenerative braking in an electric vehicle (EV). The presence of the continuous generating power is one of the major challenges for battery charging of electric vehicle. In order to meet the demand of continuous power, electric vehicles are driven by solar PV arrays. Initially, maximum power extraction (MPPT) is assessed from a solar photovoltaic (PV) array using the P&O method. The outputs of the solar PV array are integrated with electric vehicles. Such integration creates the problem of maintaining the power quality. The existing methods also address the issue of optimal speed and torque control with high transients and more settling time. In this article, an electric vehicle is modeled first and then operated in regenerative braking mode. To address the shortcomings of existing methods, the linear quadratic regulator (LQR) method is used; then, fuzzy logic controller (FLC)-based LQR is used for optimal speed and torque control during electric vehicle regenerative braking. In terms of reduced settling time and improved peak overshoot, optimal speed and regenerative torque of electric vehicles are better realized with LQR-based FLCs than with LQR controllers. In addition to this, stopping speed characteristics with various electrical parameters (voltage, current, and flux) have been analyzed with different methods in which LQR-based FLC shows its dominance to attain zero speed in regenerative mode with more sharp characteristics.

A swarm intelligence-based predictive regenerative braking control strategy for hybrid electric vehicle

Braking energy recovery is one of the main technologies affecting the economic performance of an electric vehicle. To improve economy from as much recovered braking energy as possible on the premise of ensuring vehicle security is the goal of regenerative braking control strategy. However, due to the non-linear and multi-objective characteristics of hybrid braking system, finding the optimal regenerative braking control strategy, considering safety, economy, and comfort, remains a challenge. Considering the efficient characteristics of regenerative braking system and battery aging, a swarm intelligence-based predictive regenerative braking control strategy is proposed. Particle swarm optimisation is used as the main part of the strategy, the ant colony algorithm is used to modify the iterative process of particle swarm optimisation to avoid convergence to a locally optimal solution, and model predictive control theory is applied in the control strategy to realise the optimal control. Then, under emergency braking conditions and urban cycling conditions, the stability and economy of proposed strategy are test by the simulation experiments. Finally, to reduce the computational complexity of the control strategy, an equivalent control strategy is proposed based on the nearest point method, and its effectiveness is verified by hardware-in-loop experiment.

Optimal regenerative braking torque of permanent-magnet synchronous motor in electric vehicles

This study investigated a method to determine the optimal regenerative braking torque with maximum energy recovery at given speed, which is essential for the regenerative braking control of electric vehicles. The method is based on the loss modelling of a permanent-magnet synchronous motor (PMSM) system, which consists of electric motor and inverter. The system-loss model is established based on a PMSM mathematical model including iron loss and is used to derive the optimal regenerative braking torque symbolic formula. In addition, the parameter sensitivity of the optimal regenerative braking torque is analysed and a method for online parameter identification is used to obtain the actual parameter values in real time. Thus, the accuracy and adaptability of the optimal regenerative braking torque during the motor-operation process is improved. The analysis results are validated through simulation and bench tests.

Optimal regenerative braking torque of permanent-magnet synchronous motor in electric vehicles

This study investigated a method to determine the optimal regenerative braking torque with maximum energy recovery at given speed, which is essential for the regenerative braking control of electric vehicles. The method is based on the loss modelling of a permanent-magnet synchronous motor (PMSM) system, which consists of electric motor and inverter. The system-loss model is established based on a PMSM mathematical model including iron loss and is used to derive the optimal regenerative braking torque symbolic formula. In addition, the parameter sensitivity of the optimal regenerative braking torque is analysed and a method for online parameter identification is used to obtain the actual parameter values in real time. Thus, the accuracy and adaptability of the optimal regenerative braking torque during the motor-operation process is improved. The analysis results are validated through simulation and bench tests.

Designing a set of efficient regenerative braking strategies with a performance index tool

The goal of this study is to design efficient regenerative braking strategies for a recreational three-wheel rear-wheel-drive hybrid electric vehicle. Current studies provide several optimal regenerative braking strategies, but no tool to obtain a set of acceptable strategies. A performance index tool is thus proposed and used to evaluate the efficiency of a given strategy. With this tool it is then possible to define a set of efficient regenerative strategies. The performance index is based on knowledge of a global efficiency map defined as the ratio of the incoming battery power to the extracted kinetic power. Two simulators of the vehicle are implemented in MATLAB/Simulink: one with a rear-wheel slip model and the other without slip considerations. They also include the longitudinal dynamics of the vehicle and the efficiency of the electrical drive from the electric motor to the battery. They were validated with experimental measurements of several accelerations and decelerations on a dry asphalt road from 0 km/h to 60 km/h. The simulated global efficiency map was also experimentally validated by regenerative braking measurements on a dry asphalt road from 50 km/h to 0 km/h. The simulated global efficiency map is used to design the optimal strategy (with and without wheel slip considerations). The performance map deduced from the global efficiency map was used to define the boundaries for the optimal strategy deviations and hence to limit the regenerated energy drop. Simulations show that there is a wide range of acceptable strategies from 0 km/h to 50 km/h on a dry asphalt road and hence give the driver the possibility of modulating the regenerative braking within a good energy recapture level. Finally, the design methodology presented with a simulated global efficiency map is also applicable with an experimental efficiency map which can be updated online.

Instantaneous optimal regenerative braking control for a permanent-magnet synchronous motor in a four-wheel-drive electric vehicle

Recovering the kinetic energy of a vehicle is one inherent advantage of an electric vehicle. A permanent-magnet synchronous motor is widely adopted for the traction motor in an electric vehicle with the advantage of a high efficiency and a high torque density. The principle for electric braking control of the permanent-magnet synchronous motor under field-oriented control is studied. The efficiency model of the electric drive system, which is different from that of the internal-combustion engine drive system, can be exactly described by analytical equations. On this basis, the battery power can be expressed as a function of the angular velocity and the electromagnetic torque of the motor. By solving the partial differential equation for the battery power, the instantaneous optimal regenerative braking torque of the permanent-magnet synchronous motor is simply calculated according to the vehicle braking torque demand and the motor speed. Compared with the existing efficiency map method, the analytical technology is easily implemented. Then a four-wheel-drive electric vehicle is investigated to achieve optimal regenerative braking control. The dynamic behaviour of braking in the four-wheel-drive electric vehicle is also considered. The parallel braking pattern and the series braking pattern are investigated in order to evaluate the availability of braking energy recovery. The instantaneous optimal regeneration energy can be recovered for the series braking system, and a significant amount of energy can be recovered for the parallel braking system by adjusting the free travel of the brake pedal.

Comparison of two strategies for optimal regenerative braking, with their sensitivity to variations in mass, slope and road condition

A three wheels recreational vehicle is studied for the purpose of regenerative braking control. Most of the existing literature propose optimal methods which consist in defining the braking torque as a function of the vehicle speed. The originality of this study is to propose a new strategy based on the control of the rear wheel slip. A simulator based on Matlab/Simulink and validated with experimental measurements compares the two strategies and their sensitivities to variation in mass, slope and road condition. Numerical simulations show that the regenerative braking based on a slip controller is less affected by parametric changes. It thus becomes possible to ensure optimal recovery of energy and vehicle stability even in case of parametric fluctuations.

Optimal brake torque distribution for a four-wheeldrive hybrid electric vehicle stability enhancement

Vehicle stability control logic for a four-wheel-drive hybrid electric vehicle is proposed using the regenerative braking of the rear motor and an electrohydraulic brake (EHB). To obtain the optimal brake torque distribution between the regenerative braking and the EHB torque, a genetic algorithm is used. The genetic algorithm calculates the optimal regenerative braking torque and the optimal EHB torque for the given inputs of the desired yaw moment and road friction coefficient. Based on the optimal brake torque distribution, the vehicle stability control logic proposed generates the desired direct yaw moment to compensate the errors of the side-slip angle and yaw rate by a fuzzy control algorithm corresponding to the driver's steering angle and vehicle velocity. Performance of the vehicle stability control logic is evaluated by comparison of the fixed regenerative braking and the optimal regenerative braking for a single lane change manoeuvre. It is found from the simulation results that the optimal regenerative braking is able to provide the increased recuperation energy compared with the fixed regenerative braking while satisfying the vehicle stability.