Regenerative Braking Strategy Research Articles

Research on Brake Energy Recovery Strategy Based on Working Condition Identification

As a transitional vehicle between fuel and electric vehicles, hybrid vehicles achieve energy savings and emission reductions without range anxiety. Regenerative braking has a direct impact on the fuel consumption of the whole vehicle; however, the current regenerative braking strategy for commercial vehicles is not yet perfect and has a poor adaptability in terms of working conditions and whole-vehicle load changes. Therefore, this paper proposes a regenerative braking strategy based on the identification of working conditions, by considering the influence of the vehicle load state and driving conditions on braking. Firstly, historical driving data of commercial vehicles were obtained from GPS data, driving conditions were classified using principal component analysis (PCA) and K-means, and a working condition recogniser was constructed using a back propagation neural network (BPNN) optimised with the Coati optimisation algorithm (COA). The recognition accuracy of the COA-BPNN was 7.6% better than that of the BPNN. Secondly, front and rear axle braking force distribution strategies are proposed, according to the braking intensity magnitude and load state under empty-, half-, and full-load conditions. Finally, a genetic algorithm (GA) was used to find the optimal control parameters for each category of working conditions, and the COA-BPNN condition recogniser identified the current category of working conditions needed to retrieve the corresponding optimal control parameters in the offline parameter library. The simulation results under C-WTVC and synthetic conditions show that the energy recovery rate of the proposed strategy in this paper reached up to 69.65%, which is at most 206.3% higher than that of the fixed-ratio strategy and at most 37.4% higher than that of the fuzzy control strategy.

A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

With increasing global attention to climate change and environmental sustainability, the sustainable development of the automotive industry has become an important issue. This study focuses on the regenerative braking issues in pure electric vehicles. Specifically, it intends to elucidate the influence of the braking force distribution of the front and rear axles on access to energy recovery efficiency. Combining the I curve of a pure electric vehicle and the boundary line of the Economic Commission of Europe (ECE) regulations, the braking force distribution relationship between the front and rear axles is formulated to satisfy braking stability. The maximum regenerative braking force of the motor is determined based on the motor torque characteristics and battery charging power, and the regenerative braking torque is optimized by combining the constraints of the braking strength, battery state of charge (SOC), and vehicle speed. Six road working conditions are built, including the New European Driving Cycle (NEDC), the World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), the China Light-Duty Vehicle Test Cycle—Passenger (CLTC-P), and the New York City Cycle (NYCC). The efficiency of the regenerative braking strategy is validated by using the Simulink/MATLAB simulation. The simulation results show that the proposed dynamic logic threshold control strategy can significantly improve the energy recovery effect of electric vehicles, and the energy recovery efficiency can be improved by at least 25% compared to the situation without regenerative braking. Specifically, under the aforementioned road working conditions, the braking energy recovery efficiency levels are 27.69%, 42.18%, 49.54%, 47.60%, 49.28%, and 51.06%, respectively. Moreover, the energy recovery efficiency obtained by the current dynamic logic threshold is also compared with other published results. The regenerative braking control method proposed in this article makes the braking control of electric vehicles more precise, effectively reducing energy consumption and improving the driving range of electric vehicles.

A Bidirectional Control Regenerative Braking Strategy for EV Applications- A Review

In this paper, Review on bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for a two-wheeler application. To reduce the harmful emissions from automobiles and massive surges in fuel prices, automotive electric vehicles are an effective alternate solution. In this paper, a cascaded bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for a two-wheeler application. The dual control strategy with the cascaded converter is used to increase the average power stored during the braking period and to reduce the vehicle’s stopping time. The converter with the proposed control strategy used in this work has made it possible to charge the battery even when the back emf of the machine is less than the battery voltage. A fuzzy logic control strategy is used to consider the non-linear factors like SOC, speed of the vehicle and the required brake force. This is done in order to make the system more reliable and realistic. implementing the dual control strategy, the average power stored by the battery is increased by 2.5 times and the vehicle comes to halt faster in comparison with the existing control strategy. The versatility of the strategy is shown by examining three different scenarios during the regenerative braking process

Regenerative braking energy recovery control strategy for electric vehicles with battery temperature measurement

Aiming at the problems of short mileage and overheating and easy damage of the battery in electric vehicles, the fuzzy control regenerative braking strategy with vehicle speed, braking intensity, battery state of charge (SOC), and the introduction of battery temperature as fuzzy control input and regenerative braking distribution percentage as output are proposed; secondly, the ideal braking force distribution curve and ECE regulations are used as the boundary to determine the braking force distribution range of pure electric vehicles. Finally, the control strategy designed in this paper is simulated under selected working conditions by ADVISOR software, and the effectiveness and superiority of this control strategy are verified. The strategy can not only reduce the damage to the battery caused by high temperatures but also obtain more braking energy.

Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles

In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.

Research of Regenerative Braking Strategy for Electric Vehicles

In the context of global energy instability caused by the transformation of global demand for energy and energy resources, one of the most important areas in the automotive industry is the development of electric vehicles. Serial production of high-tech electric vehicles with a long range contributes to the stabilization of the energy market and the sustainable development of the whole fuel-energy sector. To evaluate the possibility of optimizing the electric vehicles energy consumption, various regenerative braking strategies are discussed in the article based on the Nissan Leaf electric vehicle, which simulation model includes submodules of the traction electric motor, hybrid braking system, traction rechargeable battery and tires. In order to test the adequacy of the simulation model to reproduce the relationship between the operating parameters of electric vehicles various systems and evaluate their ability to regenerate energy during braking the simulation results were compared with the actual experimental data published by the Lab Avt research laboratory (USA). The relative error of the mathematical modeling results of the braking energy regeneration processes is 4.5 %, which indicates the adequacy of the electric vehicle simulation model and the possibility of its using as a base for research and comparison of the energy efficiency of various regenerative braking strategies. As the results of experiments have shown, the usage of the proposed control strategy of the regenerative braking maximum force allows increasing 2.14 times the energy recharging traffic to the battery as compared with the basic control strategy of fixed coefficient braking forces distribution with an increase in braking distance by 10 m. An alternative control strategy of regenerative braking optimal efficiency as compared to the basic control strategy provides a reduction in braking distance by 13.2 % at increasing by 84.4 % the amount of energy generated by the electric motor for recharging the batteries. The carried out investigations confirm the available significant potential for improving the efficiency of the electric vehicles usage by developing the control strategy and algorithms of the braking energy regeneration.

Integrated hydrogen and battery energy systems as emergency backup in electric trains

Moving towards sustainable transport by adopting alternative energy sources to traditional ones is, in these times more than ever, of fundamental importance for humanity. In this paper, the possibility of using fuel cell- and/or battery-based energy systems to replace the Diesel engine of a conventional electric train (the Hitachi Blues), is studied.Four different system configurations are proposed, all satisfying the common characteristic of being perfectly placed in rooms previously occupied by Diesel engines. Their sizing, layout and weight have been thoroughly discussed in the paper.Furthermore, to analyze the application limits of these configurations, their installation on an electric train running along a real railway line in the Calabria region (Italy) was simulated. To size the components and define the layout of the plant, computer-aided design software (CAD 3D) was used, starting from the original construction drawings of the railway carriages. Numerical and dynamic zero-dimensional simulations were implemented to evaluate the performance rating of the systems. Among the tested powertrains, two of the proposed configurations (a full-battery and a hybrid fuel cell-battery systems) have proved to be more than adequate to replace the overhead line, reaching an average driving range of about 100 km. Moreover, two different overhead line malfunction scenarios were assumed to evaluate the train performance. In each case, the innovative powertrains allow the train to travel the entire journey. In detail, the overhead line provides more than half of the energy, while the fuel cell reaches almost 25 % of the overall energy demand, consuming the whole amount of hydrogen stored on board, approximately 24 kg. The battery supplies a maximum of 545 kWh (roughly 40 %), conserving a 45 % State of Charge, and up to 165 kWh (over 10 %) can be recovered through regenerative braking strategies.

Analysis of Regenerative Braking Strategy for Electric Vehicles Based on Braking Intention

Improved energy recovery during braking is the goal of the distributed drive electric car regenerative braking system. A regenerative braking control strategy for electric vehicles based on braking intention is suggested in order to address various drivers’ braking intents and road adhesion circumstances. An electric vehicle regenerative braking model for distributed drives is created using the MATLAB/Simulink tools. On the basis of FTP-75 operating conditions and open road operating conditions, the efficacy and economy of the control strategy in this study are verified. According to the various road adhesion coefficients, the simulation results demonstrate that the control method in this work can appropriately divide the braking torque of the wheels on both sides of the coaxial wheel. The technique can successfully enhance the regenerative braking recovery of distributed drive electric vehicles as compared to the often employed regenerative braking control method.

Regenerative Braking Strategy of Dual-Motor EV Considering Energy Recovery and Brake Stability

The dual-motor EV (Electric Vehicle) is increasingly favored by manufacturers for its excellent performance in terms of power and economy. How to further reduce its energy consumption and make full use of the dual-motor energy recovery is an important support to improve the overall vehicle economy and realize the “dual carbon” strategy. For the dual-motor EV architecture, the motor model, power battery loss model and vehicle longitudinal braking force model are established and the energy recovery-dominated regenerative braking torque distribution (RBD) rule of the dual motors is designed. Based on genetic algorithm (GA) theory and taking into account SOC, vehicle speed and braking intensity, a regenerative-braking torque optimization method is proposed that integrates energy recovery and braking stability. The braking intensity of 0.3 and the initial vehicle speed of 90 km/h are selected for verification. Compared with the rule method, the energy recovery and stability are improved by 22.8% and 4.8%, respectively, under the genetic algorithm-based and energy recovery-dominated regenerative-braking torque distribution (GA-RBD) strategy. A variety of conditions are selected for further strategy validation and the result shows that compared with the rule-based method, both energy recovery and braking stability are improved as braking speed and braking intensity increase under the GA-RBD strategy.

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

The networking of microgrids has received significant attention in the form of a smart grid. In this paper, a set of smart railway stations, which is assumed as microgrids, is connected together. It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid. Also, the operational costs of stations under various conditions decrease by applying the proposed method. The smart railway stations are studied in the presence of photovoltaic (PV) units, energy storage systems (ESSs), and regenerative braking strategies. Studying regenerative braking is one of the essential contributions. Moreover, the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method. The networked microgrid scheme of railway stations (based on coordinated operation and scheduling) and independent operation of railway stations are studied. The proposed method is applied to realistic case studies, including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company (TUSROC). The rolling stock is simulated in the MATLAB environment. Thus, the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming (MILP).

An Adaptive Regenerative Braking Strategy Design Based on Naturalistic Regeneration Performance for Intelligent Vehicles

An Adaptive Regenerative Braking Strategy Design Based on Naturalistic Regeneration Performance for Intelligent Vehicles

Efficient Regenerative Braking Strategy Aimed at Preserving Vehicle Stability by Preventing Wheel Locking

This paper presents a regenerative braking logic to be adopted on full electric vehicles with front, rear-drive or all-wheel drive with one motor for each axle, which aims at maximizing energy recovery under braking, avoiding wheel locking thus preventing vehicle instability. The logic implies the adoption of a brake-by-wire system i.e., the hydraulic braking system can be activated independently from the brake pedal. As a matter of fact, with the pedal pressed, the logic gives priority to the braking action of the electric motor(s) which acts as a generator, thus maximizing energy recovery, however taking into account various limitations, including the wheel locking limit, ensuring the stability of the vehicle. When the electric motor cannot satisfy the regenerative torque request, braking is integrated with the help of the hydraulic brakes, whose contribution aims to bring the braking towards a condition of optimal braking distribution. The front and rear hydraulic systems must therefore be independent of each other and controllable separately. This logic was tested via simulation, and it emerged that, on the WLTC driving cycle, the logic saved about 30% in consumption compared to the same vehicle without regenerative recovery, and about 23% compared to a logic commonly adopted on the market. On cycle US06, it saves about 24% and 19%, respectively.

A Dual Control Regenerative Braking Strategy for Two-Wheeler Applications-A Review

In this paper, Review on bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for a two-wheeler application. To reduce the harmful emissions from automobiles and massive surges in fuel prices, automotive electric vehicles are an effective alternate solution. In this paper, a cascaded bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for atwo-wheeler application. The dual control strategy with the cascaded converter is used to increase the average power stored during the braking period and to reduce the vehicle’s stopping time. The converter with the proposed control strategy used in this work has made it possible to charge the battery even when the back emf of the machine is less than the battery voltage. A fuzzy logic control strategy is used to consider the non-linear factors like SOC, speed of the vehicle and the required brake force. This is done in order to make the system more reliable and realistic. implementing the dual control strategy, the average power stored by the battery is increased by 2.5 times and the vehicle comes to halt faster in comparison with the existing control strategy. The versatility of the strategy is shown by examining three different scenarios during the regenerative braking process.

Service-Oriented Real-Time Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles

This paper presents a real-time vehicle speed planning system called the real-time energy-optimal deceleration planning system (RT-EDPS). Connectivity and autonomous driving technologies that provide map and navigation data, traffic light information, and front detection sensor data are exploited to perceive and forecast upcoming deceleration events. A parameterized polynomial-based deceleration model is employed as the deceleration strategy. Real driving test data that characterize the physical limits of regenerative braking are used to model the objective function and constraints of the parameterized deceleration commands. The proposed RT-EDPS involves two speed-planning strategies with different scales of the planning horizon. Within a deceleration event horizon ascertained by the signal phase and timing data of traffic signals and 3D geographic map data, a dynamic programming-based energy-efficient deceleration strategy is used to generate long-sighted speed profiles. While the scheduled vehicle speed is being tracked, if a preceding vehicle is detected within a predefined threshold range, then a model predictive control-based energy-efficient deceleration strategy that incorporates the traffic status ahead of the ego vehicle and exploits V2V communication with the preceding vehicles is activated to re-plan the speed trajectory of the vehicle to guarantee a collision-free distance for the preceding vehicle. In virtual driving experiments, we record energy-recuperation efficiency gains of over 40% as compared to human drivers.

A Dual Control Regenerative Braking Strategy for Two-Wheeler Applications with Different Modes of Operation -A Review

In this paper, Review on bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for a two-wheeler application. To reduce the harmful emissions from automobiles and massive surges in fuel prices, automotive electric vehicles are an effective alternate solution. In this paper, a cascaded bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for a two-wheeler application. The dual control strategy with the cascaded converter is used to increase the average power stored during the braking period and to reduce the vehicle’s stopping time. The converter with the proposed control strategy used in this work has made it possible to charge the battery even when the back emf of the machine is less than the battery voltage. A fuzzy logic control strategy is used to consider the non-linear factors like SOC, speed of the vehicle and the required brake force. This is done in order to make the system more reliable and realistic. implementing the dual control strategy, the average power stored by the battery is increased by 2.5 times and the vehicle comes to halt faster in comparison with the existing control strategy. The versatility of the strategy is shown by examining three different scenarios during the regenerative braking process.

Feasibility study of a diesel-powered hybrid DMU

Nowadays, the interest in hybrid vehicles is constantly increasing, not only in the automotive sector, but also in other transportation systems, to reduce pollution and emissions and to improve the overall efficiency of the vehicles. Although railway vehicles are typically the most eco-friendly transportation system, since commonly their primary energy source is electricity, they can still gain benefits from hybrid technologies, as many lines worldwide are not electrified. In fact, hybrid solutions allow ICE-powered (internal combustion engine) railway vehicles, such as diesel multiple units (DMUs), to operate in full-electric mode even when the track lacks electrification. The possibility to switch to full electric mode is of paramount importance when the vehicle runs on urban or underground track sections, where low or zero emission levels are required. We conduct the feasibility study of hybridization of an existing DMU vehicle, designed by Blue Engineering S.r.l., running on the Aosta–Torino Italian railway line, which includes a non-electrified urban track section and an electrified underground section. The hybridization is obtained by replacing one of the diesel generators installed on the original vehicle with a battery pack, which ensures the vehicle to operate in full-electric mode to complete its mission profile. The hybridization is also exploited to implement a regenerative braking strategy, which allows an increase in the energetical efficiency of the vehicle up to 18%. This work shows the sizing of the battery pack based on dynamic simulations performed on the Turin underground track section, and the results demonstrate the feasibility of the hybridization process.

Regenerative Braking Control Strategy of Electric Vehicles Based on Braking Stability Requirements

Electric vehicles are effective way to solve energy and environmental problems, but the promotion and application of electric vehicles are suppressed by their limited endurance range seriously. The regenerative braking technology is an important method to increase the endurance range of the electric vehicle. During the braking process, the kinetic energy of the electric vehicle can be converted into electric energy and stored in the energy source device with the regenerative braking system, so the endurance range of the electric vehicle can be increased accordingly. In order to increase the efficiency of energy recovery, a regenerative braking strategy with the optimization distribution algorithm is proposed in this paper, and the braking forces of the front and rear axles are distributed optimally with variable ratios based on the braking strength. With the optimal braking force distribution ratio and related constraint conditions, the regenerative braking control strategy was designed to meet the braking stability and the maximum braking energy recovery. And then a simulation model of the braking control strategy was built with MATLAB/Simulink software, and the simulation tests on UDDS and NEDC cycle conditions were done to verify the effectiveness of the designed regenerative braking control strategy. Compared with the control strategy of ADVISOR software, the braking energy recovery efficiency was improved more than 51.9 % while maintaining the braking stability.

Safe and Energy-Efficient Car-Following Control Strategy for Intelligent Electric Vehicles Considering Regenerative Braking

Intelligent electric vehicles (IEVs) have attracted more and more attention benefitting from the characteristics of high degree of safety and energy efficiency. This paper proposes an adaptive cruise control framework considering regenerative braking to improve safety and energy efficiency of IEVs during the car-following process. At first, a coupled and nonlinear dynamic model of IEVs system is constructed, which is mainly composed of a powerful battery, an electric motor, a single-speed transmission, and a hydraulic braking system. Then, an adaptive fuzzy sliding mode high-level controller is designed to accurately obtain the desired longitudinal acceleration of IEVs, in which the fuzzy logic is utilized to approximate the switching control item of the sliding mode control for chattering free. And the stability of high-level controller is proven by the Lyapunov theory. In the lower-level controller, traction control and brake control are designed to track the desired acceleration produced by the high-level controller, in addition, a novel regenerative braking strategy is presented to maximize the braking energy recovery. Finally, the simulation results indicate that the proposed control scheme has the excellent performance of longitudinal tracking and braking energy recovery with no loss of safety.

Research on regenerative braking strategies for hybrid electric vehicle by co-simulation model

Regenerative braking is an important factor in improving hybrid electric vehicle (HEV) fuel economy. This paper presents the simulation modelling of a power-split hybrid electric vehicle with different regenerative braking strategies. A co-simulation model is used to enhance the simulation capability for the hybrid vehicle performance and development of control strategy. AMESim is used to model the complex physical components including engine, transmission, motors, battery and hybrid vehicle, and the physical model is integrated with control model established by MATLAB/Simulink, which is required to operate the vehicle and the regenerative braking system through standard drive cycles. Simulation results show that a regenerative braking control strategy can recuperate significant amounts of energy. Vehicle fuel economy in EV and HEV modes is improved significantly by coupling the proposed regenerative braking strategy.

Parameterized Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles: A Real-Time Dynamic Programming Approach

This paper presents a vehicle speed planning system called the energy-optimal deceleration planning system (EDPS), which aims to maximize energy recuperation of regenerative braking of connected and autonomous electrified vehicles. A recuperation energy-optimal speed profile is computed based on the impending deceleration requirements for turning or stopping at an intersection. This is computed to maximize the regenerative braking energy while satisfying the physical limits of an electrified powertrain. In automated driving, the powertrain of an electrified vehicle can be directly controlled by the vehicle control unit such that it follows the computed optimal speed profile. To obtain smooth optimal deceleration speed profiles, optimal deceleration commands are determined by a parameterized polynomial-based deceleration model that is obtained by regression analyses with real vehicle driving test data. The parameters are dependent on preview information such as residual time and distance as well as target speed. The key design parameter is deceleration time, which determines the deceleration speed profile to satisfy the residual time and distance constraints as well as the target speed requirement. The bounds of deceleration commands corresponding to the physical limits of the powertrain are deduced from realistic deceleration test driving. The state constraints are dynamically updated by considering the anticipated road load and the deceleration preference. For validation and comparisons of the EDPS with different preview distances, driving simulation tests with a virtual road environment and vehicle-to-infrastructure connectivity are presented. It is shown that the longer preview distance in the EDPS, the more energy-recuperation. In comparison with driver-in-the-loop simulation tests, EDPS-based autonomous driving shows improvements in energy recuperation and reduction in trip time.