Regenerative Mode Research Articles

Performance analysis of field oriented controlled PMSM based electric truck

The electric vehicle (EV) concept is considered the ideal solution to save the environment from pollution occurring due to internal combustion engines or ICE-based vehicles. However, electric power trains have found more penetration in the segment of passenger vehicles and electric buses. This paper presents the performance of an electric truck. Field-oriented controlled permanent magnet synchronous motor (PMSM) is used for the powertrain of the proposed electric truck. The performance of the proposed electric truck is analyzed for propulsion as well as regenerative mode of operation using MATLAB. The effect of different gradient conditions of the road surface on the behavior of the proposed truck is observed. The presented simulation results depict the satisfactory operation of the proposed PMSM-driven electric truck for various operating conditions.

Chronic anthropogenic disturbance causes prolific resprouting and dwarfing – A case study of a widely distributed subtropical tree

Chronic anthropogenic disturbance (CAD) comprises the additive effect of multiple anthropogenic stressors that lead to subtle but continuous alterations to the structure, composition and functioning of natural ecosystems. However, this effect has been little studied at the tree species level. Here, we examined CAD effects on Chinese beech (Fagus engleriana), a common species in northern subtropical forests that utilizes both vegetative resprouting and seeding as regenerative modes. We computed an index of CAD for each plot based on livestock grazing intensity, wood extraction and miscellaneous resource use. Over 60 20 × 20 m plots, we measured the height and DBH of a total of 1261 Chinese beech individuals (21.02 ± 13.06 per plot, [mean ± SD]), including 593 adults (9.88 ± 4.89), 576 saplings (9.60 ± 8.91) and 92 seedlings (1.53 ± 3.91), and assessed the proportions of each that exhibited resprouting (0.91 ± 0.11, 0.69 ± 0.29, and 0.09 ± 0.25, respectively). CAD was associated with a greater number of individual trees and a proliferation of stems, and with a greater proportion of resprouting seedlings, but had no effect on adult-sized stem DBH. Chinese beech thus appears generally tolerant of CAD, which may ultimately result in its increasing contribution to the maintenance of forest biomass. CAD was also associated with shorter adult and sapling stems, but taller resprouting seedling stems, implying that CAD induced dwarfism as Chinese beech matures. Further analysis showed that the proportion of resprouting was related to small scale wood extraction and miscellaneous resource use, but not to grazing. In the context of better understanding cryptic effects of CAD, our findings show that CAD can alter tree morphology and regenerative processes. These coppicing-type effects of CAD on tree morphology structure are typically associated with higher community biodiversity in managed forest coppice, and future work should investigate the biodiversity consequences of CAD at our study site. Our study shows that CAD has important implications for forest resource utilization, planning, and management, with effects on forest biomass and carbon storage in human-modified landscapes.

Electric vehicle charger energy management by considering several sources and equalizing battery charging

This article proposes a novel energy management structure for electric vehicles, consisting of a supercapacitor and two types of batteries, to improve efficiency and navigable distance. The key features of a suitable energy storage system include high power and energy density, low cost and weight, minimal maintenance, and long life. Although batteries are the primary power storage source in electric vehicles, they have power limitations. Therefore, a high-power-density supercapacitor is added to the battery to create a hybrid energy storage system, reducing stress on the battery and increasing its lifespan. The article presents a new structure for hybrid storage systems based on the existence of a main battery, a replaceable battery, a supercapacitor, and a DC-DC converter. An active charge equalizer circuit for the main battery in standby mode and its control system are also introduced. A control method for dividing power between different sources under different conditions is presented, and the design parameters of the energy storage system are optimized using a genetic algorithm to minimize mass and losses. The performance of the proposed system is evaluated through simulation in MATLAB software and tested in a small-scale laboratory sample. The article also provides a detailed analysis of different working modes, including electric motor acceleration, low-speed mode, high-speed mode, and electric regenerative braking mode. Additionally, a control method for energy management between batteries and supercapacitors is introduced, aiming to increase efficiency. The results show that the proposed hybrid energy storage system can effectively improve the efficiency of electric vehicles.

Overvoltage Avoidance Control Strategy for Braking Process of Brushless DC Motor Drives with Small DC-Link Capacitance

Single-phase input rectifier brushless DC motor drives with a small film capacitor have many advantages, such as high power density and high reliability. However, when the motor system operates in regenerative braking mode, the dc-link capacitor with reduced capacitance may suffer from overvoltage without adding additional hardware circuits. At the same time, the braking torque control of the motor will be affected by speed variations. In order to ensure smooth and reliable operation of the motor system, an anti-overvoltage braking torque control method is proposed in this article. The relationship among the dc-link capacitance, the dc-link capacitor voltage, and the speed during regenerative braking is analyzed quantitatively, and the speed at which the regenerative braking is switched to the plug braking is obtained, which in turn consumes the capacitor energy to avoid dc-link overvoltage. Additionally, based on the relationship between the controllability of the braking torque and the speed, a reference value of the braking current that matches the speed is designed. The proposed method makes use of the capacitor’s energy storage during regenerative braking. Meanwhile, it mitigates the impact of motor speed on braking torque. Finally, the effectiveness of the proposed method is verified on a motor platform equipped with the dc-link film capacitor.

Mode decomposed passivity-based speed control of DC drive with bidirectional Zeta-SEPIC DC-DC converter for light electric vehicles

Currently, light electric vehicles are rapidly developing in various kinds. To power these vehicles with batteries, the simplest electric drive system is a DC motor controlled by a DC-DC converter. This work utilizes a bidirectional Zeta-SEPIC DC-DC converter with an integrated DC motor. This implementation enables control of motor speed and torque in traction and regenerative braking modes. Additionally, it allows for the use of a lower voltage battery compared to the motor's rated voltage, reducing battery weight and increasing safety. In this work, a decomposition approach is applied. Two separate port-controlled Hamiltonian subsystems are obtained to adjust the motor angular velocity in the traction (Zeta) and braking (SEPIC) modes of the DC-DC converter. The Passivity-Based Control (PBC) method is used to synthesize the drive control subsystems in these modes. This method is based on the energy laws of processes in systems and provides asymptotic stability of nonlinear systems, in this case, two fourth-order subsystems for speed control. Two third-order current control subsystems synthesized by the PBC were used to limit the motor current at a given level. The synthesis resulted in sets of possible structures of control influence formers (CIFs) for all PBC subsystems using Zeta and SEPIC DC-DC converters. The study analyzed the operation of the obtained structures of the CIFs, selected the most effective ones, and determined the laws of adaptation of their parameters to the value of the motor angular velocity through computer simulation in Matlab/Simulink. The results of the simulation showed that the drive operated well in both static and dynamic modes.

AI‐enabled bumpless transfer control strategy for legged robot with hybrid energy storage system

AbstractDesigning Hybrid energy storage system (HESS) for a legged robot is significant to improve the motion performance and energy efficiency of the robot. However, switching between the driving mode and regenerative braking mode in the HESS may generate a torque bump, which has brought significant challenges to the stability of the robot locomotion. To address this issue, an AI‐enabled control strategy for bumpless transfer switching is proposed, which is composed of a Proximal Policy Optimisation (PPO)‐based non‐linear active disturbance rejection controller and Deep neural network (DNN)‐based bumpless transfer strategy. We indicate that the proposed intelligent strategy solves bumpless transfer by reducing the torque bump during the system switching process. Meanwhile, the authors analyse the energy driving subsystem and the energy regenerating subsystem based on operational modes and the energy flow. Via the parameters tuning criterion, the authors adopt a PPO algorithm to adaptively tune the parameters of the non‐linear active disturbance rejection controller, which can improve the performance of the primary torque control. A DNN‐based intelligent bumpless transfer strategy is proposed to set the initial value of two switching controllers at a switching moment. Simulations and experimental results validate the effectiveness of the proposed control strategy.

Electric shovel trajectory tracking with inversion sliding mode based on Lyapunov functions

The harsh actual working environment and heavy weight of the electric shovel pose a great challenge to its trajectory tracking performance. Taking into account the strong robustness and anti-interference performance of the sliding mode control method and the global stability of the inversion control method, a Lyapunov-based inversion sliding mode trajectory tracking method is proposed in this paper, and its effectiveness is verified through numerical simulation based on the electromechanical coupling dynamic model of the electric shovel and the actual electric shovel tracking experiment. The results show that the trajectory tracking method can effectively realize the trajectory tracking of the electric shovel accompanied by tracking deviation within 0.3 m. The existence of the track slip phenomenon makes the electric shovel shake when steering, which also makes the actual operating velocity of the electric shovel fluctuate greatly. Meanwhile, the steering process of the electric shovel is accompanied by a regenerative braking steering mode.

MODELLING AND DESIGNING OF E BIKE USING SOLAR PANLES

The rider of an E-bike has the option to rely solely on the motor, pedal while using the motor simultaneously, or pedal only, akin to a conventional bicycle. It's essential that the installation of photovoltaic (P.V.) panels on the electric bicycle does not compromise riding comfort. The idea behind solar energy integration is to equip the bicycle with a high-torque motor powered by solar energy absorbed through portable solar panels. This energy can directly power the motor if it meets requirements; otherwise, a battery serves as backup. During periods of inactivity, such as daytime, the solar panel charges the battery, enhancing the bike's efficiency. This is where electric bicycles shine, offering a green, health-conscious, and swift mode of transportation. Beyond these benefits, electric bicycles can even generate electric power through regenerative pedal power mode. The versatility of an Electric Bicycle extends to various uses. Our goal is to create an Electric Bicycle available at the most affordable rate compared to market prices.

Novel Energy Management Scheme for a Permanent Magnet Electric Drive-Based Hybrid Vehicle Using Model Predictive Control

The present work deals with the design of a traction system for a hybrid vehicle using a PMSM-type motor, including an energy regeneration stage, using a Neutral Point Piloted (NPP) inverter. Optimal operation allows the motor to be supplied in the traction stage with the correct energy in such a way that it operates properly; on the other hand, during braking, it facilitates the transfer of energy in regenerative mode to the DC bus, storing the greatest amount of energy in the storage elements, which, in this case, refers to a battery bank. It also includes a DC-DC bidirectional boost interleaved converter to regulate the DC voltage levels both in traction and in braking. Its fundamental characteristic is that with a reduced number of switching devices, it allows for the reduction in DC voltage of the DC bus with adequate characteristics, constant and stable, regardless of abrupt changes in the output voltage reference value. Also, other features include oscillation control, that is, reducing or increasing oscillations according to operating conditions. Its transient operation is excellent, since the settling time is considerably reduced, which implies that the voltage of the DC bus does not cause mishaps in the operation of the motor. In its operation as a boost converter, in the event of any voltage value of the DC bus, the converter raises the voltage value according to the reference conditions. Similarly, control allows the voltage to be held at a stable value regardless of changes in the set point. This work presents a novel energy management scheme for permanent magnet electric drive based on a Model Predictive Control strategy, thus contributing to the effective energy management of a standard hybrid electric vehicle driving cycle. Results obtained using a real-time Hardware-in-the-Loop (HIL) platform showed the effectiveness of Model Predictive Control in dealing with power flow management while ensuring control of electric drive in all the required speed–torque profiles.

A Fuel-Efficient BLDC Motor-Driven Light Electric Vehicle With Single-Stage Onboard Charging System

This work proposed a simple, compact, and efficient scheme for an on-board charging configuration for light plug-in electric vehicles (LPEVs) with a dual charging arrangement i.e., utility grid and solar panel. The system is targeted to develop an LPEV with a low DC bus voltage (<100V) and on-board solar support using a brushless dc motor (BLDCM) drive due to its economic controller, compact drive arrangement, and high efficiency. The designed integrated converter works well under all operating modes like charging, propulsion (PP), and regenerative braking (RB) operations. The developed converter works as a power factor correction (PFC) converter during grid charging as well as performs as maximum power point tracking (MPPT) converter when charging through solar power. A new approach of the controller in regenerative mode exploiting both the drive and proposed integrated control is unique for this system. As a result, the designed system becomes compact and efficient, making it a competitive solution for an on-board charging scheme for LEVs.

Energy Management Strategy Optimization for a Novel Electric‐hydraulic Power Coupling Vehicle Considering the Influence of Multiple Driving Cycles

Hydraulic hybrid technology can further improve the economy of electric vehicles (EV). In this article, a novel electric‐hydraulic power coupling vehicle (EHPCV), which is endowed with multiple drive modes and energy regenerative braking modes, is investigated. The electric‐hydraulic power coupler (EHPC) is an innovative device for realizing power coupling and conversion. The design and optimization of the energy management strategy (EMS) are key to ensuring the efficient and stable operation of the hybrid electric vehicle. Based on the analysis of energy flow, a rule‐based EMS (RB‐EMS) is built. To achieve more reasonable energy management of the EHPCV in random driving environments, the article proposes an optimization framework for the EMS based on multiple driving cycles. More precisely, a multiobjective optimization mathematical model is established with the goal of maximizing the battery state of charge and minimizing velocity error. Moreover, the optimal Latin hypercube design is used to select the design variables that have a significant impact on the optimization objective. Definitively, the nondominated sorting genetic algorithm II algorithm combines with RB‐EMS to optimize the control parameters. The verification results show that the optimized EMS enables the EHPCV to have superior economic advantages.

Evaluation of the energy efficiency of electric vehicle drivetrains under urban operating conditions

In electric vehicles, as in hybrids vehicles, a very important factor affecting the energy efficiency of the powertrain is the ability to use the regenerative braking energy. Depending on the settings available in electric vehicles, the driver can choose different modes of operation: switch off the regenerative braking mode altogether, select the intensity of regenerative braking, or leave the control system in automatic mode. The last mode is often the only one available on eclectic vehicles, so the driver cannot decide whether to switch off or increase intensity of the regenerative braking. This paper presents a new method for evaluating the energy efficiency of electric vehicle powertrains under urban operating conditions. The presented method uses a procedure for mapping the operating conditions allowing to determine the reference level of energy consumption in relation to those recorded during the identifica-tion tests. Identification tests were carried out in the Tri-City area using electric vehicles of different purposes and operating parameters. Performed tests allowed to evaluate the regenerative braking efficiency of tested vehicle, which varies over a relatively wide range, for vehicle A from 33% to 77%, for vehicle B from 27% to 55% and for vehicle C from 36% to 58%. It can be concluded that one of the main factors determining the regenerative braking efficiency is the level of state of charge of the accumu-lator and the management algorithm used by the vehicle for controlling this parameter.

An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles

This study aims to develop a hybrid energy storage system (HESS), targeting a commercialised Hybrid Electric Vehicle model (Hyundai Sonata), that consists of battery and supercapacitor cells, to evaluate its benefits on the battery's health and vehicle's performance. The different possible configurations of combining the two energy storage devices are discussed, while a semi-active configuration is considered for the purpose of simulation and testing, where the supercapacitor is connected to a bidirectional DC-DC converter, implemented with a newly developed control logic for the HESS. The analysis of the HESS is done by modelling the vehicle powertrain on the simulation software Ricardo IGNITE, while adapting the WLTC Class 2 drive cycle. The results are validated by comparing HESS model to the sole battery model, which showed the working status of the battery is stabilised by the addition of the supercapacitor in the HESS model during both the traction and regenerative modes. There is a significant reduction in the battery peak current, 15.26% and 20.54% for the charge and discharge current, respectively. Other improvements include a 0.43% increase on the average battery SOC, a 6.8% decrease in the battery's maximum temperature and 47.56% decrease in the average battery power. On the other hand, there was a 0.46% increase on vehicle's average L/100 km fuel consumption, due to losses in the supercapacitor system. Various other results are also compared in the discussion section, justifying the addition of the supercapacitor. The investigation finishes off by conducting a parametric study based on the number of supercapacitor modules in parallel, and comparing results such as current, power, fuel consumption and battery SOC. The results show that 2 supercapacitor modules provided the optimal results in terms of battery's current and power behaviour.

Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Neural Networks in the PMSM Drive System

In this paper, a current sensor fault detection mechanism based on multilayer perceptron (MLP) in a permanent magnet synchronous motor (PMSM) drive system is presented. The solution for the PMSM was previously described and tested only in simulation studies. The described application allows the detection of basic faults (lack of signal, gain error, signal noise) in current sensors and the indication of the phase (A or B) in which the fault occurred. The work is focused on the analysis of the fault detector but also presents the possibilities of their classification. The work mainly presents experimental research for different values of speed during the load and regenerative mode. In addition to the study of various operating conditions of the drive system, the detector efficiency was also verified for three neural structures with a different number of neurons in the hidden layers. The work also presents simulation tests (in Matlab Simulink software) for the additional conditions of the drive system for the same neural structures as in the experimental studies. The results obtained during offline and online faults detection with the use of the DS1103 controller are presented.

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.

The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode

Machine learning (ML)-based methods have attracted great attention in the multi-objective optimization problems, which is the key challenge in the energy management strategy (EMS) of the multi-power hybrid system. Our recently published research in this journal verified the effectiveness and feasibility of a Deep Deterministic Policy Gradient (DDPG)-based EMS in the charge-sustaining (CS) stage of a multi-mode plug-in hybrid vehicle (PHEV). However, the application of ML-based-EMS in the charge-depletion (CD) stage and the regenerative braking mode of PHEV are still missing. This study proposes a discrete-continuous hybrid actions-based hierarchical EMS to optimally distribute the dual-motor driving force in battery electric driving and regenerative braking. In the upper layer of EMS, DDPG is trained to learn the torque distribution principles of dual-motor operation to achieve better energy efficiency without losing dynamic performance. Meanwhile, the total recoverable braking torque is also determined by the upper layer EMS considering the braking demand, mechanical and electrical braking system conditions, vehicle safety, and the provisions of law. In the lower level of EMS, the driving mode is determined under the guidance of energy consumption optimization. The verified results show that the proposed EMS outperforms other deep reinforcement learning (DRL)-based hierarchical and non-hierarchical EMSs.

Torsional oscillation suppression-oriented torque compensate control for regenerative braking of electric powertrain based on mixed logic dynamic model

This paper presents a novel torsional oscillation suppression-oriented torque compensate control for regenerative braking (TS-RTC) of electric powertrain, which is based on the gear-pair backlash and half-shaft flexibility-considered dynamic model of regenerative braking in electric powertrain (REP). According to the characteristics of transition from driving mode to regenerative braking mode (DM-to-RM), the dynamic models under different stages are transmitted into an equivalent mixed-logical dynamical (MLD) hybrid model. MLD-based hybrid braking system model and hybrid model predictive control are adopted to design the TS-RTC strategy, in which the torsional oscillation suppression-oriented cost function proved efficient in improving the regenerative braking performance during DM-to-RM process. Simulations and REP hardware in the loop test show that the fluctuation of half shaft torque are efficiently suppressed and the vehicle drivability is improved.

Multi-Chamber Actuator Using Digital Pump for Position and Velocity Control Applied in Aircraft

This paper presents a multi-chamber hydraulic actuator controlled by digital pumps and on/off valves, in order to improve the efficiency of hydraulic systems applied in aircraft for flight control. Hydraulic positioning systems are used in many different applications, such as mobile machinery, industry and aerospace. In aircraft, the hydraulic actuators are used at flight control surfaces, cargo doors, steering, landing gear and so one. However, the massive use of resistive control techniques, which throttles the passages of the hydraulic fluid, associated with internal leakage of the hydraulic components, make these systems low energy efficient. In order to improve their energy efficiency, digital hydraulics emerges as a promising solution mainly for mobile applications. In this paper a hydraulic positioning system for aircraft control surfaces using a multi-chamber actuator controlled by on/off valves and a digital pump is proposed. The use of a digital pump with three fixed displacement pumps can provide eight different volumetric displacement outputs. The multi-chamber actuator with four areas can operate in two different modes, normal or regenerative, resulting in six different equivalent areas. The regenerative mode allows the actuator to achieve higher actuation velocity values with smaller pumps. These equivalent areas combined with the different supplied flow rates can deliver 43 different discrete output velocity values for the actuator, in steady-state. For the system dynamic analyses, three mathematical simulation models were developed using MATLAB/Simulink and Hopsan, one for the digital system, and two for the conventional solutions applied in aircraft (Servo Hydraulic Actuators (SHA) and Electro Hydrostatic Actuator (EHA)). The simulation results demonstrate that the digital actuator can achieve, for position control, a maximum position error, in a steady-state, of 0.7 mm. From the energy consumption point of view, the digital circuit consumes 31 times less energy when compared with the SHA and 1.7 when compared to the EHA, resulting in an energy efficiency of 54%.

Design and Control Method of a Battery/Ultra-Capacitor Energy Storage System for EVs

This paper presents a battery/ultra-capacitor (UC) energy storage system for the operation of permanent magnet synchronous motor drives in electric vehicles (EVs). In this system, when the EV is used for accelerated operation, the battery provides a stable voltage to the inverter through the DC-DC converter. At the same time, the battery and UC jointly release energy to the inverter to share the larger current demand of the EV motor. In constant speed operation, the battery supplies energy to the inverter through a shunt diode to avoid additional power losses while the DC-DC converter is operating. During the regenerative braking mode, the current produced by the EV motor is charged to the UC to reduce pulse currents flowing into the battery, which may shorten battery life. To validate the effectiveness of the circuit design, several operation scenarios including buck and boot voltage conversion for the operation of proposed battery/UC ESS were examined in the experimental studies. Test results consolidates the feasibility and practicality of the circuit method for the EV applications.

Asynchronous Motor in a Mild Hybrid Vehicle

Problem. Strengthening requirements for reducing CO2 emissions requires reducing fuel consumption in cars with internal combustion engines. One of the ways to solve this problem is to equip cars with a hybrid power plant that combines an internal combustion engine and an electric motor. A classic hybrid vehicle requires two energy sources: a fuel tank for the internal combustion engine and a power battery for the electric motor. Therefore, hybrid vehicle has a more complex design and a relatively high cost. A mild hybrid vehicle is a new concept of a hybrid vehicle for urban use that is emerging today. Such MHVs have a simpler design and also allow you to save up to 30% of fuel in urban driving mode. Goal. The purpose of the work is to improve the economic and environmental characteristics of a mild hybrid car, due to the use of an inexpensive asynchronous electric motor with frequency control in the scalar mode, as well as more effective use of power plant control system algorithms. Methodology. Analytical research methods were used to develop algorithms for effective use of the power plant in acceleration, uniform motion, regenerative braking, and stopping modes. Mathematical modeling and calculation methods were used to justify the use of an asynchronous motor for the power plant of a mild hybrid vehicle. Results. The use of a low-power asynchronous motor in the power plant of a mild hybrid vehicle is mathematically justified. The use of the principle of rational amplitude-frequency scalar control is proposed. An algorithm and a scheme for the implementation of an induction motor control system with support for the optimal power factor have been developed. Originality. Instead of using a valve synchronous electric motor with an expensive control system, it is proposed to use an asynchronous motor, which has advantages in the power plant of a mild hybrid vehicle. An asynchronous motor will allow the use of effective algorithms of uniform motion and regenerative braking modes. Practical value. The design of a soft hybrid vehicle with the use of a low-power asynchronous electric motor makes it efficient and cost-effective. The scalar control method of an asynchronous motor, with the slip control, makes it possible to obtain a system with high control quality and lower cost of implementation.