A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Energy recovery strategy for regenerative braking system of intelligent four-wheel independent drive electric vehicles

Regenerative braking system can recovery energy in various electric vehicles. Considering large computation load of global optimization methods, most researches adopt instantaneous or local algorithms to optimize the recuperation energy, and incline to study straight deceleration processes. However, uncertain drivers' intentions limit the potential exploration of economy improvement, and simple test conditions do not reflect the complexity of actual driving cycles. Herein, an innovative control architecture is designed for intelligent vehicles to overcome these challenges to some extent. Compared with traditional vehicles, driverless ones would eliminate drivers' interferences, and have more freedoms to optimize energy recovery, route tracking and dynamics stability. Specifically, a series regenerative braking system is designed, and then a three‐level control architecture is first proposed to coordinate three performances. In the top layer, some rules with maximum recuperation energy is exploited off‐line for optimising the velocity and control commands on‐line. In the middle layer, local algorithm is used to track the commands and complex routes for optimal energy from a global perspective. In the bottom layer, hydraulic and regenerative toques are allocated. Tests are conducted to demonstrate the effectiveness of the design and control schemes

Development of Braking Force Distribution Strategy for Dual-Motor-Drive Electric Vehicle

In the development of the optimal braking force distribution strategy for a dual-motor-drive electric vehicle (DMDEV) with a series cooperative braking system, three key factors were taken into consideration, i.e. the regenerative force distribution coefficient between the front and the rear motor (β), the energy recovery coefficient at the wheels (α3), and the front-and-rear-axle braking force distribution coefficient (λ). First, the overall power loss model of the two surface-mounted permanent magnetic synchronous motors (SMPMSMs) was created based on the d-q axis equivalent circuit model. The optimal relationship of β and the overall efficiency of the dual-motor system were confirmed, where the latter was quite different from that obtained from the traditional look-up table method for the motors' efficiency. Then, four dimensionless evaluation coefficients were used to evaluate braking stability, regenerative energy transfer efficiency, and energy recovery at the wheels. Finally, based on several typical braking operations, the comprehensive effects of the four coefficients on braking stability and energy recovery were revealed. An optimal braking force distribution strategy balancing braking stability and energy recovery is suggested for a DMDEV with a series cooperative braking system

Development of Braking Force Distribution Strategy for Dual-Motor-Drive Electric Vehicle

In the development of the optimal braking force distribution strategy for a dual-motor-drive electric vehicle (DMDEV) with a series cooperative braking system, three key factors were taken into consideration, i.e. the regenerative force distribution coefficient between the front and the rear motor (β), the energy recovery coefficient at the wheels (α3), and the front-and-rear-axle braking force distribution coefficient (λ). First, the overall power loss model of the two surface-mounted permanent magnetic synchronous motors (SMPMSMs) was created based on the d-q axis equivalent circuit model. The optimal relationship of β and the overall efficiency of the dual-motor system were confirmed, where the latter was quite different from that obtained from the traditional look-up table method for the motors\u27 efficiency. Then, four dimensionless evaluation coefficients were used to evaluate braking stability, regenerative energy transfer efficiency, and energy recovery at the wheels. Finally, based on several typical braking operations, the comprehensive effects of the four coefficients on braking stability and energy recovery were revealed. An optimal braking force distribution strategy balancing braking stability and energy recovery is suggested for a DMDEV with a series cooperative braking system

Research and Implement of PMSM Regenerative Braking Control for Electric Vehicle

As the society pays more and more attention to the environment pollution and energy crisis, the electric vehicle (EV) development also entered in a new era. With the development of motor speed control technology and the improvement of motor performance, although the dynamic performance and economical cost of EVs are both better than the internal-combustion engine vehicle (ICEV), the driving range limit and charging station distribution are two major problems which limit the popularization of EVs. In order to extend driving range for EVs, regenerative braking (RB) emerges which is able to recover energy during the braking process to improve the energy efficiency. This thesis aims to investigate the RB based pure electric braking system and its implementation. There are many forms of RB system such as fully electrified braking system and blended braking system (BBS) which is equipped both electric RB system and hydraulic braking (HB) system. In this thesis the main research objective is the RB based fully electrified braking system, however, RB system cannot satisfy all braking situation only by itself. Because the regenerating electromagnetic torque may be too small to meet the braking intention of the driver when the vehicle speed is very low and the regenerating electromagnetic torque may be not enough to stop the vehicle as soon as possible in the case of emergency braking. So, in order to ensure braking safety and braking performance, braking torque should be provided with different forms regarding different braking situation and different braking intention. In this thesis, braking torque is classified into three types. First one is normal reverse current braking when the vehicle speed is too low to have enough RB torque. Second one is RB torque which could recover kinetic energy by regenerating electricity and collecting electric energy into battery packs. The last braking situation is emergency where the braking torque is provided by motor plugging braking based on the optimal slip ratio braking control strategy. Considering two indicators of the RB system which are regenerative efficiency and braking safety, a trade-off point should be found and the corresponding control strategy should be designed. In this thesis, the maximum regenerative efficiency is obtained by a braking torque distribution strategy between front wheel and rear wheel based on a maximum available RB torque estimation method and ECE-R13 regulation. And the emergency braking performance is ensured by a novel fractional-order integral sliding mode control (FOISMC) and numerical simulations show that the control performance is better than the conventional sliding mode controller

Integrated braking control for electric vehicles with in-wheel propulsion and fully decoupled brake-by-wire system

This paper introduces a case study on the potential of new mechatronic chassis systems for battery electric vehicles, in this case a brake-by-wire (BBW) system and in-wheel propulsion on the rear axle combined with an integrated chassis control providing common safety features like anti-lock braking system (ABS), and enhanced functionalities, like torque blending. The presented controller was intended to also show the potential of continuous control strategies with regard to active safety, vehicle stability and driving comfort. Therefore, an integral sliding mode (ISM) and proportional integral (PI) control were used for wheel slip control (WSC) and benchmarked against each other and against classical used rule-based approach. The controller was realized in MatLab/Simulink and tested under real-time conditions in IPG CarMaker simulation environment for experimentally validated models of the target vehicle and its systems. The controller also contains robust observers for estimation of non-measurable vehicle states and parameters e.g., vehicle mass or road grade, which can have a significant influence on control performance and vehicle safety

FUZZY LOGIC CONTROL FOR ENERGY MANAGEMENT SYSTEM OF A HYBRID ELECTRIC VEHICLE

The Hybrid Electric Vehicle (HEV) electric motor is typically powered by a battery pack through power electronics. The fuel consumption in HEV is already lower compared to conventional vehicle. However, there will be a need to control the distribution of torque between the engine and electric motor to further minimize the fuel consumption. With reference to this issues, the purpose of this project is to create a complete HEV using a MATLAB/Simulink tool. From the model created, it will be equipped with a controller for energy management system. The method used is by taking the driver command, the state of charge (SOC) of the battery, the vehicle speed, percentage of throttle and engine efficiency as inputs, a fuzzy logic control for parallel HEV has been developed in a controller to effectively control the torque distribution between Internal Combustion Engine (ICE) and electric motor which is known as In-Wheel Motor (IWM). This research also discusses the methodology for designing a base vehicle model using MATLAB/Simulink. Prior to modelling HEV model, the base vehicle model was validated in terms of the fuel consumption to verify the model. The verified built base model will then be modified to become HEV model by virtually installing IWM at the rear wheels together with a controller inside the trunk. The proposed energy management strategy is implemented on a parallel HEV model and it is then simulated to a selected drive cycles. Since the distribution of torque in HEV model is varied according to the rules set, the fuel consumption is reduced significantly as compared with conventional base vehicle model. The simulation results reveal that, the HEV model built from conventional vehicle model has a significant improvement of 23% in terms of fuel economy as well as maintaining battery SOC within its operation range

Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance

Distributed drive electric vehicles are regarded as a broadly promising transportation tool owing to their convenience and maneuverability. However, reasonable and efficient allocation of torque demand to four wheels is a challenging task. In this paper, a deep reinforcement learning-based torque distribution strategy is proposed to guarantee the active safety and energy conservation. The torque distribution task is explicitly formulated as a Markov decision process, in which the vehicle dynamic characteristics can be approximated. The actor-critic networks are utilized to approximate the action value and policy functions for a better control effect. To guarantee continuous torque output and further stabilize the learning process, a twin delayed deep deterministic policy gradient algorithm is deployed. The motor efficiency is incorporated into the cumulative reward to reduce the energy consumption. The results of double lane change demonstrate that the proposed strategy results in better handling stability performance. In addition, it can improve the vehicle transient response and eliminate the static deviation in the step steering maneuver test. For typical steering maneuvers, the proposed direct torque distribution strategy significantly improves the average motor efficiency and reduces the energy loss by 5.25%–10.51%. Finally, a hardware-in-loop experiment was implemented to validate the real-time executability of the proposed torque distribution strategy. This study provides a foundation for the practical application of intelligent safety control algorithms in future vehicles

Real-Time Optimization Based Power Flow Controller for Energy Consumption and Emissions Reduction in a Parallel HEV

As the regulations on the fuel economy and emissions standards become higher, Hybrid Electric Vehicles (HEV) are gaining more popularity in the market. HEVs improvements in fuel economy and emissions strongly depend on the energy management strategy. An optimization based power flow controller is presented in this thesis to find the appropriate power split between the Internal Combustion Engine (ICE) and the electric motor to reduce the energy consumption and emissions. However, emissions were not taken into consideration in results due to lack of reliable results. A basic power flow controller was built to compare to the optimization based controller. A plant model of each component of the vehicle was built in Simulink to evaluate the performance of each controller. Compared to the basic power flow controller, the real-time energy and emission minimization controller using shift schedule (ReTEEM-SS) reduced the energy consumption by approximately 6.2% in city driving style and 5.4% in highway driving style. The optimization based controller was further modified to replace the shift schedule with a shift logic. The real-time energy and emission minimization controller using shift logic (ReTEEM-SL) reduced the energy consumption by 10.2% in city drive style and 5.3% in highway driver style, when compared to the basic controller