Control Strategy For Electric Vehicles Research Articles

Development of Optimization Based Control Strategy for P2 Hybrid Electric Vehicle including Transient Characteristics of Engine

Models based on steady-state maps estimate fuel consumption to be 2–8% lower than real experimental measured values. This is due to the fact that during transient phases, the engine consumes more fuel than in steady phases. Some literature has addressed the conventional vehicle engine model that improves fuel consumption estimation’s accuracy during the transient state. However, the characteristics of the engine in the scope of hybrid electric vehicles (HEVs) with an integrated control strategy is yet to be covered. The controller is designed to minimize engine operation in the transient phase to enhance energy savings. In this paper, the correlation between fuel enrichment in transient and steady-state fuel estimation is established as transient correction factor (TCF). Its explanatory variable was the engine torque change rate. This paper describes the influence of engine transient characteristics on the fuel consumption of a mild HEV. The work attempts to improve the fuel economy of the HEV by introducing a penalty factor in the controller to optimize the use of the engine in transient regimes. A backward vehicle model was developed for a production vehicle with a conventional powertrain and validated experimentally using data available online. The corresponding hybrid vehicle model was developed by integrating the electric motor and battery components with the conventional vehicle model. A P2 off-axis configuration was chosen to this end as the HEV architecture. A conventional equivalent consumption minimization strategy (ECMS) was used to split the torque request between the engine and the electric motor. This control strategy was modified with TCF to penalize the engine torque change rate. The results of the simulation show that due to less transient operation of the engine, the fuel consumption was reduced from 923 g to 918 g under the US06 driving cycle. The fuel economy of the model has been simulated for UDDS and HW drive cycles too, and fuel consumption improved by 4.4 g and 3.2 g, respectively. It has been verified that by increasing the battery capacity twice (14s24p), the limitations imposed by the battery capacity can be minimized and the fuel usage can be reduced by 9 g in the UDDS cycle.

A Novel Learning-Based Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

The multisource electromechanical coupling renders the energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions and hinders the control strategies efficiently applied instantaneously, leading to massive challenges in energy-saving improvement of PHEVs. To address these issues, a novel learning-based model predictive control (LMPC) strategy is developed for a serial–parallel PHEV with the reinforced optimal control effect in real-time applications. Rather than employing the velocity-prediction-based MPC methods favored in the literature, an original reference-tracking-based MPC solution is proposed with strong instant application capacity. To guarantee the optimal control effect, an online learning process is implemented in MPC via the Gaussian process (GP) model to address the uncertainties during state estimation. The tracking reference in the LMPC-based control problem in PHEV is achieved by a microscopic traffic flow analysis (MTFA) method. The simulation results validate that the proposed method can optimally manage energy flow within vehicle power sources in real time, highlighting its anticipated preferable performance.

A drive system global control strategy for electric vehicle based on optimized acceleration curve

The existing researches of electric vehicle (EV) drive system mainly focus on the motor itself, without considering the EV energy consumption and battery life simultaneously. Thence, in this paper, a control method of EV drive system based on its energy consumption and battery life is proposed. Combining the EV driving state with the motor control, the relationship between the velocity and acceleration and the optimal dq-axis currents is established. Then, a drive system global control strategy for EV based on optimized acceleration curve is proposed, in which the maximum torque per ampere (MTPA) control is used below motor base speed and the voltage closed-loop feedback flux weakening control is used above motor base speed. Furthermore, the proposed control strategy is verified by both MATLAB/Simulink simulation and experiment. The simulation and experimental results show that compared with original working conditions, the energy consumption per kilometer and the percentage of battery capacity loss per kilometer of the global control strategy based on optimized acceleration curve are both reduced, which verifies the feasibility and effectiveness of the proposed control strategy.

Robust Speed Tracking Control for Future Electric Vehicles under Network-Induced Delay and Road Slope Variation.

Integrated motor-transmission (IMT) powertrain systems are widely used in future electric vehicles due to the advantages of their simple structure configuration and high controllability. In electric vehicles, precise speed tracking control is critical to ensure good gear shifting quality of an IMT powertrain system. However, the speed tracking control design becomes challenging due to the inevitable time delay of signal transmission introduced by the in-vehicle network and unknown road slope variation. Moreover, the system parameter uncertainties and signal measurement noise also increase the difficulty for the control algorithm. To address these issues, in this paper a robust speed tracking control strategy for electric vehicles with an IMT powertrain system is proposed. A disturbance observer and low-pass filter are developed to decrease the side effect from the unknown road slope variation and measurement noise and reduce the estimation error of the external load torque. Then, the network-induced delay speed tracking model is developed and is upgraded considering the damping coefficient uncertainties of the IMT powertrain system, which can be described through the norm-bounded uncertainty reduction method. To handle the network-induced delay and parameter uncertainties, a novel and less-conservative Lyapunov function is proposed to design the robust speed tracking controller by the linear matrix inequality (LMI) algorithm. Meanwhile, the estimation error and measurement noise are considered as the external disturbances in the controller design to promote robustness. Finally, the results demonstrate that the proposed controller has the advantages of strong robustness, excellent speed tracking performance, and ride comfort over the current existing controllers.

Power reserve predictive control strategy for hybrid electric vehicle using recognition-based long short-term memory network

For series hybrid electric vehicle (SHEV), due to the limitation of operating characteristic of engine, it is challenging to design an efficient power control strategy that can ensure instantaneous response of engine-generator set (EGS) to the sudden increase in demand power. In this paper, a power reserve predictive control strategy for SHEV is proposed. Firstly, a driving pattern recognition-based long short-term memory network is developed to predict future demand power. An improvement in the accuracy of this prediction is achieved by using a new time-series changing structure. Secondly, a novel method to regulate operation points of engine is presented, in which the instantaneous power output of engine can be responded to meet suddenly increased demand power by pre-regulating operating points according to the predicted power. Thirdly, the coordinative optimization for speed regulation of engine and power flow of vehicle is formulated in model predictive control framework considering multiple constraints of SHEV. Finally, the performance of the proposed strategy is both validated in simulation and hardware-in-loop test. The results show that the expected output power of EGS can be ensured, and the fuel economy can be improved by 9.16% and 5.91% over rule-based strategy, under the two test driving cycles, respectively.

Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach

The rapid growth of electrification, automation and connectivity in the transport industries puts forward higher requirements on control strategies to improve energy efficiency, traffic safety and driving comfort. Intense efforts have developed energy management strategies (EMS) in car-following scenarios for hybrid electric vehicles (HEVs) by adopting model predictive control (MPC). However, the computational complex online optimization intrinsic to MPC hinders its real-time implementation. This paper is thus proposed to develop a framework of energy-saving controller for HEVs based on explicit MPC, taking advantage of its online computational efficiency, to enable real-time control. To achieve this, the constrained finite-time optimization control (CFTOC) problems of car-following control and energy management strategy for a hybrid electric vehicle are formulated separately. The two problems are then shifted to explicit MPC by precomputing the explicit solutions offline and the control laws are coupled together to form the control framework. Numerical simulations show that the proposed controller can improve the energy efficiency, driving safety and comfort while reduce the online computational costs. Moreover, the result of the hardware-in-the-loop experiment demonstrates the real-time performance of the proposed controller.

STABILITY CONTROL OF ENERGY SAVING ELECTRIC VEHICLE USING DYNAMIC NONLINEAR SYSTEM STATE ESTIMATION

The purpose is to study the handling stability and energy saving mode of energy saving electric vehicles. First, the differences between electric vehicles and traditional fuel vehicles are analyzed. Moreover, based on active safety technology, the control stability, handling stability and driving safety of electric vehicles are improved. Then, based on the vehicle dynamics theory and the proposed assumptions, the modular electric vehicle dynamics model is established, including the wheel model, tire model and driver model, which can effectively control the error of the vehicle model and reduce the difficulty and complexity of vehicle modeling. Next, based on the model predictive control theory, the vehicle steady-state control strategy is established, which can predict and control the state of the electric vehicle in the compound condition and improve the control stability of the vehicle. Finally, the vehicle dynamic model and steady-state control system are simulated and tested. The experimental results show that the simulation results of the established dynamic model of the electric vehicle in angle step condition and single line shifting condition are consistent with the results of professional software, which shows that the established dynamic model can accurately describe the dynamic state of electric vehicle and provides the basis for the research of vehicle stability control strategy. Experimental results of the control strategy under the condition of double line shifting show that the designed control strategy can accurately track the state of the system and constrain the deviation in a small range, so as to ensure the driving stability of the electric vehicle. This exploration provides a reference for the study of dynamic analysis and handling stability control strategy of electric vehicles.

Investigation on electric vehicle motor challenges, solutions and control strategies

This research paper tells about the different types of defects faced by the motors used in electric vehicle such as cost of the motor and it also discusses about the solutions to reduce the problems. In recent years the sharp rise in greenhouse gas emissions have forced the population to move towards electric vehicle. This target can only be achieved by removing the drawbacks such as cost and mileage of electric vehicle. Electric motors play a huge role in electric vehicle and motors that are used in EVs are quite costly and efficiency of the motor affects the mileage of electric vehicle. In this context, this paper tells about the defects or challenges of the motors in the vehicle. Mainly three motors are used in electric vehicle such as induction motor, switched reluctance motor and permanent magnet synchronous motor. This research paper discusses different solutions, and control strategies of EV motors in order to achieve the low cost and better responses as motor plays a huge part in electric vehicle and also the future scope of these motor is forecasted. Above investigation of control strategies is carried out by MATLAB/Simulink Software.

Current Track Circle Model Predictive Torque Control Strategy for Electric Vehicle

Current Track Circle Model Predictive Torque Control Strategy for Electric Vehicle

Energy Analysis and Control Strategy of Planetary Gear-Based Master-Slave Electrohydraulic Power-Coupled Electric Vehicle

Energy Analysis and Control Strategy of Planetary Gear-Based Master-Slave Electrohydraulic Power-Coupled Electric Vehicle

Energy-optimal adaptive cruise control strategy for electric vehicles based on model predictive control

Adaptive cruise control (ACC), as an important part of the process of realizing self driving, is very consistent with the current concept of vehicle production vigorously advocating safety, energy saving and environmental protection. However, how to integrate energy optimization into the design of the controller and realize the trade-off between the tracking performance and economy for the host vehicle is a very important problem. Therefore, this paper proposes a strategy for energy-optimal adaptive cruise control (EACC) for pure electric vehicles based on model predictive control (MPC) algorithm. By the way to introduce motor energy consumption as the economic evaluation index, the optimized torque can make the host vehicle track the preceding vehicle more economically with the constraints related to safety, comfort and tracking satisfied. At the same time, based on the nonlinear autoregressive with external input (NARX), the acceleration prediction of the preceding vehicle is selected as the disturbance for MPC, which can effectively improve the speed tracking performance for the host vehicle in the situation of driving at low speed and braking at high speed. In addition, the variable weight strategy is used to balance tracking performance and economy, which improves the adaptability of EACC vehicles under different working conditions like high speed and low speed. The results under the cycle condition of NEDC and WVUCITY show that the proposed EACC based on MPC+ (introducing leading vehicle speed prediction and motor energy consumption) shows a good energy saving compared with the other strategies based on PID, LQR and MPC. Compared with the classic Eco-ACC strategy based on dynamic programming, the proposed MPC + control strategy has the advantage of realizing more gentle torque output, thus the peak of the battery discharge current is relatively less than that of the controller based on DP, which is beneficial for prolong the battery life. The hardware-in-loop test evaluates the response characteristics of the controller and validates the effectiveness of the proposed MPC + strategy under real-time conditions.

Research on a PMSM control strategy for electric vehicles

This paper proposes a novel state estimation based permanent magnet synchronous motor (PMSM) control method for electric vehicle (EV) driving. Firstly, a state feedback decoupling control with disturbance feed-forward (SFDCDF) is described. As motor angular speed and rotary angle are key information for the proposed control algorithm and park’s transformation, a novel observer based angular speed estimator (OBASE) is proposed for angular speed estimation. Moreover, an extended Kalman filter (EKF) based rotary angle estimator (EBRAE) is used for rotary angle estimation with information of the estimated angular speed. The convergence of angular speed estimation is proven through Lyapunov stability theory. Simulation results also indicate that the proposed algorithms can control PMSM torque, current, and angular speed to accurately follow reference values without severe fluctuation. In addition, in order to provide SFDCDF with load torque information, the OBASE is slightly modified to work as a vehicle load estimator (VLE) so PMSM responds more rapidly and speed fluctuates more slightly when the load suddenly changes. Then a series of hardware in the loop (HIL) simulations are carried out. Results indicate that the proposed control strategy can precisely estimate PMSM’s angular speed and rotor angle. Also, it can improve the driving performance of PMSM used on EVs.

Traction Control and Total Driving Force Distribution Strategy for Electric Vehicles with Four in Wheel Motors based on PD-Fuzzy Control

In this paper, a torque distribution control for electric vehicles with an in-wheel motor installed for each wheel is proposed in order to manage the traction operation of the vehicle and avoid slip ratio from reach extreme values while the vehicle moves into slippery surfaces. The driving force control is the main controller in the proposed control system and its consist from two control loop. PD-Fuzzy logic and PI-control are applied as a wheel speed controller in the inner control loop with the aim of improving stability and support the traction operation of the wheels. To retain the total requested driving force, force distribution strategies are obtained when the vehicle moves on the road with a low friction coefficient surface. Also, it works as a yaw moment suppressor when the vehicle runs into split slippery surfaces which improves vehicle performance. The proposed vehicle dynamics and torque distribution control system are simulated using Simulink-MATLAB Program. The results of the simulation demonstrated the effectiveness of the proposed method.

A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

Conclusive evidence has demonstrated the critical importance of adaptive cruise control (ACC) in relieving traffic congestion. To improve the performance of the ACC system, this paper proposes a novel ACC strategy for electric vehicles based on a hierarchical framework. Three main efforts have been made to distinguish our work from the existing research. Firstly, a sliding acceleration identification model is established based on the recursive least squares algorithm with multiple forgetting factors (MFF-RLS). Secondly, with vehicle following, economy, and comfort as the optimization objectives, the upper-level controller is developed based on the model predictive control (MPC) algorithm. Benefit from the identification of the sliding acceleration, the MPC controller holds better capability in accommodating environmental changes. Thirdly, an iterative learning lower-level controller is designed to control the driving and braking systems. Considering the efficiency of regenerative braking, the braking force distribution strategy is also designed in the lower-level controller. Simulation results show that, compared with the conventional MPC-based ACC strategy, the proposed strategy has similar performance in vehicle following, but it makes great improvements in comfort and economy. The specific features are that the vehicle acceleration and speed fluctuation are significantly reduced, and the energy consumption is also reduced by 2.05%.

A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory

A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory

Vehicle-vehicle energy mutual aid control strategy for electric vehicles

Vehicle-vehicle energy mutual aid control strategy for electric vehicles

Fuzzy predictive control strategy for plug-in hybrid electric vehicles over multiple driving cycles

Fuzzy predictive control strategy for plug-in hybrid electric vehicles over multiple driving cycles

Research on Control Strategy and Central Control System of Intelligent Electric Vehicle

With the continuous improvement of world science and technology, vehicle technology has also been greatly developed. Cars bring convenience to people’s travel, and occupy an increasingly high proportion in social life. Automobile plays an important role in the continuous progress of contemporary society and the change of human travel conditions. At the same time, the increasing number of cars also brings many problems: energy consumption and environmental pollution are worsening, traffic accidents are increasing year by year. According to the who survey, the number of casualties caused by traffic accidents in China is gradually increasing. High traffic accidents have caused irreversible losses to social development, people’s lives and property. It has become the development goal to improve the vehicle active safety, control the vehicle to automatically identify the road, drive according to the planned route and reduce the frequency of traffic accidents. In the traditional driving environment, people occupy a dominant position. In the face of complex and changeable traffic environment and a large number of traffic information, it is easy to make mistakes in driving and cause serious traffic accidents. Therefore, it is very important to extricate the driver from the complex and changeable traffic environment and make driving safer. The intelligent vehicle collects the information about the road and the vehicle itself through the sensor, and sends it to the central controller which controls the transverse and longitudinal movement of the electric vehicle. The central controller sends out corresponding instructions to control the horizontal and longitudinal operation of the vehicle according to the control strategy, so as to realize the control of the driving behavior of the vehicle, so that the vehicle can drive safely on the desired track at the expected speed, It can avoid driver fatigue driving behavior and traffic accidents caused by driving technology. Various sensors in the intelligent vehicle can obtain more vehicle information at the same time, and control the vehicle through the controller processing.

Research on Fuzzy Control Strategy and Genetic Algorithm Optimization for Parallel Hybrid Electric Vehicle

In this paper, the fuzzy control strategy for parallel hybrid electric vehicle(HEV) is proposed. Based on fuzzy logic theory, SOC and torque demand scale factor are taken as the input, a fuzzy logic controller is designed. Combining with the rule-based control strategy(RBCS), the engine and motor output torque are gotten, so the whole energy control strategy is proposed. Then the genetic algorithm(GA) is used to get the optimal results: fuel consumption and emission level, to verify the accuracy of fuzzy control strategy.

Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information

Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information