Control Strategy For Electric Vehicles Research Articles

Energy Efficient and Safe Control Strategy for Electric Vehicles Including Driver Preference

In this manuscript, a control strategy for electric vehicles is developed to optimise the energy consumption while respecting constraints associated with both inter-vehicle safety and comfort, which is a challenge in typical optimal control solution methodologies. Firstly, the long-term optimal control is developed using Pontryagin's Maximum Principle (PMP). Thereafter, the obtained PMP solution is used to bound the state space for a computationally tractable Dynamic Programming (DP) optimisation to ensure the satisfaction of the safety constraints. While the acceleration subproblem solution is similar to internal combustion engine vehicles (ICEVs), the combination of regenerative and hydraulic braking significantly alters the nature of the optimal braking profile. Since a DP solution is not tractable for real-time implementation of combined braking, a fast heuristic is developed, which achieves 98% of the optimal energy recovery calculated by the DP in the simulated cases. Simulation results demonstrate that the proposed strategy respects the acceleration and safety constraints while saving approximately 5% energy use without significantly increasing travel time. Further simulations were conducted to evaluate the effect of driver preferences on energy use. It was shown that a 9.5% reduction in energy use if the driver is willing to accept a 10% speed reduction.

A Drive System Global Control Strategy for Electric Vehicle Based on Optimized Acceleration Curve

A Drive System Global Control Strategy for Electric Vehicle Based on Optimized Acceleration Curve

A PMSM fuzzy logic regenerative braking control strategy for electric vehicles

Regenerative braking system is a system by which an energy conversion device is used to convert kinetic energy into electrical energy and store it in an energy storage device for use when the motor vehicle is driving. To improve the energy recovery rate of pure electric vehicles, a series regenerative braking control strategy based on PMSM fuzzy logic is proposed in this paper. According to this strategy, the motor braking shall be used as much as possible based on ensuring braking stability, 4 braking zones shall be divided according to the braking intensity, and different braking force distribution strategies shall be used, while comprehensively considering influencing factors such as vehicle speed, ECE regulations, battery, and motor characteristics. Simulink and Cruise are used for modeling and united simulation. The results show that the built model is accurate and reliable. The energy recovery rate can be improved effectively and the cruising range of pure electric vehicles can be extended based on proposed series regenerative braking control strategy.

The Eco-Driving Considering Coordinated Control Strategy for the Intelligent Electric Vehicles

Minimum energy consumption with maximum comfort driving experience defines the ideal human mobility. Recent technological advances in most highly automated driving systems on electric vehicles with regenerative braking system not only enhance the safety and comfort level but also present a significant opportunity for automated eco-driving. This research focuses on the longitudinal eco-driving considering the coordinated control for 4WD intelligent electric vehicles. The intelligent electric vehicle framework with 4-wheel hub motors is established and the intention-aware longitudinal automated driving strategy for overall traffic situation levels is proposed. Further, the coordinated control strategy arbitrates the control mode basing on the traffic situation level and distributes braking forces between the electronic hydraulic braking system and the cooperative regenerative auxiliary braking system. The proposed strategy is verified in the co-simulation environment and field test respectively. Test results show optimal control effects in overall traffic situation levels and an enhanced energy recycling efficiency.

Research on Deep Reinforcement Learning-based Intelligent Car-following Control and Energy Management Strategy for Hybrid Electric Vehicles

Research on Deep Reinforcement Learning-based Intelligent Car-following Control and Energy Management Strategy for Hybrid Electric Vehicles

Research on Direct Yaw Moment Control Strategy of Distributed-Drive Electric Vehicle Based on Joint Observer

Combined with the characteristics of the distributed-drive electric vehicle and direct yaw moment control, a double-layer structure direct yaw moment controller is designed. The upper additional yaw moment controller is constructed based on model predictive control. Aiming at minimizing the utilization rate of tire adhesion and constrained by the working characteristics of motor system and brake system, a quadratic programming active set was designed to optimize the distribution of additional yaw moments. The road surface adhesion coefficient has a great impact on the reliability of direct yaw moment control, for which joint observer of vehicle state parameters and road surface parameters is designed by using unscented Kalman filter algorithm, which correlates vehicle state observer and road surface parameter observer to form closed-loop feedback correction. The results show that compared to the “feedforward + feedback” control, the vehicle’s error of yaw rate and sideslip angle by the model predictive control is smaller, which can improve the vehicle stability effectively. In addition, according to the results of the docking road simulation test, the joint observer of vehicle state and road surface parameters can improve the adaptability of the vehicle stability controller to the road conditions with variable adhesion coefficients.

A Critical Review of Advanced Electric Machines and Control Strategies for Electric Vehicles

Transportation electrification has attracted much attention in modern society. Among all electrified transportation tools, electric vehicle (EV) is absolutely the one that has great potential to compete with and further take the place of traditional fossil fuel vehicles. This article is to outline and investigate advanced electric machines and their control strategies for EV applications. The key is not only to reveal new design ideas, topologies, structures, methodologies, control strategies, pros and cons, and foresight for advanced electric machines but also to fully integrate these ideas into practical EV applications. This critical review will clarify the development trends of electric machines and their controls.

A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter

The control strategy of electric vehicles mainly depends on the power battery state-of-charge estimation. One of the most important issues is the power lithium-ion battery state-of-charge (SOC) estimation. Compare with the extended Kalman filter algorithm, this paper proposed a novel adaptive square root extended Kalman filter together with the Thevenin equivalent circuit model which can solve the problem of filtering divergence caused by computer rounding errors. It uses Sage-Husa adaptive filter to update the noise variable, and performs square root decomposition on the covariance matrix to ensure its non-negative definiteness. Moreover, a multi-scale dual Kalman filter algorithm is used for joint estimation of SOC and capacity; the forgetting factor recursive least-square method is used for parameter identification. To verify the feasibility of the algorithm under complicated operating conditions, different types of dynamic working conditions are performed on the ternary lithium-ion battery. The proposed algorithm has robust and accurate SOC estimation results and can eliminate computer rounding errors to improve adaptability compared to the conventional extended Kalman filter algorithm.

Particle Swarm Optimization for Control Strategy of Hybrid Electric Vehicles

This paper presents a particle swarm optimization algorithm (PSO) as a control strategy for the offline driving cycle to obtain the best torque distribution between the two sources: internal combustion engine (ICE) and the electric motor (EM). The purpose to minimize the fuel consumption, emissions, and maximize the state of charge of the battery for the model of power-split hybrid electric vehicles (PSHEV), while the requirements of the driving performance considered as constraints. The control strategy has been applied for the UDDS driving cycle under the Matlab Simulink software environment. The results of the value of fuel consumption compared with fuzzy logic control (FLC), the global optimization genetic algorithm (GA), and ADVISOR. After comparing, the results demonstrate the effectiveness of the (PSO) algorithm over the mentioned methods in lowering fuel consumption by 8.87% for the (FLC), 22.6% for the GA. Maximizing the state of charge of the battery by 5.6% for the ADVISOR program and closest to optimal results for FLC

Online Control Strategy for Plug-In Hybrid Electric Vehicles Based on an Improved Global Optimization Algorithm

Neural networks are widely used in the learning of offline global optimization rules to reduce the fuel consumption and real-time performance of hybrid electric vehicles. Considering that the torque and transmission ratio are direct control variables, online recognition by a neural network of these two parameters is insufficiently accurate. In the meanwhile, the dynamic program (DP) algorithm requires huge computing costs. Based on these problems, a fusion algorithm combining a dynamic programming algorithm and an approximate equivalent fuel consumption minimum control strategy (A-ECMS) is proposed in this paper. Taking the equivalent factor as the control variable, the global optimal sequence of the factor is obtained offline. The back propagation (BP) neural network is used to extract the sequence to form an online control strategy. The simulation results illustrate that, compared with the traditional dynamic programming algorithm, although the fuel consumption increases slightly, the computational cost of the fusion algorithm proposed in this paper is significantly reduced. Moreover, because the optimal sequence of the equivalent factors is within a particular range, the online control strategy based on DP-A-ECMS has a high robustness. Compared with an online control strategy based on the torque and transmission ratio, the fuel economy is improved by 2.46%.

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

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

Eco-driving control for connected and automated electric vehicles at signalized intersections with wireless charging

Electric vehicles have been well recognized by many as a solution to reduce greenhouse emissions, improve mobility, and decrease energy consumption in the transportation system. In this paper, we propose a novel wireless charging scheme for electric vehicles. The proposed scheme deploys partial wireless charging lane to intersections, so as to utilize the slow movement of vehicles at traffic lights for recharging. Furthermore, an eco-driving control strategy for electric vehicles at intersections with wireless charging, i.e., W-eco-driving, is developed, aiming to decrease the total energy consumption and increase traffic efficiency. With the aid of connected and automated vehicles technology, driving and surrounding data are accessible in real-time. Then electric vehicles can be controlled using the W-eco-driving strategy accurately. The proposed strategy is finally validated based on a specific intersection configuration. Both single electric vehicle control and electric vehicle platooning are discussed in test examples. We also investigate the influences of wireless charging power, location and length. Numerical results show that the wireless charging scheme with W-eco-driving is able to increase the driving range and decrease the travel cost simultaneously. The total travel cost can be saved up to 16% by following W-eco-driving. We further provide some suggestions about the construction of wireless charging lanes in the vicinity of signalized intersections. Compared with the pursuit of a long wireless charging lane, it is more important to put it in a proper position in the early stage of construction.

Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles

Thermal management systems of passenger vehicles are fundamental to provide adequate cabin thermal comfort. However, for battery electric vehicles they can use a significant amount of battery energy and thus reduce the real driving range. Indeed, when heating or cooling the vehicle cabin the thermal management system can consume up to 84% of the battery capacity. This study proposes a model-based approach to design an energy-efficient control strategy for heating electric vehicles, considering the entire climate control system at different ambient conditions. Specifically, the study aims at reducing the energy demand of the compressor and water pumps when operating in heat pump mode. At this scope, the climate control system of the reference vehicle is modelled and validated, enabling a system efficiency analysis in different operating points. Based on the system performance assessment, the optimized operating strategy for the compressor and the water pumps is elaborated and the results show that the demand-based control achieves up to 34% energy reduction when compared to the standard control.

Research and Analysis on Low-temperature Charging Control Strategy of Electric Vehicle

In this paper, the pure electric vehicle is taken as the research object. Under the same conditions (such as the same limit temperature) under different conditions, the charging control strategy test of the collective prototype and the same vehicle is carried out under different conditions. The test data is used to analysis vehicle charging control strategy and key technical indicators that affect charging performance. The test results show that from the analysis of the collective prototype test, AC charging can be started normally in the low temperature environment. The charging power of most test vehicles has a certain attenuation compared with the normal temperature, and the charging time has a certain increase. From the same temperature comparison test of the same car, the difference of charging current trend, battery input current, battery temperature and maximum charging current between low temperature and normal temperature is analyzed.

An Energy-Saving Torque Vectoring Control Strategy for Electric Vehicles Considering Handling Stability Under Extreme Conditions

Four-wheel independently actuated electric vehicles (FWIA EVs) allow variable distributions of driving torques among individual wheels to improve vehicle performance. To reduce energy consumption while ensuring handling stability, we propose an optimal torque vectoring control strategy based on a two-level distribution formula. This strategy can naturally decouple front/rear axle torque vectoring from left/right torque vectoring and avoid the contradiction between stability and energy saving. First, considering the motor efficiency, the vehicle's total torque is optimally distributed to the front and rear axles based on model predictive control. Then, based on the front/rear axle distribution ratio, the left/right torque vectoring is revised to produce a suitable additional yaw moment to improve the handling stability. A sliding mode controller is designed to track the reference yaw rate calculated from a nonlinear reference model. The nonlinear reference model is more suitable for extreme conditions due to the accurate reflection of the nonlinear characteristics. A suitable additional yaw moment can ensure vehicle stability and avoid excessive energy consumption due to vehicle instability. The simulation and hardware-in-the-loop experimental results demonstrate that the proposed control strategy can reduce energy consumption while ensuring vehicle stability.

Research on the driving control strategy for ISG hybrid electric vehicles

ABSTRACT To overcome such Hybrid Electric Vehicle (HEV) engine problems as high fuel consumption and poor economical efficiency when working in low-efficiency areas, this paper intends to establish the ISG light hybrid vehicle structure-based mathematical models of engines, electric motors, batteries and the whole vehicle power output first; to analyse the relationship between the engine load rate and the optimal engine fuel consumption characteristics, and then to take into consideration the performance characteristics of the motor battery output efficiency, so as to come up with the switching pattern of the ISG parallel hybrid vehicle operating mode. According to the switching rules, the primary influence parameters of the engine load rate as well as the State of Charge (SOC) were extracted and a fuzzy neural network control strategy for torque management was developed. Under the Matlab/Advisor joint simulation platform, a simulation model was established and the effectiveness of the fuzzy control strategy was verified under the typical state of cyclic operation NEDC. The simulation results indicate that the adoption of fuzzy neural network control strategy for torque management can lead to considerable advancements in engine operating efficiency–the fuel economical efficiency can be improved by about 12%.

A thermal management system control strategy for electric vehicles under low-temperature driving conditions considering battery lifetime

Electric vehicle thermal management system (EVTMS) is essential for electric vehicles (EVs) to guarantee cabin thermal comfort and battery appropriate temperature. However, under low-temperature driving conditions, heating power demand for EVTMS affects system performance as well as battery lifetime. In this context, an effective EVTMS solution both for cabin thermal comfort and battery heating is investigated in this paper. The EVTMS performance and battery-lifetime loss characteristics under low temperatures are analyzed. Moreover, a battery-lifetime extension method which reduces battery-lifetime loss by the peak shifting and valley filling effect for battery current is advanced. A novel EVTMS fuzzy control strategy based on this method is proposed to maintain the proper temperature range of cabin and battery, which also regulate the heater load considering battery current. Using an EV and EVTMS model validated by low-temperature driving experiments, the proposed strategy was investigated by simulation and the control effect was carried out. The results showed that compared with the original strategy, battery-lifetime loss with the proposed strategy was reduced by 3.11–3.76% while the average temperatures for the cabin and battery were almost unaffected. Therefore, the strategy can reduce battery degradation while meeting the cabin and battery heating requirements.

Control Analysis of a Real-World P2 Hybrid Electric Vehicle Based on Test Data

The control strategy of a hybrid electric vehicle (HEV) is generally not disclosed to public because it is a significant factor in determining the performance of the system. However, engineers desiring to understand the control concept of real-world HEVs can gain knowledge in various ways. In this study, we used test data obtained from a bench dynamometer and real driving to analyze the supervisory control strategy of Hyundai Ioniq Hybrid. This research can be described in three steps. First, an understanding of the mode control strategy is obtained by investigating the engine on/off behavior, which determines when the pure electric driving mode is used. Second, the shifting patterns are studied by observing the speed ratios according to the vehicle speed and the torque demand. Third, the strategy for distributing the torque between the engine and the motor is analyzed by studying the motor assistant operation. Based on the analyzed control concept, it is possible to understand the technical strategy for improving the fuel efficiency of the parallel hybrid system. This study would be useful for engineers who want to design controllers for HEVs, in that it provides the analyzed control concept and the real-world operating behaviors.

Real-Time Model Predictive Powertrain Control for a Connected Plug-In Hybrid Electric Vehicle

The continued development of connected and automated vehicle technologies presents the opportunity to utilize these technologies for vehicle energy management. Leveraging this connectivity among vehicles and infrastructure allows a powertrain controller to be predictive and forward-looking. This paper presents a real-time predictive powertrain control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) in a connected vehicle environment. This work focuses on the optimal energy management of a multi-mode PHEV based on predicted future velocity, power demand, and road conditions. The powertrain control system in the vehicle utilizes vehicle connectivity to a cloud-based server in order to obtain future driving conditions. For predictive powertrain control, a Nonlinear Model Predictive Controller (NMPC) is developed to make torque-split decisions within each operating mode of the vehicle. The torque-split among two electric machines and one combustion engine is determined such that fuel consumption is minimized while battery SOC and vehicle velocity targets are met. The controller has been extensively tested in simulation across multiple real-world driving cycles where energy savings in the range of 1 to 4% have been demonstrated. The developed controller has also been deployed and tested in real-time on a test vehicle equipped with a rapid prototyping embedded controller. Real-time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in a real-time environment.

Numerical Estimation of Switched Reluctance Motor Excitation Parameters Based on a Simplified Structure Average Torque Control Strategy for Electric Vehicles

Switched reluctance motors (SRMs) have been receiving great attention in electric vehicle (EV) applications. However, the complicated control and inherent torque ripples are the major drawbacks of SRMs. This paper introduces a numerical estimation method for the optimum control parameters of SRM based on a simplified average torque control (ATC) strategy for EVs. The proposed control aims to simplify the control algorithm to cut down complexity and cost. Besides, it aims to achieve all the vehicle requirements. A multi-objective optimization problem is set to determine the most efficient excitation parameters that can fulfill the vehicle requirements. The objective function has two terms: torque ripple and efficiency. Proper constraints for both turn-on and turn-off angles are included in order to achieve high-performance control, maximum torque per Ampere (MTPA) production, and reliable operation. Besides, additional toque constraints are involved to ensure fast dynamics, high-performance torque tracking capability, and parameter insensitivity. The motor model is accurately achieved based on the experimentally measured torque and flux characteristics. Several simulations are executed to prove the feasibility and effectiveness of the proposed control. Moreover, experimental results are obtained to validate the theoretical findings. It is observed that the proposed control has a significant reduction of torque ripples compared to the conventional control methods. The average reduction ratio of torque ripple over the speed range is about 72.43%. Besides, the proposed control succeeds in maintaining a very good efficiency and high torque/current ratio. It also has a fast-dynamic performance.