Smart Monitoring of High-Performance KY Converter in IoT-Enabled Electric Vehicle Systems

In this paper, a power-split strategy based on a real-time average power method is developed for improving power output of battery and mode switching frequency of a multi-mode hybrid energy storage system (HESS) in electric vehicles. To achieve mode switching and power distribution for the multi-mode HESS, a rule-based strategy is designed based on the high-frequency power demand. Furthermore, a simple real-time average power method is adopted to process the high-frequency power demand. Then, the real-time average power is used as a variable logic threshold value for the power-split strategy. Since the power-split controller responds to the smooth average power rather than the high-frequency power demand, the high-frequency mode switching of the multi-mode HESS can be avoided. The ultra-capacitor works as an enhanced low-pass power filter and the battery can supply smooth and steady output power to the motor inverter. Comparative simulations between the developed power-split strategy and the rule-based strategy are performed. The advantages of the developed power-split strategy for improving the power output of the battery and the mode switching frequency of the multi-mode HESS in electric vehicles are indicated under three typical driving cycles. Moreover, the longer time duration of the real-time average power is designed, the smoother power output of the battery and the lower mode switching frequency of the multi-mode HESS can be achieved.

This paper proposes a novel control scheme for hybrid energy storage system(HESS) in electric vehicles. The HESS above is composed of two parts: fuel cell (FC) and super capacitor(SC). FC, as the main power supply, provides a large proportion of required power. Whereas, SC, as the auxiliary energy storage source, plays a great role when starting and braking an electric vehicle(EV). Both energy sources are connected to the DC bus with DC-DC converters. Two converters share the very DC bus which is connected to the traction motor with a power inverter. To make sure the performances of the control system, a control method named generic model control is applied. Simulation results indicate the robustness and static performance of the proposed control scheme.

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control

Energy management strategy (EMS) is a key issue for hybrid energy storage system (HESS) in electric vehicles. By innovatively introducing the current speed information, the vehicle speed optimized fuzzy energy management strategy (VSO-FEMS) for HESS is proposed in this paper. Firstly, the pruned fuzzy rules are formulated by the SOC change of battery and super-capacitor to preallocate the required power of vehicle. Then, the real-time vehicle speed is used to optimize the pre-allocated results based on the principle of vehicle dynamics, so as to realize the optimal allocation of required power. To validate the proposed VSO-FEMS strategy for HESS, simulations were done and compared with other EMSs under the typical urban cycle in China (CYC-CHINA). Results show that the final SOC of battery and super-capacitor are optimized in varying degrees, and the total energy consumption under the VSO-FEMS strategy is 2.43% less than rule-based strategy and 1.28% less than fuzzy control strategy, which verifies the effectiveness of the VSO-FEMS strategy.

Energy management of hybrid energy storage system in electric vehicle based on hybrid SCSO-RERNN approach

To evaluate the safety of electromagnetic exposure of a magnetic coupling resonance wireless charging system in electric vehicles on the human central nervous system, COMSOL Multiphysics software is used to establish electric vehicle models with different materials and thicknesses, a magnetic coupling resonance wireless charging system model, and a human body model. The electromagnetic exposure of the electromagnetic field generated by the wireless charging system to the central nervous system of the human body in the electric vehicle with carbon fiber-reinforced plastics (CFRP) car body, aluminum alloy car body, and low carbon steel car body with different thickness shielding is simulated and analyzed. The simulation results show that CFRP, aluminum alloy, and low carbon steel vehicles can effectively shield electromagnetic fields. The shielding effect of CFRP and aluminum alloy car body improves with the increase of thickness, and the shielding effect of low carbon steel car body worsens with the increase of thickness. Under the worst shielding condition, the maximum magnetic induction strength and electric field strength of human head are respectively 0.9 碌T and 0.18 V/m the electromagnetic exposure level to the human head is less than the public exposure limit of 27 碌T and 2.97 V/m set by the International Commission on Non-Ionizing Radiation Protection. Results show that the magnetic coupling resonance wireless charging system of an electric vehicle will not harm the human central nervous system in the vehicle.

Lithium-ion batteries are highly sensitive to temperature, with excessive heat leading to accelerated aging, reduced cycle life, and safety risks such as thermal runaway. Traditional thermal management systems often struggle to maintain optimal battery temperature, especially under high ambient temperatures or dynamic operating conditions. This paper proposes a novel reinforcement learning (RL)-based control method for the coupled thermal management system of a power battery and air conditioning system in a Plug-in Hybrid Electric Vehicle (PHEV). The system integrates the battery cooling circuit with the air conditioning system via a heat exchanger, eliminating the need for a separate radiator. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed to control the water pump speed, optimizing battery temperature in real-time. Experimental results under New European Driving Cycle (NEDC) conditions at 40°C ambient temperature demonstrate that the RL-controlled pump responds faster than the Model Predictive Control (MPC) algorithm, allowing the battery temperature to reach the target temperature of 28°C 1 min earlier with a maximum fluctuation of only 0.8°C. The RL-controlled pump consumes 4 W h more energy than the MPC-controlled pump, while the compressor energy consumption is only 1.68% higher. Additionally, the temperature fluctuations in the passenger cabin under RL control are only 0.1°C higher than under MPC, ensuring passenger comfort. This study highlights the potential of RL-based control for improving the efficiency and stability of integrated thermal management systems in electric vehicles.

Purpose. To determine for the developed electromechanical control system of a 4-drive electric vehicle, the power of the drives and the coefficients of the gearboxes that ensure the minimum energy consumption during acceleration from 0 to 100 km / h in 5 s. Research methods. Mathematical analysis and modeling. Findings. The proposed solution is to determine, for a given vehicle dynamics (acceleration from 0 to 100 km / h in 5 s), by mathematical modeling, values of drive powers and gear ratios, which increase the efficiency of the electromechanical system. The condition for increasing the efficiency is to minimize the consumption of electrical energy for acceleration. The developed computer model of the electromechanical control system of the 4 - drive electric vehicle allows to carry out researches on definition of electromechanical parameters of the control system providing the minimum energy consumption at dispersal from 0 to 100 km / h for 5 s. The current consumed by the drives was determined by studying the energy characteristics of the drives obtained on a previously developed laboratory stand. Gear ratios and drive power have been found to provide minimum power consumption when accelerating a 4-wheel drive electric vehicle. From the obtained data it is seen that for each power studied there are minima in the range of gear ratios 1-3,4. Also in the course of the research it was found that among the studied powers of 30-160 kW there is a drive that uses less energy. For the studied electric vehicle, these are four drives of 75 kW with a reduction factor of 3,3. The presence of minima in the interval between the gear ratios and the interval of the studied drive powers is explained by the variability of the engine efficiency in the load ranges varying from 0.3 to 2 times and the engine shaft speed of 0-3000 rpm, due to varying degrees of loss impact, the dynamic modes of the drive. Originality. The study of energy characteristics based on the developed control system for a 4-wheel drive vehicle makes it possible to find the electromechanical parameters of the system that provide a minimum of energy consumption during acceleration from 0 to 100 km/h in 5 s. Practical value. The electromechanical parameters of the control system of the 4-drive electric vehicle obtained as a result of research allow to receive economy of electric energy at dispersal from 0 to 100 km / h for 5 s in 2-3% within one experimental nominal power of the engine.

In order to optimize the operation status of hybrid energy storage system in electric vehicles, a novel fuzzy logic control strategy is proposed. This strategy adopts Kalman filtering algorithm to estimates state of charge (SOC) and state of power (SOP), which can calculate the optimum power and alleviate the errors of SOC effectively. Besides, the framework of the strategy and the model of both battery and supercapacitor are introduced. The proposed control strategy will help to deal with energy storage system problem of relatively short lifespan and poor limited range in electric vehicles. So stability and efficiency of hybrid energy storage system (HESS) improve greatly.

A battery management system (BMS) plays an important role in electric vehicles (EVs) in order to achieve a reasonable-lasting lifetime. An equalizing method is essential in order to obtain the best performance. A monitoring system is required to check if any cell voltage is high or low. In this paper, an equalizing and monitoring system for an ultra-light electric vehicle is proposed. The monitoring system detects if one cell is fully charged or all cells are fully charged and the equalizing system tops each cell at the desired voltage. To solve this issue, a light-emitting diode (LED) band gap is used as a voltage reference to inform the user if any cell is at its high voltage. A smart monitoring displays on the liquid crystal display (LCD), if one cell is high or all cells are high. This detection also provides a signal to the microcontroller to turn on/off the charger if all cells are high. Also, a Bluetooth module was designed to command the microcontroller the charger to turn on/off via voice/text message by using a smartphone. Additionally, a new smart monitoring system based on the Bluetooth model (HC05) and mobile app has been made in order to monitor individual cell voltage. A major feature of the system is to draw a very-low current, so that the system does not contribute significantly to the self-discharge of the battery and the circuit does not need sophisticated control. Manufacturers of large electric vehicles may have more intelligent systems that may require a permanent connection to the grid and allow high standby losses, where more state of charge (SOC) may be lost per day. The paper is rather focused on reducing the standby losses, and to activate the equalizer only when charging and/or driving. The experimental results are performed in order to verify the feasibility of the proposed circuit.

The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends on the implementation of sophisticated control algorithms. From fundamental rule-based systems to advanced predictive and intelligent control strategies, the evolution and integration of these algorithms are driven by the need to efficiently manage the power flow, optimize energy utilization and ensure the long-term reliability of hybrid energy storage systems. This study briefly presents (in the form of a mini review) the research in this field and the development directions and application of state-of-the-art control algorithms, also highlighting the needs, challenges and future development directions. Based on the analysis made, it is found that from the point of view of performance vs. ease of implementation and computational resource requirements, fuzzy algorithms are the most suitable for HESS control in the case of common applications. However, when the performance requirements of HESSs relate to special and high-tech applications, HESS control will be achieved by using convolutional neural networks. As electric vehicles continue to evolve, the development of more intelligent, adaptive and robust control algorithms will be essential for achieving the full potential of integrating ultracapacitors into electric mobility.

–This paper designs the high-efficiency and the low-cost drive system of the small-size electric vehicles (EVs). The power circuit for driving the dc motor is designed by considering both the cost and efficiency. In order to reduce the conduction loss of MOTFET and diode for controlling an armature voltage, some MOSFETs and diodes at the armature are in parallel connection. An operating sequence for both the field current and the armature voltage according to the accelerator pedal angle is suggested for changing smoothly the rotating direction of dc motor. Through the simulation studies, the performances of the proposed methods are verified. Keywords: Electric vehicle, conduction loss, buck converter, switching loss, DC motor 1. Introduction Recently, because environmental pollution and the energy crisis are rising globally, electric vehicles (EVs) are promising instead of traditional gasoline vehicles. As the EVs are driven by electricity stored in a battery, they still suffer from the main problem as short driving range due to the low energy content of the batteries. As how to use the battery’s energy efficiently is a hot issue for developing EVs, it is important to design the drive system of a traction motor with high efficiency. Selection of traction motors for propulsion system is very important step that requires special attention. In fact, the choice of electric-propulsion systems for EVs mostly depends on three factors: efficiency, cost, and reliability. In an industrial point of view, the major types of electric motors adapted or under serious consideration for EVs include the dc motor, an induction motor (IM), and brushless DC (BLDC) motor. The BLDC motor has been widely used in the EVs, because it provides high power density, and has higher efficiency than induction motor [1]-[3]. By the way, in order to drive the BLDC motor, the 3-phase PWM inverter and complex control system are required [4]. Recently, the price of PM has been dramatically raised. They cause the increase of the manufacturing cost of the EVs. The small-size EVs such as the golf car and scooters require the simple propulsion system in order to reduce the manufacturing cost. Although a brushed dc motor has some problems such as requirement of maintaining both the brush and commutation, it has some advantages like a simple drive system and high starting torque. Thus, the dc motor as the traction motor is more suitable at the low cost small-size EVs [5]. In this paper, the power circuit for driving the dc motors is designed by considering both the cost and efficiency. In order to reduce the conduction loss of MOTFET and diode for controlling an armature voltage, some MOSFETs and diodes at the armature are in parallel connection. An algorithm to change smoothly the rotating direction of the motor is suggested. In order to verify the performances of proposed methods, some simulation studies are carried out.

The regenerative braking system in electric vehicles is an advanced energy recovery technology that enhances overall efficiency by converting kinetic energy, typically lost during braking, into electrical energy for battery storage. Unlike conventional friction-based braking systems that dissipate energy as heat, the proposed design employs a DC motor operating in dual mode—acting as a motor during propulsion and as a generator during deceleration. The recovered electrical energy is conditioned through power electronic components such as rectifiers, MOSFET-based control circuits, and DC-DC converters to ensure stable voltage regulation and safe battery charging. This approach not only improves vehicle energy utilization and extends driving range but also minimizes mechanical wear, reduces maintenance costs, and supports sustainable transportation. By optimizing energy conversion and storage, the system contributes significantly to the development of environmentally friendly and energy-efficient electric mobility solutions

Considering flexibility of the support shafts as well as bearing supports, the effect of meshing impact force and meshing stiffness on the dynamic behavior of a gear transmission system in electric vehicle is investigated in this paper using the proposed hybrid user-defined element method. First, a structured grid generation method is introduced to establish accurate mesh models of the pinion and gear teeth. Second, coupling the tooth mesh models and the flexible shafts as well as bearings, two finite element models are, respectively, constructed for the two helical gear pairs of the electric vehicle reduction unit to calculate the meshing impact force. Next, the basic mechanism of meshing impact is explained in detail according to the finite element results, and the impact force is determined as one of the main internal excitations substituted into the dynamic model established by the hybrid user-defined element method. Under 50 N m input torque and 12,010 r/min rotational speed of the input shaft, the simulation results by the hybrid user-defined element method indicate that the example system reaches a steady state and the vibrations primarily occur at the meshing frequencies. With an increment of 600 r/min of the input rotational speed, it is also concluded from the results that (1) the calculated impact force approximately presents linear growth with the increase of the input shaft rotational speed and (2) the root mean square values of the vibration acceleration generally grow with the increase of the speed.

Battery/Supercapacitors combination offers superior performances for a hybrid electric energy storage system in Electric Vehicles. This paper, presents a frequency power sharing strategy to get the best use of complementarity between battery and supercapacitors. Storage devices are sized based on reduced scale specifications for the studied EV. Power and current waveform simulation results are shown for each storage device due to the use of a frequency power sharing policy. Various power split configurations are analyzed to study their effect on battery current waveforms. Optimal frequency sharing policy for the battery/SCs hybrid reducing RMS battery current is obtained. The best use of battery/SCs association provides significant improvements of Electric Vehicle performances in terms of battery lifetime and amortized cost.