Optimization of multi-motor and multi-speed powertrain system for electric vehicles based on efficiency characteristics between motor and inverter

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 µT and 0.18 V/m the electromagnetic exposure level to the human head is less than the public exposure limit of 27 µT 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.

This paper presents an optimal design of dynamic wireless automatic charging system for roadway-powered electric vehicles (RPEVs), which can effectively improve the charging performance of wireless electric vehicles. For automatic charging, the optimization of industrial automation systems on the application of RPEVs can fundamentally solve a series of problems, especially for the central issue of how to improve the charging performance. The proposed optimal automation system is generally introduced at first, which is consist of controller, actuator, sensor and controlled object. As controlled object models, the track is studied in detail. In this paper, both theoretical analysis and simulation results are provided to verify the validity of the proposed optimal dynamic automatic charging system.

Shared autonomous vehicles (SAVs) encompassing autonomous driving technology and car-sharing service are expected to become an essential part of transportation system in the near future. Although many studies related to SAV system design and optimization have been conducted, most of them are focused on shared autonomous battery electric vehicle (SABEV) systems, which employ battery electric vehicles (BEVs) as SAVs. As fuel cell electric vehicles (FCEVs) emerge as alternative fuel vehicles along with BEVs, the need for research on shared autonomous fuel cell electric vehicle (SAFCEV) systems employing FCEVs as SAVs is increasing. Therefore, this study newly presents a design framework of SAFCEV system by developing an SAFCEV design model based on a proton-exchange membrane fuel cell (PEMFC) model. The test bed for SAV system design is Seoul, and optimization is conducted for SABEV and SAFCEV systems to minimize the total cost while satisfying the customer wait time constraint, and the optimization results of both systems are compared. From the results, it is verified that the SAFCEV system is feasible and the total cost of the SAFCEV system is even lower compared to the SABEV system. In addition, several observations on various operating environments of SABEV and SAFCEV systems are obtained from parametric studies.

Aiming at problems of large computational complexity and poor reliability, a parameter matching optimization method of a powertrain system of hybrid electric vehicles based on multi-objective optimization is proposed in this paper. First, according to the vehicle basic parameters and performance indicators, the parameter ranges of different components were analyzed and calculated; then, with the weight coefficient method, the multi-objective optimization (MOO) problem of fuel consumption and emissions was transformed into a single-objective optimization problem; finally, the co-simulation of AVL Cruise and Matlab/Simulink was achieved to evaluate the effects of parameter matching through the objective function. The research results show that the proposed parameter matching optimization method for hybrid electric vehicles based on multi-objective optimization can significantly reduce fuel consumption and emissions of a vehicle simultaneously and thus provides an optimized vehicle configuration for energy management strategy research. The method proposed in this paper has a high application value in the optimization design of electric vehicles.

As a main way of logistics transportation, electric logistics vehicle is beneficial to logistics industry and save energy. Parameters matching and optimization method of power transmission system for electric logistics vehicle is studied in this article. Data processing rules and principles are created for CHTC-LT working condition. Working condition and genetic algorithm are used for parameters matching and transmission ratio optimization of power transmission system. Driving range of electric logistics vehicle could be improved.

Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles

5 - Energy management systems for battery electric vehicles

The regulatory standards concerning safety of the occupants, pedestrians & the vehicle have been made stringent in recent years. This priority of safety has led to the introduction of Antilock Braking System (ABS) and Electronic Stability Control (ESC) systems in vehicles. The presence of an electric motor, the key powertrain member in electric vehicles (EVs), makes the implementation of safety systems very effective and challenging at the same time. This is effective, since the torque response from the motor is instantaneous, aiding in ultra-low response time from & to Electronic control unit (ECU) leading to faster vehicle control. However, the challenge is the synchronization of an off-the-shelf available ABS/ESC hardware to the existing EV control architecture. In addition, low weight of the vehicle requires the control and actuators to be very fast for stable operation. The toggling of the regenerative brakes and the intervention of the ABS based on vehicle conditions needed a handful of design & testing iterations. The paper explains about ABS & ESC system implementation & its testing details in a light-weight electric vehicle.

In this paper, effects of lightweight gear blank on static and dynamic behavior for electric drive system in electric vehicles are studied. First, a hybrid finite element-analytical method is proposed in this paper to establish gear load contact analysis model considering the structure of lightweight gear blank, which can balance the computing speed and numerical accuracy. Second, this paper establishes a rigid-flexible coupled dynamic model of electric drive system considering shaft elasticity, bearing stiffness, and housing flexibility. Finally, aiming at the noise, vibration, and harshness problem of an electric drive system equipped on electric vehicles, effects of gear web and gear rim thickness on noise, vibration, and harshness excitation source such as static transmission error and dynamic meshing force as well as dynamic response at bearings and housing are analyzed. The results show that changing gear web and gear rim thickness can significantly reduce dynamic meshing force and dynamic response. Compared with solid gear, dynamic meshing force is reduced by 68.50%, and dynamic response is reduced by 66.70% after optimization, thereby significantly improving the noise, vibration, and harshness performance of gear transmission system in electric vehicles.

This research aims to enhance the efficiency of wireless charging systems in electric vehicles by integrating a hybrid ultracapacitor-battery energy storage solution. Traditional standalone battery-based energy storage systems in wireless charging often face sub-optimal charging efficiency, resulting in extended charging times and reduced energy transfer efficiency. To address this limitation, we propose a hybrid approach that combines the rapid charging capability of ultracapacitor (supercapacitor) with the long-term storage capacity of batteries. The optimal charging range is 0 cm to 2 cm, and the combined output voltage and current are 5 V to 12 V and 0.63 A, respectively. This hybrid energy storage system will significantly boost electric vehicles (EVs) charging efficiency. Our research involves experimental evaluation and data analysis to assess crucial parameters, including charging efficiency, energy transfer efficiency, and charging time. The experimental results are validated and compared against existing battery-only systems, shedding light on the advantages and limitations of the hybrid approach. This study contributes to the optimization of wireless charging systems, enhancing energy transfer efficiency, and promoting the broader adoption of wireless charging technology in electric vehicles.

To boost the performance of the air-cooling battery thermal management system, this study designed a novel vortex adjustment structure for the conventional air-cooling battery pack used in electric vehicles. T-shape vortex generating columns were proposed to be added between the battery cells in the battery pack. This structure could effectively change the aerodynamic patterns and thermodynamic properties of the battery pack, including turbulent eddy frequency, turbulent kinetic energy, and average Reynolds number, etc. The modified aerodynamic patterns and thermodynamic properties increased the heat transfer coefficient with little increase in energy consumption and almost no additional cost. Different designs were also evaluated and optimized under different working conditions. The results showed that the cooling performance of the Design 1 improved at both low and high air flow rates. At a small flow rate of 11.88 L/s, the Tmax and ΔT of Design 1 are 0.85 K and 0.49 K lower than the conventional design with an increase in pressure drop of 0.78 Pa. At a relative high flow rate of 47.52 L/s, the Tmax and ΔT of the Design 1 are also 0.46 K and 0.13 K lower than the conventional design with a slight increase in pressure drop of 17.88 Pa. These results demonstrated that the proposed vortex generating design can improve the cooling performance of the battery pack, which provides a guideline for the design and optimization of the high-performance air-cooling battery thermal management systems in electric vehicles.

In this paper, the authors propose an advanced brushless DC motor (BLDCM) drive for low cost and high performance electric propulsion system in electric vehicles (EVs) and hybrid electric vehicles (HEVs). It includes reduced parts power converter topologies and an optimal PWM control strategy to produce the desired dynamic and static speed and torque characteristics. The theoretical explanation and operational principle are described in detail, and the performance of the proposed low cost BLDCM drive is compared with its conventional counterpart by informative simulation results. An IGBT inverter with high speed DSP (TI TMS320 F243) is also built to provide experimental results.

With the new challenges brought by the high penetration of Renewable Energy Resources (RESs) into the modern grid, developing new solutions and concepts are necessary. Microgrid (MG) is one of the new concepts introduced to overcome upcoming issues in the modern electricity grids. MGs and Multi-Microgrids (MMGs) are defined as the building blocks of smart grids. MGs are the small units, where power generation and consumption happen at the same location and MG makes the decisions by itself. MGs can operate grid-connected or island mode depending on the functionality of the MG. Energy Management System (EMS) is the decision making centre of the MG. The data from the devices is received by the EMS and after processing, the commands are sent to the controllable components. Management of voltage, active and reactive power, neutral current, unit commitment and economic dispatch are of the tasks of EMS. In this PhD thesis, an optimal EMS for MGs and MMGs is developed. The main objective of this project by developing the EMS is to optimize the energy flow in the MGs and MMGs to obtain peak load shaving in a cost beneficial system. In order to achieve an efficient EMS, communication system, forecasting system, scheduling system, and optimization system are modelled and developed. Different types of EMS operation, centralized, decentralized and distributed, are investigated in this work to achieve the best combination for MMG EMS operation. The communication system is mainly utilizing Modbus TCP/IP protocol for data transmission at local level and Internet of Things (IoT) protocols (MQTT) for the global communication level. A communication operation algorithm is proposed to manage the MMG EMS under different communication operation modes and communication failure conditions. Furthermore, a monitoring system is developed to collect the data from different devices in the MG. The data is processed in the MG EMS and the commands are sent to components through the communication infrastructure. The link between MGs and MMGs is through the proposed two-level communication system, where the expansion of MGs to a MMG is investigated. In the MMG, MGs are functioning as a unit while having different priorities and operating under different policies. Each MG has its own MG EMS and the EMSs transfer information through the communication system between each other in either centralized, decentralized, distributed, or no communication modes under the MMG EMS. The forecasting system is required in the EMS to predict the future MG characteristics such as power generation and consumption. The forecasted data is the input to the optimization and scheduling system of EMS. Employing the forecasting system in the EMS would increase the accuracy of the optimization and scheduling systems. In this thesis, the timeseries-based forecasting algorithms are employed to predict next day’s active power using the load data, generation data, weather data and temperature data as the inputs. The heart of EMS is the scheduling and optimization system. The purpose of the scheduling system is to define the amount and the time of energy flow in the MG for different generation sources and consumption loads. Furthermore, scheduling system is responsible for peak load shaving and valley filling. On the other hand, the optimization system has the task of minimizing the operation costs of the MGs. The role of market in the scheduling and optimization is important. Time of Use (ToU) tariff is the pricing system, which determines the peak and off peak hours for energy usage pricing. In order to apply the optimization system, a model of the system, an objective function and systems constraints are defined, where aging of battery energy storage system (BESS), operational cost of components and MG cost benefits are considered. To operate the EMS scheduling and optimization system, IBM CPLEX Optimization Studio solver conducts the optimization while for the scheduling system, objective function and constraints are defined in MATLAB. In this thesis, a rule-based, MILP and MIQP optimization system for commercial MGs including electric vehicles (EVs) are proposed to investigate performance of MG EMS for different case studies. In this thesis, the literature for different scheduling and forecasting systems is investigated and different optimization algorithms are analysed. The communication protocols utilized in this research are described and compared to other protocols in the literature. In different chapters of this thesis, the modelling of MGs and MMG EMS, different modules of EMS, forecasting, optimization, scheduling and communication systems are described and analysed. A novel communication system for MMG EMS operation is proposed for commercial buildings. The performance of MG EMS and MMG EMS is examined for power and neutral current sharing, operation cost optimization, and demand peak shaving applications and results are compared to investigate the performance of proposed algorithms.

Lithium-ion traction battery systems of hybrid and electric vehicles must have a high level of durability and reliability like all other components and systems of a vehicle. Battery systems get heated while in the application. To ensure the desired life span and performance, most systems are equipped with a cooling system. The changing environmental condition in daily use may cause water condensation in the housing of the battery system. In this study, three system designs were investigated, to compare different solutions to deal with pressure differences and condensation: (1) a sealed battery system, (2) an open system and (3) a battery system equipped with a pressure compensation element (PCE). These three designs were tested under two conditions: (a) in normal operation and (b) in a maximum humidity scenario. The amount of the condensation in the housing was determined through a change in relative humidity of air inside the housing. Through PCE and available spacing of the housing, moisture entered into the housing during the cooling process. While applying the test scenarios, the gradient-based drift of the moisture into the housing contributed maximum towards the condensation. Condensation occurred on the internal surface for all the three design variants.

This paper proposes a masterless control strategy for parallel active front-end (AFE) systems for off-board electric vehicle (EV) charging and stationary applications. The proposed solution enables high modularity in AC-DC power converters designated for EV charger systems, where the AFE modules can be separated and recombined, enabling them to be more versatile, flexible and scalable. The presented AFE rectifier system consists of several self-sufficient modules with distributed control that can exchange data through CAN bus. The proposed system has functions like dynamic resource allocation, automatic interleaving, circulating current control, accurate current sharing and demonstrates increased fault tolerance. In this paper, the main operating principle of the masterless control of modular AFE rectifier systems is explained. Moreover, the performance of the AFE rectifier system supplying power to a 100 kW off-board DC charger is demonstrated by MATLAB Simulink with changing load, module failures and possible manufacturing flaws.