Electric Vehicle Power System Research Articles

Optimization and Structural Analysis of Automotive Battery Packs Using ANSYS

The development of new energy vehicles, particularly electric vehicles, is robust, with the power battery pack being a core component of the battery system, playing a vital role in the vehicle’s range and safety. This study takes the battery pack of an electric vehicle as a subject, employing advanced three-dimensional modeling technology to conduct static and dynamic analyses. Through weight reduction and structural optimization, an innovative power battery pack design scheme is proposed, aiming to achieve a more efficient and lighter electric vehicle power system. The main research tasks are as follows: Firstly, we designed the main load-bearing components of a certain electric vehicle’s power battery pack and established a three-dimensional (3D) model. Then, the model was simplified according to the actual stress conditions of the power battery pack of the electric vehicle and imported into finite element analysis (FEA) software. Next, based on the fundamental principles of the finite element method (FEM), we conducted static analyses under three conditions: bumpy road sharp left turn, bumpy road sharp right turn, and bumpy road emergency braking. The analysis results indicate that the strength of the battery pack meets the allowable requirements, suggesting that the lower housing design has significant redundancy, providing guidance for subsequent optimization. Finally, through modal analysis, we extracted the first six modes of the power battery box, with the first mode frequency being 33.69 Hz. This suggests that the battery pack may experience resonance during actual operation. Based on the static and modal analysis results, we proposed a structural optimization and lightweight design solution for a certain electric vehicle battery pack and compared it with the pre-optimization data.

Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems

This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source—specifically, solar energy—to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100 W prototype demonstrated the converter’s ability to deliver stable 24 V and 48 V outputs from a 12 V input, with output voltage deviations kept within ± 1%, significantly improving upon the ± 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications.

Electric vehicle power system in intelligent manufacturing based on soft computing optimization

Soft computing technology has attracted extensive attention in computer engineering and automatic control domains because it can deal with uncertainties, fuzziness, and complex practical problems. This study adopts a Genetic Algorithm (GA) in soft computing technology to realize the cooperative optimization of electric vehicle's dynamic and economic performance. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness, and complex practical problems, making GA an effective tool for solving complex optimization problems. Firstly, the electric vehicles' power system structure and energy management strategy are investigated and analyzed. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Thirdly, NSGA-II is used to construct the electric vehicles' power and energy configuration, with power and economic performance as the main optimization objectives. Meanwhile, a fuzzy logic controller is designed to adjust the parameters of GA online, so that the optimization process is closer to the actual operating conditions. Finally, the relevant variables are selected to achieve the optimization goal, the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the optimized electric vehicle's acceleration time is remarkably reduced, the dynamic performance is improved by more than 7 %, and the power loss is reduced by 5 %. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. This study provides a new idea and method for the performance optimization of electric vehicles. Moreover, it offers a valuable reference for the innovation and development of electric vehicle technology in the intelligent manufacturing field. This study indicates that electric vehicles could be more efficient, energy-saving, and environmentally friendly to serve people's travel needs in the future.

A high-efficiency poly-input boost DC–DC converter for energy storage and electric vehicle applications

This research paper introduces an avant-garde poly-input DC–DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources—solar energy and fuel cells—with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85–90% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 91–95% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDC’s adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DC–DC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures.

Investigation of novel type of cylindrical lithium-ion battery heat exchangers based on topology optimization

The in-depth research on the heat exchanger for lithium-ion batteries is of significant importance due to its crucial role in ensuring the safe operation of electric vehicle (EV) power systems. To enhance the thermal and flow characteristic of the heat exchangers, the novel heat exchangers for 18650-cylinderical lithium-ion batteries have been proposed by topology optimization with the minimization of pressure drop and the lowest average temperature (∏1) and the minimization of pressure drop and the lowest temperature difference (∏2) as two different optimal goals. The topology optimization based on the variable density method is utilized to get two-dimensional (2D) optimal channels, and then the optimized results are extended to three-dimensional (3D) heat exchangers. The 3D heat exchangers are named TOHE-1 which is gotten by stretching the topology channels with ∏1 as optimal goal and TOHE-2 which is gotten by that with ∏2 as optimal goal. Subsequently, a detailed thermodynamic analysis is performed to assess the effect of the type of heat exchangers, channel heights, weight factor of topology optimization function, mass flow rates and discharge rates on the cooling effect of the batteries. The results show that compared with the traditional channel heat exchangers, the heat transfer coefficient of topological heat exchangers have increased by 49.92%, while reducing pressure drop by 27.81%. Meanwhile, compared to TOHE-1, heat transfer coefficient of TOHE-2 is increased by 9.70%, while pressure drop is saved by 4.70%. Other studies have indicated that for a 4 C discharge of the battery, a topological heat exchanger with a channel height of 1.5 mm, a thermal performance weight factor of 0.1, and a mass flow with 3.3 × 10−3 kg s−1 is the optimum heat dissipation scheme. Finally, the numerical studies are verified by conducting the convective heat transfer experimental tests.

Design and research of an electric unmanned aerial vehicle power system

An Electric power test system of UAVs is presented, which can be applied to validate the efficiency of UAV propulsion systems and provide reliable verification for propeller numerical calculations. The design proposal, working principle, test equipment and test experiment is introducted. Reliability of various functions of the system is examined and assessed through the multiple comparisond tests, which indicates that, when subjected to identical conditions, the numerical solutions for propeller thrust, torque, and motor efficiency exhibit average errors of 1.02%, 10%, and 10.04%, respectively. The results indicate that the power system test platform effectively validates numerical calculation results, providing reliable engineering application value for general UAV propulsion system research. The findings of this study offer support for optimizing power systems of the UAV, providing valuable references for future engineering applications and development.

A Critical Analysis of Quadratic Boost Based High-Gain Converters for Electric Vehicle Applications: A Review.

Conventional DC-DC boost converters have played a vital role in electric vehicle (EVs) powertrains by enabling the necessary voltage to increase to meet the needs of electric motors. However, recent developments in high-gain converters have introduced new possibilities with enhanced voltage amplification capabilities and efficiency. This study discusses and evaluates the state-of-the-art high-gain DC-DC converters for EV applications based on the Quadratic Boost Converter (QBC). Research into innovative topologies has increased in response to the increasing demand for efficient and high-performance power electronic converters in the rapidly expanding EV industry. Due to its ability to provide more significant voltage gains than conventional boost converters, the QBC has become a viable option for meeting the unique requirements of EV power systems. This survey focuses on the efficiency, power density, and overall performance parameters of QBC-based high-gain converters. The literature review provides a foundation for comprehending power electronics converters' trends, challenges, and opportunities. The acquired knowledge can enhance the design and optimization of high-gain converters based on the QBC, thereby fostering more sustainable and efficient power systems for the expanding electric mobility industry. In the future, the report suggests that investigating new high-gain converter design methodologies will reduce component stress and enhance the intact system efficiency.

Direct regeneration of high-value LiFePO4 cathode materials with nitrogen doped carbon coating

Lithium iron phosphate (LiFePO4) batteries are currently becoming the primary power system for electric vehicles. However, after the life period, the heavy amounts of electric cars would give rise to a great number of scrapped LiFePO4 batteries, which inevitably causes serious environmental pollution and resources waste problems. Therefore, it is much imperative and urgent to develop the green technology for regenerating and recycling these scrapped LiFePO4 batteries. Herein, we reported an efficient and straightforward solid-phase strategy to achieve the direct regeneration of scrapped LiFePO4 cathode materials. The decommissioned LiFePO4 was successfully regenerated by adding lithium carbonate and melamine under the heat treatment condition, and the battery performance was repaired. The electrochemical performance of regenerated LiFePO4 is even higher than that of the unused LiFePO4 at 0.05 C. Moreover, the regenerated LiFePO4 also exhibits excellent capacity preservation rate of 99.03 % after 200 cycles and outstanding rate capability at 5 C with a discharge capacity of 116 mAh/g. The systematical study demonstrates that the nitrogen-doped carbon coatings play vital role in improving the properties of regenerated LiFePO4 cathode materials. This work provides a potential approach to resolve the scrapped LiFePO4 cathode materials to meet the coming retired tide of lithium ion batteries.

Improving Sustainability in Urban and Road Transportation: Dual Battery Block and Fuel Cell Hybrid Power System for Electric Vehicles

Improving Sustainability in Urban and Road Transportation: Dual Battery Block and Fuel Cell Hybrid Power System for Electric Vehicles

Functional Situational Awareness for Reliable and Efficient Operation of Hybrid Electric Vehicle Power Systems

Functional Situational Awareness for Reliable and Efficient Operation of Hybrid Electric Vehicle Power Systems

A deep reinforcement learning approach to energy management control with connected information for hybrid electric vehicles

Considering the importance of the energy management strategy for hybrid electric vehicles, this paper is aiming at addressing the energy optimization control issue using reinforcement learning algorithms. Firstly, this paper establishes a hybrid electric vehicle power system model. Secondly, a hierarchical energy optimization control architecture based on networked information is designed, and a traffic signal timing model is used for vehicle target speed range planning in the upper system. More specifically, the optimal vehicle speed is optimized by a model predictive control algorithm. Thirdly, a mathematical model of vehicle speed variation in connected and unconnected states is established to analyze the effect of vehicle speed planning on fuel economy. Finally, three learning-based energy optimization control strategies, namely Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) algorithms, are designed under the hierarchical energy optimization control architecture. It is shown that the Q-learning algorithm is able to optimize energy control; however, the agent will meet the ”dimension disaster” once it faces a high-dimensional state space issue. Then, a DQN control strategy is introduced to address the problem. Due to the limitation of the discrete output of DQN, the DDPG algorithm is put forward to achieve continuous action control. In the simulation, the superiority of the DDPG algorithm over Q-learning and DQN algorithms in hybrid electric vehicles is illustrated in terms of its robustness and faster convergence for better energy management purposes.

A Dynamic Control Method for Buck + $LLC$ Cascaded Converter With a Wide Input Voltage Range

DC–DC converters with a wide input voltage range have earned numerous daily applications in the industry. An example is power systems for electric vehicles, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LLC$</tex-math></inline-formula> converters with remarkable features have also been deployed to provide isolation. It is, therefore, common to find a front buck converter cascaded to a back <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LLC$</tex-math></inline-formula> converter, where their intermediate bus voltage is set low to accommodate the lowest input voltage to the buck converter. Unfortunately, this causes efficiency to drop at a high input voltage. To avoid the drop, a reconfiguring strategy has been proposed to convert the front buck converter from full bridge to half bridge, as its input voltage rises above a specified threshold. Furthermore, to ensure a smooth and fast reconfiguration, a dynamic control method has been proposed to direct the intermediate bus voltage to track a reference trajectory with a lower current surge. Besides, the method employs pulse-shift modulation to keep a stable output voltage and proper control initialization to prevent oscillation during the transition. These favorable features and an overall higher efficiency have subsequently been verified through experimental testing with a wide input voltage range.

Pure Electric Vehicle Power System Matching Design and Simulation

According to the design requirements of pure electric drive modification of a certain A-class car and the vehicle’s fundamental parameters, the longitudinal dynamics theory of the vehicle is used to analyze the driving motor, power battery and fixed speed ratio transmission, and other key components. With the help of Cruise simulation software, a pure electric vehicle powertrain model was established and simulation tests were carried out on the New European Driving Cycle, 60km/h isokinetic range cruise, 100km/h acceleration time, and hill-climbing degree and other operating conditions. The results show that the parameters of the power system of pure electric vehicles are reasonably selected, and the power indicators, driving range, and power consumption per 100 kilometers meet the performance design requirements of the whole vehicle, which have theoretical guiding significance for the development of the whole vehicle.

Fault Alarms and Power Performance in Hybrid Electric Vehicles Based on Hydraulic Technology

In order to improve the fault alarm effect on the power performance of hydraulic hybrid electric vehicles (HEV), this paper proposes a fault alarm method for hybrid electric vehicle power performance based on hydraulic technology, builds a hybrid electric vehicle power system model, uses hydraulic technology to extract the characteristic signals of key components, uses support vector mechanisms to build a hybrid electric vehicle classifier, and obtains the fault alarm results for dynamic performance based on hydraulic technology. The results show that the proposed method can improve real-time diagnosis and alarm for engine faults in HEV, and the fault can be diagnosed after 5 s of injection, thus ensuring the dynamic stability of HEV.

Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning

Because of the high-power demand of fuel cell hybrid vehicles, a multi-stack fuel cell system (MFCS) composed of multiple low-power fuel cell stacks (FCSs) instead of a high-power one has become a satisfactory solution. This is due to the modularity of MFCS, which is more reliable and durable. However, the hybrid power system (MHPSS) of an MFCS hybrid electric vehicle possesses not only MFCS, but also batteries to improve the dynamic performance of MHPSS. Due to the difference in characteristics of MFCS and battery, the energy management strategy (EMS) of MHPSS is the key of ensuring its safe and efficient operation. However, most of the existing MHPSS EMSs are complicated in design and complex to compute online. Besides, they also do not consider the robustness of EMS, that is, EMS has the ability to guarantee the safe operation of MHPSS when the MFCS fails. To solve the above problems, this paper proposes an EMS based on independent Q-learning (IQL) which is an algorithm of multi-agent reinforcement learning to maintain battery state of charge (SOC) and minimize hydrogen consumption. The proposed EMS is not only simple in design, but also can guarantee the normal operation of the MHPSS when the MFCS fails, and can be transplanted to execute online on a microcontroller unit or a field programmable gate array. The various parts of the MHPSS model are first built, then the IQL strategy (IQLS) is trained offline in the established model environment, and finally, the IQLS is ported to the hardware-in-the-loop platform for validation. In order to verify the effectiveness of the proposed EMS, the solution of dimensionality-reduced DP is also used to calculate the fuel economy. Through the experimental verification under different initial SOC and driving cycles, it can be seen that the IQLS proposed in this paper can achieve the goal of maintaining battery SOC and minimizing hydrogen consumption, and also has good generalization ability and safe operation ability under fault conditions.

A portable, auxiliary photovoltaic power system for electric vehicles based on a foldable scissors mechanism

In recent years, countries worldwide have actively advocated electric vehicles for environmental protection. However, restrictions on the driving range and charging have hampered the promotion of electric vehicles. This study proposes a portable, auxiliary photovoltaic power system based on a foldable scissors mechanism for electric vehicles. The system includes a photovoltaic power generation module and an electricity transfer module. The photovoltaic power generation module built based on a foldable scissors mechanism is five times smaller than in its unfolded state, improving its portability in its folded state. The electricity transfer module transfers electricity into the cabin via wireless power transfer units and stores electricity in supercapacitors. Solar simulation experiments were conducted to evaluate the system's performance: maximum output power of 1.736 W is measured when the load is 5 Ω, while maximum wireless power transfer efficiency is up to 57.7% with 10 Ω load. An electric vehicle in Chengdu city was simulated for a case study. The results show that the annual output of a single photovoltaic power system can drive the MINIEV for 423.625 km, indicating that the proposed system would be able to supply power for electric vehicles as an auxiliary power supply system.

Active Control of Torsional Vibration during Mode Switching of Hybrid Powertrain Based on Adaptive Model Reference

When the energy management and coordinated control of the hybrid electric vehicle power system are not proper, torsional vibration problems will occur in various working states, especially in the mode switching process. These vibrations will affect the comfort, economy and emission of vehicles. In order to suppress the torsional vibration, this paper studied the active vibration control algorithm for the hybrid powertrains under the switching process of pure electric mode to hybrid mode. Primarily, the clutch combination process was divided into five stages and the dynamic models of the transmission system of each stage were established, respectively. Moreover, the principle of model reference adaptive control was analyzed. The applicability of the method to the torsional vibration of the driveline during mode switching was described. Furthermore, the clutch free displacement phase was used as the reference model. A model reference adaptive torsional vibration controller was built based on the controlled model. Finally, the efficacy of this active method for vibration reduction was simulated. The simulation results show that torsional vibration is most likely to occur in the speed coordination stage and the full participation stage. In these two stages, the designed controller can reduce the fluctuation of motor speed by 93.2% and 97.5%, respectively, the engine speed by 79.6% and 77.4%, respectively, the motor acceleration by 96.7% and 82.3%, respectively, and the engine acceleration by 88.9% and 82.3%, respectively. In addition, the controller can reduce the impact degree of the transmission system to within ±1 m/s3.

Design Process of Electric Vehicle Power System

This paper presents the research on the process of designing and optimizing the powertrain of electric vehicles, such as the general arrangement of electric vehicles, the design of electric motors, transmission systems, battery systems, as well as selecting the appropriate layout design. In addition, the article analyzes the computational models of electric drive systems, energy systems and calculates the performance of these systems in accordance with actual use. Finally, design and simulation calculations of the powertrain and energy of electric vehicles are performed using Simcenter Amesim software. Keywords: Electric vehicle, battery, electric motor, Simcenter Amesim

Energy Management Strategy in 12-Volt Electrical System Based on Deep Reinforcement Learning

The increasing electrification in motor vehicles in recent decades can be attributed to higher comfort and safety demands. Strong steering and braking maneuvers reduce the vehicle’s electrical system voltage, which causes the vehicle electrical system voltage to drop below a critical voltage level. A sophisticated electrical energy management system (EEMS) is needed to coordinate the power flows within a 12-volt electrical system. To prevent the voltage supply from being insufficient for safety-critical consumers in such a case, the power consumption of several comfort consumers can be reduced or switched off completely. Rule-based (RB) energy management strategies are often used for this purpose, as they are easy to implement. However, this approach is subject to the limitation that it is vehicle-model-specific. For this reason, deep reinforcement learning (DRL) is used in the present work, which can intervene in a 12-volt electrical system, regardless of the type of vehicle, to ensure safety functions. A simulation-based study with a comprehensive model of a vehicle electric power system is conducted to show that the DRL-based strategy satisfies the main requirements of an actual vehicle. This method is tested in a simulation environment during driving scenarios that are critical for the system’s voltage stability. Finally, this is compared with the rule-based energy management system using actual vehicle measurements. Concluding measurements reveal that this method is able to increase the voltage at the most critical position of the 12-volt electrical system by approximately 0.6 V.

Optimal Energy Efficient Control of Pure Electric Vehicle Power System Based on Dynamic Traffic Information Flow

To improve the energy efficiency and driving condition adaptability of pure electric vehicles (EVs) in a complex traffic environment, simulation conditions that can dynamically update traffic information based on measured data were designed. Next, to increase the pure EV’s driving range, an energy efficient optimal control framework based on dynamic traffic information flow was proposed, which includes the traffic information layer, the target planning layer, and the prediction and control layer. In the traffic information layer, the vehicle receives and updates the traffic information data. The time-domain information and distance domain information are converted in the target planning layer and the optimal state-of-charge (SOC) reference trajectory is planned through the remote cloud computing system. The traffic information is predicted in the prediction and control layer, and an SOC scroll tracking control method is proposed to continuously control the power system to achieve the goal of optimal energy consumption and economic driving. Finally, under a variety of real road simulation conditions with dynamic traffic information, we verify that the economic performance of the proposed control framework is equivalent to that of the one based on dynamic programming algorithms and has the potential for online real-time control.