Thermal Management System For Electric Vehicles Research Articles

Research and evaluation of the arrangement of heat exchangers in electric vehicle thermal management systems for CO2 refrigerant

The application of transcritical CO2 cycle in the thermal management system of electric vehicles has become increasingly prevalent, but the existing thermal management system architecture has not been optimized to specifically enhance the performance of CO2 refrigerant. This paper analyzes the impact of various heat exchanger arrangements on CO2 thermal management system and evaluates system configurations based on a new performance index, cycle coefficient of performance. The effects of air temperature, ambient temperature, and heat load on system performance are conducted with a high precision model. The chiller is connected in parallel-series with indoor heat exchangers, resulting in a maximum coefficient of performance improvement of 27.89% at an indoor air inlet temperature of 10 °C, and a maximum coefficient of performance improvement of 21.69% at an ambient temperature of 5 °C, under simultaneous heating conditions. The radiator is placed on the windward of gas cooler, leading to a maximum coefficient of performance increase of 4.85% in heating mode, but a maximum coefficient of performance decrease of 9.84% in cooling mode. The evaluation of these configurations across different operating cycles reveals that the parallel-series connection is well-suited for the new European driving cycle, showing an 18.4% improvement in cycle coefficient of performance compared to the series-parallel connection. The windward arrangement of radiator proves effective for highway fuel economy test cycle in winter and the new European driving cycle in summer. This study provides effective instruction for configuration design and evaluation of transcritical CO2 vehicle thermal management systems.

Performance investigation of electric vehicle thermal management system with thermal energy storage and waste heat recovery systems

This study investigates the electric vehicle thermal management system performance, utilizing thermal energy storage and waste heat recovery, in response to the imperative shift toward carbon-free electric vehicles to overcome the challenge of low energy efficiency in the thermal management system. The heat generation according to the electrical load on the battery was calculated based on experimental data. The thermal performances of the cabin, power electronic thermal management, and battery thermal management system were explored under various operating conditions at different ambient temperatures. A fully charged thermal energy storage system, including low- and high-temperature phase change materials and waste heat recovery systems, was applied in summer and winter. The total energy consumption for cooling and heating saved to a maximum of 65.9 % in summer and 26.2 % in winter. The mileage extension rate was calculated by distributing the power demand according to the vehicle exterior and motor performance of the battery. Thus, by directly saving the thermal parasitic electrical energy and using it to extend the driving mileage, the electric vehicle achieved a mileage extension of 24.2 % in summer and 18.6 % in winter.

Using Latent Heat Storage for Improving Battery Electric Vehicle Thermal Management System Efficiency

<div>One of the key problems of battery electric vehicles is the risk of severe range reduction in winter conditions. Technologies such as heat pump systems can help to mitigate such effects, but finding adequate heat sources for the heat pump sometimes can be a problem, too. In cold ambient conditions below −10°C and for a cold-soaked vehicle this can become a limiting factor. Storing waste heat or excess cold when it is generated and releasing it to the vehicle thermal management system later can reduce peak thermal requirements to more manageable average levels. In related architectures it is not always necessary to replace existing electric heaters or conventional air-conditioning systems. Sometimes it is more efficient to keep them and support them, instead. Accordingly, we show, how latent heat storage can be used to increase the efficiency of existing, well-established heating and cooling technologies without replacing them. We investigate different possibilities for the integration of phase change materials into a baseline battery electric vehicle thermal management system and compare the resulting benefits.</div>

Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies

The electric vehicle thermal management system (EVTMS) plays a crucial role in ensuring battery efficiency, driving range, and passenger comfort. However, EVTMSs still face unresolved challenges, such as accurate modeling, compensating for temperature variations, and achieving efficient control strategies. Addressing these issues is crucial for enhancing the performance, reliability, and energy efficiency of electric vehicles. Therefore, this study presents a cooling EVTMS model, considering both the battery pack temperature and the cabin comfort, and utilizes the prediction of neural network as a feedforward in a fuzzy PI controller to compensate for the model temperature variations. The simulation results reveal that, compared with PI controller and MPC, the neural network fuzzy PI (NN-Fuzzy PI) controller can well predict and compensate for the system’s nonlinear characteristics as well as the time-delay caused by heat transfer, achieving superior control performance and reducing energy consumption. The battery pack temperature and PMV fluctuations are effectively constrained within [−0.5, 0.5] and [−0.1, 0.1], reducing up to 150% and 164%, and the energy consumption of the pump and compressor are reduced by up to 0.23 and 100.1 KJ, with ranges of 18% and 2.68%. Meanwhile, the neural network feedforward also works effectively in different controllers. The findings of this research can provide valuable insights for TMS engineers to select advanced control strategies.

Research on prediction model of electric vehicle thermal management system based on particle swarm optimization- Back propagation neural network

The variation of temperature in various components of the Electric Vehicle Thermal Management (EVTM) system is the main factor affecting energy consumption. A good temperature prediction method is a prerequisite for optimizing energy strategies. The temperature variation of each component in the thermal management system is influenced by various factors, such as ambient temperature, vehicle speed, and air conditioning compressor speed. Therefore, this paper proposes a temperature prediction method based on Particle Swarm Optimization (PSO) and Back propagation Neural Network (BP). The PSO-BP method is used to update the weights and thresholds of the neural network. Real-time data collected from road experiments are used to ensure the accuracy of the influencing factors. Simulation results show that compared to the BP neural network, the proposed PSO-BP prediction method reduces the prediction errors by 66%, 75%, and 25% respectively.

Control Strategy of New Energy Vehicle Interior Thermal Management System Based on Intelligent Assisted Algorithms

<p>Reasonable and efficient utilization of pure electric vehicle thermal management system can improve the battery utilization rate of new energy vehicles, thereby increasing the vehicle’s range and charging anxiety. This article proposes a new tram thermal management system scheme for temperature management in new energy vehicles, and designs corresponding thermal management system control strategies based on fuzzy adaptive PID control method. Finally, in order to verify the effectiveness of the management plan and control strategy, Matlab software was used for simulation. The simulation results showed that the intelligent control method proposed in this paper can increase the battery utilization rate by 17%.</p> <p> </p>

A flexible double network aerogel reinforced by SiO2/ZrO2 fibers paper with excellent thermal insulation at high-temperature

Silica aerogel composites reinforced by fibers (SRF) have been used as thermal insulation walls to suppress lithium-ion batteries (LIBs) thermal runaway in electric vehicles. Increasing energy density of lithium-ion batteries brings out the requirement of higher temperature resistance and better thermal insulation performance of silica aerogel composites. Herein, we report a simple strategy for preparing aerogel reinforced by ZrO2/SiO2 fibers papers (AZSP) with double network structure for thermal insulation at high-temperature by conventional papermaking process and sol-gel method. The papermaking process made long fibers oriented perpendicular to thickness and opacifier ZrO2 fibers dispersed evenly in composites and mesoporous silica aerogels were filled in nanofibers skeleton. AZSP exhibited an excellent thermal insulation property at high-temperature with thermal conductivity of 0.039 W/(m·K) at 500 °C. The nanofibers reinforcement had been found to impart several desirable attributes to the material, including high flexibility, smooth surface suitable for stamping, painting, writing, or printing, and tensile stress of 0.74 MPa, thus rendering it an attractive option for diverse applications. Furthermore, the exceptional thermal stability of AZSP was demonstrated by the absence of any significant shrinkage deformation upon exposure to a butane flame. These remarkable properties, in conjunction with its high flexibility and thermal insulation performance, made AZSP a promising candidate for utilization as an insulation material for electric vehicle thermal management systems to mitigate the risk of thermal runaway.

A Review of Sensor Applications in Electric Vehicle Thermal Management Systems

With the rapid development of the automotive industry, the application of sensors is of great importance in maintaining the reliability of electric vehicles and ensuring the safe operation of electric vehicles. Faced with the increasing data of thermal management system condition monitoring, sensor detection is widely used in the monitoring of electric vehicle thermal management system. In recent years, a large number of related studies and contributions to the literature have been published. Although a number of reviews have summarized this, these reviews lack an overview of the issues and methods raised in these studies. This paper reviews recent sensor applications for electric vehicle thermal management systems. Currently, battery internal sensors, battery external sensors and related multi-sensor fusion, traditional motor sensors, positionless motor sensors, and component-level sensors of air conditioning systems are the main application sensors in the field of thermal management systems. This article introduces the basic principles of each type of sensor, reviews the relevant applications of various thermal management modules, and summarizes the usage characteristics of each type of sensor. The main problems faced by the existing research on the application of thermal management system-based sensors, such as the detection accuracy of traditional sensors and the detection stability of advanced sensors, are summarized, and the solutions proposed by the existing research are also summarized. Finally, some future research directions, trends, and hotspots are outlined. It is hoped that this review can help readers to understand the problems and existing solutions for thermal-management-system-based sensor applications, and to conduct related research more effectively.

System design and analysis methods for optimal electric vehicle thermal management

The thermal management system (TMS) in an electric vehicle (EV) encounters many challenges due to the stringent thermal requirements of EV components and concurrent range reduction in cold conditions. Efficient systems require thermal architectures with highly interconnected components to satisfy a wide range of operating conditions. This work addresses the lack of a design methodology for clean sheet EV TMS designs by introducing a decision tree to enable analysis-driven design space exploration using simulation and modeling tools. A versatile simulation framework is developed inside MATLAB-Simulink using Simscape to capture dynamic physical interactions of coolant and refrigerant thermal systems and is validated at both the component and system-levels. Direct and indirect configurations for cabin conditioning are analyzed to compare relative performance. The indirect configuration is found to have a 1.6–1.8x longer conditioning time and a coefficient of performance decrease of 18–31% and 31–41% for heating and cooling, respectively. A previously unexplored general integrated loop architecture is created for concept-level analysis of various EV TMS configurations. Operating modes are formulated for all possible driving conditions and are switched with a control strategy. A detailed analysis is done for an idealized system to study the system-level performance, and critical modes are identified for different driving conditions. The range increase of the heat pump relative to positive temperature coefficient heating varies from 4 to 33%, with an extra 1–4.4% possible by idealized waste heat recovery. Waste heat recovery also increases the heating capacity by 28%, making heat pump operation feasible at low temperatures. The applicability of the decision tree in the context of various EV TMS designs of leading manufacturers and existing literature is also discussed.

Life cycle climate performance evaluation of electric vehicle thermal management system under Chinese climate and driving condition

Electric vehicles have become an effective alternative to traditional fuel vehicles due to their environmental friendship and energy security. Unlike mobile air conditioning, electric vehicle thermal management systems are responsible for the temperature of both the cabin and the electrical system. This paper discusses how the life cycle climate performance of a thermal management system can be evaluated to reflect its environmental performance and guide the direction of development. The GREEN-EVTM-LCCP model is developed to assess greenhouse gas emissions over the life cycle of the EVTM systems and to standardize the calculation of energy consumption based on vehicle end-of-life mileage. For efficient management and wide temperature coverage, the Amesim simulation platform is centered on refrigerant injection heat pump systems. The results show that urban climate has the greatest impact on the greenhouse gas emissions of thermal management systems at 54.80% in China, followed by refrigerant type, energy efficiency, and driving conditions. Under typical Chinese climate and driving conditions, the thermal management system has a life cycle equivalent emission of 18,664 kg. The use of more environmentally friendly refrigerants and improved system cycle efficiency is now effective measures to reduce greenhouse gas emissions.

A two-stage eco-cooling control strategy for electric vehicle thermal management system considering multi-source information fusion

A two-stage eco-cooling control strategy for electric vehicle thermal management system considering multi-source information fusion

Co-regulation of integrated thermal management based on refrigerant cooling for electric vehicles

As an essential part of an electric vehicle thermal management system, collaborative regulation of power battery thermal management and AC temperature is imperative to maximize the vehicle's driving range and energy utilization effectiveness. This work proposes a joint control pattern for the compressor (Com) and variable opening valves (VOVs) with a dual-evaporator system. Specifically, the compressor rotation speed is the dominant control variable, and the battery's thermal behavior and system COP are investigated. The analytic results demonstrate that when the system is under moderate and high thermal loads, the Com-VOVB-VOVC strategy can regulate the temperature of both the battery and AC temperature. Meanwhile, the goal of low energy consumption is optimized. Nevertheless, when the thermal load is low, the Com-VOVB strategy can attain steady temperature regulation for the dual-evaporator and decrease the complexity of the system control. In addition, a Map approach to the temperature regulation is presented, enhancing the effectiveness and dynamism of thermal management control. The common control method for an integrated thermal management system can effectively confront system thermal load changes and decrease energy consumption.

Parametric study on thermal management system for the range of full (Tesla Model S)/ compact-size (Tesla Model 3) electric vehicles

Electric vehicle is slowly taking over the market share of internal combustion engine vehicle around the globe and range extension study turns into a one key research area. Electric vehicle thermal management system plays a large role in driving range, whereas existing literature mainly focus on component level and lack the vehicle level range analysis. This study conducts a parametric study on the vehicle level thermal management and its impacts on the range of two vehicles (full size - Tesla Model S/ compact size – Tesla Model 3). Vehicle level thermal system model is built considering air conditioning refrigerant loop, indirect battery liquid cooling loop, and cabin cooling air loop. In addition, vehicle propulsion system model is built and utilized in the range simulation. Five feedback controllers are designed for the thermal management system to control the temperature of battery pack, cabin climate, condenser subcool, evaporator superheat, and chiller superheat. Various factors are considered in the parametric study including air conditioning system on/off switch, vehicle size, driving cycle, ambient temperature, cabin target temperature, and battery pack target temperature. In the results analysis, the performance of five feedback controllers is evaluated in step response and a driving cycle. Energy consumption of aerodynamic drag, rolling resistance, thermal management, regenerative braking, and auxiliary load are discussed in the parametric study. The results show that turning air conditioning system on could potentially reduce the EV range by 38–45 % at city driving condition. In addition, Ambient temperature shows significant nonlinear impact on EV range and the impact gets worse as temperature turns higher. For Tesla Model 3, when the ambient temperature rises from 30 °C to 35 °C and then to 40 °C, the range drops by 3.3 % and 13.4 %, respectively. Compared with ambient temperature, cabin temperature and battery pack temperature settings have less impact on the range. The code utilized in this study is made open source via Github (https://github.com/binxuAI/EV_TMS), which can be utilized as the guidance in the thermal management range impact analysis and concept design of electric vehicle.

A Computationally Efficient Algorithm for Perturbed Dynamic Programs (A-PDP)

This letter proposes an algorithm for solving perturbed dynamic programs, where first-order corrections are applied to a pre-computed optimal strategy and its corresponding value function using local quadratic approximation. The technique is developed to handle perturbations of external inputs and parameters affecting system dynamics, objective and constraint functions, allowing the application to a wide variety of perturbed problems. The method is applied to the energy optimization of an electric vehicle thermal management system, formulated as a constrained dynamic program where external conditions and target set-point are perturbed from their nominal values. Simulations result show 80% reduction in computation time from a Dynamic Programming (DP) solution, with only minimal effect on the overall controller performance.

Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans.

Pseudo-optimal discharge pressure analysis of transcritical CO2 electric vehicle heat pumps due to temperature glide

• A pseudo-optimal discharge pressure for electric vehicle heat pumps was proposed. • The principle of pseudo-optimal discharge pressure was explained. • Pseudo-optimal discharge pressure exists in subcritical and transcritical states. • The effects of conditions on pseudo-optimal discharge pressures were revealed. Performance improvement of electric vehicle heat pumps can enhance thermal comfort and reduce the power consumption of electric vehicles, hence stimulating their development. In this paper, the cyclic characteristics of an electric vehicle heat pump were studied under different operating modes and working conditions. Different from the optimal discharge pressure of conventional CO 2 systems which was determined by the variation gradient characteristics of the transcritical isotherm, the discharge pressure of electric vehicle heat pumps at the peak COP value was defined as a pseudo-optimal discharge pressure and was determined by the obvious temperature glide at the outlet of the gas cooler. This pseudo-optimal discharge pressure existed at both transcritical and subcritical states. The difference in the pseudo-optimal discharge pressure between the subcritical or transcritical states was first evaluated. Then an in-depth analysis of the influence of key parameters on the pseudo-optimal discharge pressure was studied to develop an accurate prediction method since the traditional prediction methods were not applicable. The results showed that pseudo-optimal discharge pressure increased with the ambient temperature, inlet air temperature, and supply air temperature. This study provides valuable information for the comprehensive performance optimization of an electric vehicle thermal management system.

An optimal thermal management system heating control strategy for electric vehicles under low-temperature fast charging conditions

• An improving fast charging protocol is proposed to avoid lithium plating. • A multi-stage heating control strategy based on the battery temperature is developed. • Dual crucial objectives, heating energy consumption and charging time are balanced. • An optimal heating control strategy for low-temperature fast charging is proposed. • The optimal control strategy can reduce heating energy consumption by 11.61%. Electric vehicle thermal management system (EVTMS) and its corresponding control strategy are essential to ensure the battery performance at low temperatures. Due to the complexity of the low-temperature fast charging process, it is necessary to comprehensively consider the battery heating and charging performance when developing the heating strategy. However, most studies only focus on the charging time. In this context, an EVTMS is investigated in this paper, and the simulation models are established. In addition, the battery heating and charging performance under low-temperature fast charging conditions are analyzed by experiment and simulation. An improved fast charging protocol for reducing the battery-lifetime degradation is proposed. Moreover, an EVTMS multi-stage heating control strategy is developed to realize the cooperative control with the improved charging strategy. Taking dual crucial yet conflicting factors, heating energy consumption and charging time, as the optimization objectives, the proposed EVTMS control strategy is optimized using genetic algorithm. And an optimal heating control strategy is obtained. The results show that compared with the strategy only considering the charging time, the optimal strategy can reduce the heating energy consumption by 11.61% and increase the charging efficiency by 1.2%, only with the increase of the charging time by 2.98 min.

Design and analysis of electric vehicle thermal management system based on refrigerant-direct cooling and heating batteries

A thermal management system (TMS) based on R134a refrigerant is proposed, which not only meet the thermal requirements of cabin, but also refrigerant-directly cool and heat battery. Compared with the traditional electric vehicle (EV) TMSs, an electronic expansion valve (EXV) is equipped after the battery cooling/heating plate in the refrigerant branch circuit. The mainly working modes of the TMS and their control methods are described in detail. The paper applies mathematical one-dimension simulation method to analyze the feasibility of mainly working modes, including battery preheating mode, mixed heating mode, low-temperature mixed heating mode. The results show that the TMS is completely feasible to balance the heat distribution between cabin and battery by controlling the EXVs opening. In the battery pre-heating mode, the relationship between refrigerant state of cooling/heating plate outlet and the battery temperature uniformity is analyzed, and a control strategy to improve the battery temperature uniformity is proposed. In low temperature environment, when the TMS switches from cabin heating mode to mixed heating mode, the cabin air temperature will drop sharply. This is because the battery temperature is too low, that pressure of TMS is too low to maintain the heating demand of the cabin.

Investigation on the performance enhancement of electric vehicle thermal management system utilizing floating loop with finite heat exchanger size

This study suggests an electric vehicle heat pump and thermal management system employing a floating loop. The floating loop manages the thermal state of power electronics and electric motors by utilizing the liquid refrigerant at the condenser outlet. This loop recovers the waste heat generated by power electronics and electric motors in winter and enhances the cooling performance in summer through the superior cooling performance of evaporative heat transfer. Configurations of heat pump and thermal management system are presented with operating schematic in summer and winter. To verify the performance of the suggested system, the heat pump and thermal management system model is established based on experimentally validated heat pump component models and electric device models. Heat exchangers are reallocated within a finite volume to investigate the effect of thermal load shift from the coolant to the refrigerant. The result shows that the heat pump system utilizing a floating loop with the largest refrigerant heat exchanger can save power consumptions in winter up to 27.7% and 5.8% in summer while maintaining the thermal state of electric devices within the appropriate range.

Life Cycle Climate Performance Evaluation of Electric Vehicle Thermal Management System Under Chinese Climate and Driving Condition

Life Cycle Climate Performance Evaluation of Electric Vehicle Thermal Management System Under Chinese Climate and Driving Condition