Management System For Electric Vehicles Research Articles

Novel battery thermal management system for electric vehicles with a loop heat pipe and graphite sheet inserts

• New battery thermal management proposed with Loop Heat Pipes and graphite sheets. • Loop Heat Pipe is a passive technology so no need for parasitic power or maintenance. • Feasibility verified through numerical model validated by experimental demonstrator. • Proposed design reduces maximum temperature by 3.6 °C compared to liquid cold plate. • Numerical model proved useful as design tool for working fluid and materials. Aiming to improve on key Electric Vehicles issues, such as maximum temperature during fast charging, parasitic power and cost reduction, an innovative thermal management system for a 3-cell battery module, including a flat plate Loop Heat Pipe and graphite sheet inserts, is presented. The proposed Loop Heat Pipe, located at the bottom of the module, can transfer efficiently up to 150 W from the battery module to a remote heat exchanger connected to the HVAC chiller of the vehicle. Graphite sheets have the twofold function of promoting heat transfer along the cell plane direction, while hindering it in the transverse direction. Since Loop Heat Pipes need no electrical power, this design allows for a reduction of parasitic power consumption, as well as a reduction in the need of maintenance, with respect to standard forced air or liquid-based thermal management systems. The design feasibility was verified thanks to a Lumped Parameter Model, which was validated against in-house experimental data, using a copper/copper flat plate Loop Heat Pipe and two different working fluids, ethanol and water. Results showed that this design complies with the battery thermal requirements both at pack and cell level, with maximum temperature during fast charge and temperature spread across the cell being 31.5 °C and 2 °C, respectively, during ambient temperature tests. When compared against a liquid cold plate design, this new design lowered the maximum temperature after fast charge by 3.6 °C. Finally, the developed model proved able to predict the effects of design parameters on the thermal performance of the system.

A vapor compression-adsorption thermal management system for electric vehicle: Concept and working fluid pairs

Thermal management system that usually based on vapor compression refrigeration system plays a significance role in electric vehicles. The system can be also based on the adsorption refrigeration, which can work for battery waste heat recovery. However, the adsorption refrigeration can hardly operate continuously. To combine the advantage of vapor compression and adsorption technology, a vapor compression-adsorption thermal management system (VCATMS) is proposed to provide battery waste heat recovery for cooling in summer and preheating in winter. Therefore, the cooling/preheating load reduces and electricity is saved. Schematic and detailed working modes are presented. The performance prediction method of the system is established, and the performances of different working fluid pairs are investigated. The results show that the activated carbon (AC)-R410A working fluid pair has the best performance because of its good equilibrium adsorption characteristic. Electricity saving can reach 31.3 kJ on recovery mode in summer and 67 kJ on preheating mode in winter, respectively. As for temperature control, battery can be preheated at around 30 °C under −30 °C to 10 °C ambient temperature without any electricity consumption. Besides, heat transfer performance of battery heat exchanger has an adverse effected on AC, which can be weaken by impregnating high thermal conductivity material.

Design and optimization of a hybrid battery thermal management system for electric vehicle based on surrogate model

The hybrid thermal management scheme for lithium-ion battery combining the advantages of various thermal management strategies has been widely adopted. However, due to the complex influence parameters involved in the hybrid thermal management system, its optimal design has become a difficult problem. In this study, a hybrid thermal management system based on phase change material (PCM), liquid cooling, and heat pipe is first designed, and then a precise and reliable numerical heat transfer model is established. In order to optimize the system and improve the optimization efficiency, the Adaptive-Kriging-High dimensional model representation (HDMR) method is used to construct a surrogate model of the thermal management system, and the influencing factors sensitivity analysis and optimization design of the hybrid thermal management system are also conducted. The results show that the thermal conductivity of PCM, the thickness of PCM, the length of heat pipe and the velocity of inlet water have a significant influence on the maximum temperature and temperature difference of the battery system. According to the optimization design of these four factors based on the multi-objective particle swarm optimization (MOPSO), it is found that the optimized thermal management system has the best ability to dissipate heat and maintain temperature uniformity as compared to the original design. In addition, this optimization system has the ability to prevent thermal runaway propagation under the condition of thermal abuse conditions. With these prominent performances, the proposed method is expected to provide insights into the engineering design and optimization of the battery thermal management system for electric vehicle.

A Study on Battery Controller Design for the Estimation of State of Charge (SoC) in Battery Management System for Electric Vehicle (EV)/Hybrid EV (HEV)

A Study on Battery Controller Design for the Estimation of State of Charge (SoC) in Battery Management System for Electric Vehicle (EV)/Hybrid EV (HEV)

IOT Based Battery Management System for Electric Vehicle

IOT Based Battery Management System for Electric Vehicle

Retraction: A review on passive cooling techniques for lithium-ion battery thermal management system of electric vehicle (IOP Conf. Ser.: Mater. Sci. Eng. 1145 012046)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation.IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process.IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further.[1] Cabanac G, Labbé C and Magazinov A 2021 arXiv:2107.06751v1 Retraction published: 23 February 2022

A review on passive cooling techniques for lithium-ion battery thermal management system of electric vehicle

Li ion batteries have gained popularity in electric vehicle application in past few years because of its high energy density and long-life cycle. The high energy density Li ion batteries are also generating a large amount of heat which needs to be dissipated for the stability, longevity of the battery and safety of vehicle. Thus, a battery thermal management system (BTMS) is very much essential for the battery system. The objective of BTMS is to maintain the cell temperature and thus improve life cycle of battery system. It has been reported that the battery pack has better thermal stability and lifetime when operated at a temperature range of 15 to 35 °C and maximum cell temperature difference of 5 °C. Among battery cooling techniques, passive approaches are considered as less complex and less expensive. In this context, a review on passive cooling methods for BTMS has been discussed in this paper. Subsequently, a summary of characteristic parameter for an efficient passive BTMS has been provided.

Fuzzy Controller based Hybrid Energy Management System for Electric Vehicle

Fuzzy Controller based Hybrid Energy Management System for Electric Vehicle

Research on electric vehicle thermal management system with coupled temperature regulation between crew cabin and power battery pack

Based on the structure of the thermal management system for electric vehicles, complete the design of the thermal management system for the whole vehicle, and realize the coupling temperature regulation between the vehicle cabin and the power battery pack. A direct cooling system model containing electric compressors, electronic expansion valves, heat exchangers, power battery packs, and other components coupled to the air conditioning system is established. Based on this, a vehicle thermal management model of the entire vehicle including electric vehicle, electric motor, high and low voltage network, vehicle cabin, air conditioning system, and power battery pack is completed. Develop the logic threshold control strategy, compressor speed control strategy, and electronic expansion valve opening control strategy for the vehicle thermal management system. Through the comparative analysis of the temperature control effect of the thermal management system on the cabin and the power battery pack under different ambient temperatures, the effect of different temperatures on the vehicle range is analyzed. The results show that this vehicle thermal management system can meet the requirements for battery pack heat dissipation and vehicle cabin refrigeration.

Multidisciplinary optimal design of prismatic lithium‐ion battery with an improved thermal management system for electric vehicles

AbstractThe reliability and performance of lithium‐ion battery implemented in electric vehicles is greatly influenced by temperature. However, there is yet to have a systematic battery thermal strategy due to the poor understanding of battery thermal behavior. Although several heat management systems have been proven to be functional in regulating the operating temperature effectively, its capability would be overtaken by the escalating internal resistance due to ageing after a certain period. Hence, an integration of finite element method (FEM) with particle swarm optimization (PSO) approach is proposed, more specifically for phase change material‐based cooling system, by performing multiobjective optimization of battery pack volume and temperature effects. As such, the multidisciplinary design optimization is considered level by level for the battery pack in order to prevent premature ageing. After justifying the lithium‐ion composition, the prismatic battery pack is modeled parametrically and its thermal responses assessed using FEM. The dimension and arrangement of battery cells are assigned as design variables and constrained in a fixed range, which will then be iterated through online optimization whereby the temperature difference, SD and volume are objectives and minimized by using PSO algorithm. The optimization results are summarized: maximum temperature difference is 0.8208383 K, SD is 0.17239 K, and area is 53 082 cm3. The results also imply that the numbers and columns for cells have a significant impact on the effectiveness of heat transfer which may be correlated to its cell dimension.

Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods

The prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long-term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. Then, we propose a three-level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real-time data, and detection data. The electric vehicle's factory parameters are used for determining the life curve of the electric vehicle and its components, the real-time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. This health management database is established to help make condition-based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole-life-cycle life prediction and fault diagnosis of electric vehicles.

IoT Based Battery Management System for Electric Vehicles

The use of the Internet of Things (IoT) in monitoring the operation of an electric vehicle battery is described in this study. It is obvious that an electric car is completely reliant on a battery as a source of energy. The amount of energy given to the vehicle, on the other hand, is gradually reducing, resulting in a performance decline. This is frequently a major source of concern for battery manufacturers. The idea of monitoring the performance of the vehicle using IoT techniques is proposed in this study so that monitoring can be done directly. The suggested IoT-based battery monitoring system has two main components: a monitor and an interface. The system is capable of detecting deteriorated battery performance and sending notification messages to the user for further action, based on experimental results.

Experimental and simulation study of liquid coolant battery thermal management system for electric vehicles: A review

Lithium-ion batteries are among the most commonly used batteries to produce power for electric vehicles, which leads to the higher needs for battery thermal management system (BTMS). There are many key concerning points for the users of these batteries, which include reliability, safety, life cycle, and the operating temperature of the batteries. It is known through review that water is the best coolant for batteries, in which the maximum temperature was 43.3°C while the temperature of the coolant was 30°C during the discharge rate of battery pack at 4 C. An effective cooling system is necessary in prolonging the battery life, which controls the temperature difference between the batteries and the peak temperature of the battery. This review paper aims to summarize the recent published papers on battery liquid-cooling systems, which include: battery pack design, liquid-cooling system classification, and coolant performance. Furthermore, this study discusses other factors related to the recent studies, such as the properties and applications of different liquid coolants (oil and water) under the classification of liquid-cooling system and the difference between passive and active, indirect and direct, and external and internal cooling systems are discussed. Moreover, this paper investigates the effect of temperature on the performance of battery in three aspects: low, high, and differential temperatures. Moreover, the study provides a systematic review of liquid-based systems for direct and indirect contact modes.

Induction Heater Based Battery Thermal Management System for Electric Vehicles

The life and efficiency of electric vehicle batteries are susceptible to temperature. The impact of cold climate dramatically decreases battery life, while at the same time increasing internal impedance. Thus, a battery thermal management system (BTMS) is vital to heat and maintain temperature range if the electric vehicle’s batteries are operating in a cold climate. This paper presents an induction heater-based battery thermal management system that aims to ensure thermal safety and prolong the life cycle of Lithium-ion batteries (Li-Bs). This study used a standard simulation tool known as GT-Suite to simulate the behavior of the proposed BTMS. For the heat transfer, an indirect liquid heating method with variations in flow rate was considered between Lithium-ion batteries. The battery and cabin heating rate was analyzed using the induction heater powers of 2, 4, and 6 kW at ambient temperatures of −20, −10, and 0 °C. A water and ethylene glycol mixture with a ratio of 50:50 was considered as an operating fluid. The findings reveal that the thermal performance of the proposed system is generally increased by increasing the flow rate and affected by the induction heater capacity. It is evident that at −20 °C with 27 LPM and 6 kW heater capacity, the maximum heat transfer rate is 0.0661 °C/s, whereas the lowest is 0.0295 °C/s with 2 kW heater capacity. Furthermore, the proposed BTMS could be a practical approach and help to design the thermal system for electric vehicles in the future.

A hybrid battery thermal management system for electric vehicles under dynamic working conditions

Lithium-ion batteries have been widely used to propel electric vehicles (EVs) owing to their high energy density, long lifespan, and high stability. However, the inevitable battery heat generation, particularly when there is a rapid increase in power under dynamic working conditions, threatens the safety and performance of EVs. In this study, we develop a hybrid battery thermal management system incorporating micro heat pipe arrays, convective air, and intermittent spray water. The heat pipes siphon the heat from the inside of the battery pack to the outside, and convective air dissipates heat during the normal operation of the EVs, while further cooling is achieved via intermittent spray water at high-power operations. For a 75 Ah lithium-ion battery pack under dynamic working conditions, the proposed hybrid system enables the maximum temperature to be reduced to 29.6°C and the temperature non-uniformity to be 1.6°C, which are 21% and 57% lower than those of thermal management systems without water spraying functions, respectively. Additionally, the energy consumption of the hybrid thermal management system is 4.9 Wh, only accounting for 1.8% of the total battery pack power capacity. Given these advantages, it is expected that the proposed thermal management system is a promising tool to address the practical thermal problems of lithium-ion battery packs used in EVs.

Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries

Energy storage is recognized as a key technology for enabling the transition to a low-carbon, sustainable future. Energy storage requires careful management, and capacity prediction of a lithium-ion battery (LIB) is an essential indicator in a battery management system for Electric Vehicles and Electricity Grid Management. However, present techniques for capacity prediction rely mainly on the quality of the features extracted from measured signals under strict operating conditions. To improve flexibility and accuracy, this paper introduces a new paradigm based on a multi-domain features time-frequency image (TFI) analysis and transfer deep learning algorithm, in order to extract diagnostic characteristics on the degradation inside the LIB. Continuous wavelet transform (CWT) is used to transfer the one-dimensional (1D) terminal voltage signals of the battery into 2D images (i.e., wavelet energy concentration). The generated TFIs are fed into the 2D deep learning algorithms to extract the features from the battery voltage images. The extracted features are then used to predict the capacity of the LIB. To validate the proposed technique, experimental data on LIB cells from the experimental datasets published by the Prognostics Center of Excellence (PCoE) NASA were used. The results show that the TFI analysis clearly visualised the degradation process of the battery due to its capability to extract different information on electrochemical features from the non-stationary and non-linear nature of the battery signal in both the time and frequency domains. AlexNet and VGG-16 transfer deep learning neural networks combined with stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms are examined to classify the obtained TFIs at different capacity values. The results reveal that the proposed scheme achieves 95.60% prediction accuracy, indicating good potential for the design of improved battery management systems.

Structure design and effect analysis on refrigerant cooling enhancement of battery thermal management system for electric vehicles

A liquid-cooled battery thermal management system, consisting of a refrigerant flow through a cold plate, allows the battery to recharge cycles at aggressive rates and temperatures. Based on the validated fluid-solid coupling model, the design and evaluation of the refrigerant-based thermal management structure, and the analysis and control of characteristic parameters under different conditions are explored in this paper. A thermal management structure, involving the tube side and cross-section of the cold plate, reaches a compromise between heat exchange quantity, required power consumption, mass, volume. For the controllable parameters, results show that the refrigerant mass flux has an optimal solution for thermal management, yet the evaporation temperature is the limit of system stability and control. Based on the refrigerant properties, a cascade-change strategy with priority is proposed to consider both temperature drop and temperature uniformity of the battery. Besides, although the refrigerant-based thermal management structure cannot prevent the thermal abused of a single cell, it can suppress the thermal runaway propagation from one to adjacent cells.

Reliability Prediction of Battery Management System for Electric Vehicles Based on Accelerated Degradation Test: A Semi-Parametric Approach

The Li-ion battery has emerged as the most prominent energy storage device and been widely applied in electric vehicles (EVs). To ensure the safety and performance of batteries, a battery management system (BMS) is incorporated in the EVs. However, how to predict and verify the BMS reliability remains an issue. We conduct the accelerated degradation test on the BMS and develop a semi-parametric framework to predict the reliability. A new measure based on similarity is further proposed for model comparison. A real data set of the BMS is used to verify the validity of the proposed method. A comparison with the other four models shows that our approach can generate a more similar trend to the original data, with 39.23% improvement compared with the best-performed model based on the proposed measure. Besides, the proposed model gives a prediction of lower bound of the reliable lifetime as 10.8 years, which is more reasonable compared with the results (i.e., around one year) given by the conventional models.

A cost effective accumulator management system for electric vehicles

Abstract An accumulator or battery is an energy storage cramped in an adaptable stockade. Lithium-ion batteries are commonly used in hybrid electric vehicles (HEV) and battery operated electric vehicles (BOEV) due to its eco-friendliness and increased efficiency. To maintain lithium batteries in the safe operating region and also to perform tasks like cell balancing, preventing thermal runaway, maintain the state of health, an effective battery management system (BMS) is required. The BMS should also communicate effectively between host devices and battery packs. This paper proposes a reliable, modular and cost-efficient BMS, which will emanate an alert when a fault occurs and thus preventing the battery from damage. An efficient control strategy has been proposed for charging and discharging of the battery pack. The thermal analysis of the lithium-ion battery used in this work is simulated using battery design studio (BDS) with the inclusion of a self-discharging effect. The proposed hardware setup also provides a provision for on-board diagnosis (OBD) and logging in the accumulator management system (AMS) to constantly monitor the cell parameters like voltage, current, and temperature. The live data display of AMS working is also shown during abnormal and normal conditions. Also, an attempt is made to use the design of proposed AMS for HEV.

A Coordinated Electric Vehicle Management System for Grid-Support Services in Residential Networks

The increased integration of light-duty electric vehicles (EVs) into low-voltage (LV) residential networks imposes capacity issues for the grid operators. For example, the uncoordinated and clustered charging of residential EVs can often overload grid assets, jeopardize network reliability, and violate local voltage constraints. This article proposes a coordinated management system for EVs in LV residential networks with power grid support functionalities to address grid overloading and local voltage constraints violation. The charging and discharging of EV batteries in the network are coordinated via a local EV aggregator. The coordination is realized using multiagent system architecture that provides the EV owners with full decision-making authority and preserves their privacy. The EV coordination and vehicle-to-grid (V2G) resource optimization of the EV aggregator is formulated as a mixed-integer programming-based optimization model to minimize the electricity cost for the EV owners based on a real-time tariff while complying with local grid constraints. The proposed methodology is evaluated via simulation of an LV residential network in Sydney, Australia with actual load demand data. The simulation results indicate the efficacy of the proposed strategy for the electricity cost reduction of the EV owners while mitigating grid overloading and maintaining desired bus voltages.