Smart Battery Management System for Electric Vehicles

In the early stages of developing new standards, there are obstacles in formulating standard technical requirements due to a lack of information about stakeholder needs from different perspectives that may lead to formulated standards not being accepted by consensus. So it is important to know all the needs of stakeholders and seek a mutual agreement for each stakeholder. This study uses FACTS (framework for the analysis, comparison, and testing standards) in the development framework of standardization and testing requirements Battery Management System (BMS) for electric vehicles. There are three stages in the method of FACTS: analysis, comparison, and testing. FACTS approach is used to identify the needs of stakeholders on the technical requirements and provide a framework to define the testing required in the BMS. This approach provides additional information about formalism and structure, so the standards development can be applied to all stakeholders. FACTS approach is only used for the standardization process, especially in the drafting. The results of this study are the framework that can be used in the preparation of BMS standards so that BMS standards in Indonesia could be developed.

The typical structure of energy management system for new electric vehicle was analyzed to summarize the function. The research trend was pointed out based on the analysis of performance characteristics of energy management system for electric vehicle.

Performance analysis of liquid-based battery thermal management system for Electric Vehicles during discharge under drive cycles

The Battery is the most basic part of the Electric Vehicle, which serves as a major source of energy and gives it sustainable mobility. In electric vehicles, the technology that is highly recognized and used for energy storage is based on lithium chemistry. Nevertheless, the room for research is still open. This involves the collection of materials for the development of cells. The development of algorithms and the design of electronic circuits for a better and more efficient use of batteries is also one area of study. It is important to keep an eye on the critical operating parameters of the battery during charging for the optimum output of the batteries. A battery management system (BMS) is one of those mechanisms for monitoring internal and ambient battery temperature, current, voltage, and charging and discharge operations. Within this paper we speak about some of the popular battery control methods and framework. We also speak about the state-of-the-art device criteria for optimum battery efficiency and its general architecture.

A Thermal Management System for Electric Vehicles (EV) based on the Peltier-Effect Heat Pumps was designed, fabricated, installed into an EV and its performance was tested. A mathematical model was derived from this system and was simulated using a SPICE program. The results show the applicability of the Peltier-effect heat pumps for the temperature regulation of an EV.

Electric vehicles (EVs) are the fastest-growing type of transport. Battery packs are a key component in EVs. Modern lithium-ion battery cells are characterized by low self-discharge current, high power density, and durability. At the same time, the battery management system (BMS) plays a pivotal role in ensuring high efficiency and durability of battery cells and packs. The BMS monitors and controls the battery charge and discharge to ensure EV safety and optimum operation. This paper is devoted to analyzing BMS circuitry configurations and algorithms. The analysis includes circuit solutions and algorithms for implementing the main BMS functions, such as parameter monitoring, protection, cell balancing, state estimation, charging and discharging management, communication, and data logging. The paper provides insights into the recent research literature on BMS, and the advantages and disadvantages of methods for implementing BMS functions are compared. The paper also discusses the application of artificial intelligence technologies and aspects of further work on next-generation BMS technologies.

The wireless Battery Management System (BMS), one of the emerging technologies, offers advantages over the conventional wired BMS by enabling the reduction of battery pack weight and size, ease of maintenance, and improving communication speed limitations. Also, in addition to the communication reliability of the wired BMS, the wireless BMS can enhance the reliability during the battery pack design/manufacturing, as well as the reliability of fault response in the event of battery pack malfunctions and failures. Consequently, active research on the wireless BMS is being conducted in the mobility sector, for electric vehicles (EVs) and railway vehicles and such, where BMS performance enhancement is critical. However, most previous studies in the mobility field have focused primarily on methodologies. Also, actual battery pack configuration and the operational environment of the wireless BMS is not sufficiently considered in previous studies. This paper studies the design of a wireless BMS that incorporates Bluetooth communication technology and targets the EV, which is a representative application in the mobility sector. This research comprehensively analyzes the design requirements and considerations for designing and implementing an efficient wireless BMS environment for EVs. Specifically, this study focuses on: analysis and selection of wireless communication methods; optimization of battery module case design for wireless BMS; and analysis of wireless communication constraints along with strategies to mitigate them. Based on the results of our study, we propose an optimized theoretical design method. Subsequently, the proposed design method is validated through various experiments conducted under simulated EV operating conditions.

<div class="section abstract"><div class="htmlview paragraph">The electric vehicle thermal management system is a critical sub-systems of electric vehicles, and has a substantial impact on the driving range. The objective of this paper is to optimize the performance of the heat pump air conditioning system, battery, and motor thermal management system by adopting an integrated design. This approach is expected to effectively improve the COP (Coefficient of Performance) of cabin heating. An integrated thermal management system model of the heat pump air conditioning system, battery, and motor thermal management system is established using AMEsim. Key parameters, such as refrigerant temperature, pressure, and flow rate at the outlet of each component of the system are compared with the measured data to verify the correctness of the model established in this paper. Using the established model, the impact of compressor speed on the heating comfort of the cabin under high-temperature conditions in summer was studied, and a control strategy for rapid passenger compartment cooling is proposed. Additionally, a hybrid cooling strategy was established to address the priority issues of cabin and battery cooling, and compared with traditional cooling strategies in terms of cooling time and accuracy. The results demonstrate that the hybrid cooling strategy is capable of simultaneously cooling the cabin and battery if ambient temperature is 40°C. Compared with traditional methods that prioritize cooling either the cabin or the battery, the hybrid cooling strategy enables the rapid cooling of the battery while maintaining the cabin temperature comfort, and significantly reduces the discomfort time of passengers in the cabin by 64.25%.</div></div>

Lithium-ion rechargeable cells have the highest energy density and are the standard choice for battery packs for many consumer products, from laptops to electric vehicles. While they perform superbly, they can be rather unforgiving if operated outside a generally tight safe operating area (SOA), with outcomes ranging from compromising the battery performance to outright dangerous consequences. In order to solve this problem, Battery Management System (BMS), a technology specially used to supervise battery packs, is used for the management of battery packs. The oversight that a BMS provides usually includes: Monitoring the battery, Providing battery protection, Estimating the battery’s operational state, Continually optimizing battery performance, Reporting operational status to external devices. The BMS certainly has a challenging job description, and its overall complexity and oversight outreach may span many disciplines such as electrical, digital, control, thermal, and hydraulic. This chapter classifies the topology of BMS and investigates the hardware and software architecture of BMS. The main functions of BMS are introduced.KeywordsBattery Management SystemElectric vehicleMonitoring the batteryEstimating the battery’s operational state

The overconsumption of fossil fuels has drastically increased the emissions of toxic gases causing harm to the environment. Batteries are one of the most important components of Electric Vehicles. When dealing with bigger capacities and high power requirements, high power supplying battery packs are required, which consist of several batteries. These high capacity battery packs are prone to reach high temperatures during charging and discharging thus, giving rise to a lot of complications. Hence, the use of a battery management system is essential. It is responsible to optimize the battery pack which results in better and safer working of the battery pack. The main objective of this paper is to simulate a MATLAB/Simulink model of a Battery Management System (BMS) and analyze the different types of estimation methods of the main parameters of a battery management system. It also provides an idea on the most efficient and cost-friendly methods to implement in a BMS. The main parameters include the balancing of the battery pack, state of charge (SoC) estimation methods, state of health (SoH) estimation, charging and discharging control and temperature monitoring, which help improve the performance of the battery.

This is a preliminary research in energy optimization system (ENOS) for Electric Vehicle (EV) whereby eco-driving is one of the solutions. Eco-driving initiative is not only benefit to the environment by optimizing the energy consumption but also enhancing the propulsion performance of Electric Vehicle. The efforts to reduce energy consumption could be achieved directly by studying contributing factors from the propulsion characteristic and the behavior of the vehicle dynamics (longitudinal vehicle dynamics in particular). We derived a single expression to describe energy consumption which could be programmed as the eco-driving algorithm. The expression is started by developing sets of equation of motions of longitudinal vehicle dynamics and relates them with the propulsion behavior that includes the counter-force by the traction torque and battery management system for Electric Vehicle. This mathematical model is established to analyze the energy consumption which shows the parameters that are important in the strategy to acquire the optimal performance and efficiency. This whole effort is part of the strategy to develop algorithm for eco-driving program.

The Electric vehicles (EVs) are progressively supplanting gasoline vehicles in the automotive industry as a result of their environmental and economic advantages. Jordan’s investment in the electric vehicle (EV) market is projected to grow by 35% by 2025. In addition, the Jordan National Vision 2025 initiative provides backing for environmentally friendly energy projects, specifically those related to electric vehicles (EVs) and the necessary infrastructure, including charging stations. This paper presents the proposal, design, and implementation of an affordable intelligent EV charging management system based on the Internet of Things (IoT). This system streamlines the charging process while simultaneously ensuring robust security measures and enhancing customer convenience. Given that the entire university campus is reliant on solar energy, the management system at Al-Zaytoonah University of Jordan (ZUJ) is being introduced to regulate the charging process of electric vehicles (EVs) belonging to ZUJ staff, students, visitors, and the local community

The Electric Vehicle (EV) revolution is rapidly reshaping the automotive industry offering alternative to combustion engine vehicles. A cornerstone of this transformation is the lithium-ion battery, which powers these EVs. The possibility of battery failures leading to explosions and substantial losses is a critical concern that demands innovative solution. The primary objective of the Battery Health Management and Fault Predictive System (BHMFPS) is to enhance the safety and reliability of electric vehicles by implementing advanced monitoring and predictive maintenance measures for EV batteries. The disadvantages of existing methods include limited predictive capabilities and mostly focused on basic maintenance needs. The methodology involves developing machine learning models for health and fault prediction. The dataset contains various parameters, such as temperature, voltage, current, state of charge, and other relevant metrics.

Chapter 7.4 - State of charge prediction of battery management system for electric vehicles

This article presents a novel cloud-based charging management system for electric vehicles (EVs). Two levels of cloud computing, i.e., local and remote clouds, are employed to meet the different latency requirements of the heterogeneous EVs while exploiting the lower-cost computing in remote clouds. Specifically, we consider time-sensitive EVs at highway exit charging stations and EVs with relaxed timing constraints at parking lot charging stations. We propose algorithms for the interplay among EVs, charging stations, system operator, and clouds. Considering the contention-based random access for EVs to a 4G Long-Term Evolution network, and the quality of service metrics (average waiting time and blocking probability), the model is composed of: queuing-based cloud server planning, capacity planning in charging stations, delay analysis, and profit maximization. We propose and analyze a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">price-incentive method</i> that shifts heavy load from peak to off-peak hours, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">capacity expansion method</i> that accommodates the peak demand by purchasing additional electricity, and a hybrid method of price incentives and capacity expansion that balances the immediate charging needs of customers with the alleviation of the peak power grid load through price-incentive based demand control. Numerical results demonstrate the effectiveness of the proposed methods and elucidate the tradeoffs between the methods.