Machine Learning-Enabled Battery Management System on FPGA for Electric Vehicles

Recently, electric vehicle (EV) technology has received massive attention worldwide due to its improved performance efficiency and significant contributions to addressing carbon emission problems. In line with that, EVs could play a vital role in achieving sustainable development goals (SDGs). However, EVs face some challenges such as battery health degradation, battery management complexities, power electronics integration, and appropriate charging strategies. Therefore, further investigation is essential to select appropriate battery storage and management system, technologies, algorithms, controllers, and optimization schemes. Although numerous studies have been carried out on EV technology, the state-of-the-art technology, progress, limitations, and their impacts on achieving SDGs have not yet been examined. Hence, this review paper comprehensively and critically describes the various technological advancements of EVs, focusing on key aspects such as storage technology, battery management system, power electronics technology, charging strategies, methods, algorithms, and optimizations. Moreover, numerous open issues, challenges, and concerns are discussed to identify the existing research gaps. Furthermore, this paper develops the relationship between EVs benefits and SDGs concerning social, economic, and environmental impacts. The analysis reveals that EVs have a substantial influence on various goals of sustainable development, such as affordable and clean energy, sustainable cities and communities, industry, economic growth, and climate actions. Lastly, this review delivers fruitful and effective suggestions for future enhancement of EV technology that would be beneficial to the EV engineers and industrialists to develop efficient battery storage, charging approaches, converters, controllers, and optimizations toward targeting SDGs.

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.

The battery is merely an energy storage and the key for all-electric vehicles is understanding how to use the battery in the most optimal way in order to secure vehicle performance over a long period of time. The operating and controlling strategies of a battery rely on the understanding of the fundamental cell constraints, which are turned into battery and vehicle control strategies, and implemented as algorithms in the battery management system (BMS): the control unit of the battery. The BMS will control and monitor the performance and status of the battery and communicate the operational constraints currently available to the control system of the vehicle. There are many cross-dependent parameters to be understood and to be incorporated in a robust and reliable control system. Input data for the BMS are the state functions, e.g. state of charge and state of health, battery temperature, and usage history, required to secure optimal performance in a durable and safe manner. How this control and communication is handled depends on the battery and vehicle manufacturers, and is not covered in this book. Instead, the underlying fundamentals will be discussed in terms of electrochemical and material constraints. In the following sections, battery control and management will be described: charge control and methods, thermal and safety management, as well as the state functions, i.e. state of charge (SOC), state of health (SOH), and state of function (SOF). Battery management system The battery management system (BMS) utilises a number of parameters that are linked to each other and most of the key parameters are path dependent, and the usage and environmental history affects future operational possibilities. Each of these parameters affects the battery control and management system: temperature, voltage range, current, and energy throughput. Temperature is one of the most important parameters for the BMS and the corresponding control strategies. The battery should be used within a specific temperature range, a range defined by the chemistry inside the cell. At temperatures outside this predefined range, higher as well as lower, side reactions may take place, side reactions limiting battery life and possibly causing abuse situations.

As the bridge of battery and vehicle management system and the drivers, battery management system (BMS) for electric vehicle performance is playing a more and more key role. This article introduces several kinds of battery display methods and displays, and for each display method on the feasibility study, also focuses on the electric car batteries systematic, modular design and the chip integration technology of battery management system.

Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion engines, driven by growing environmental concerns and advancements in renewable energy. Despite their benefits, challenges persist in the adoption and efficient operation of EVs, largely due to limitations in battery technology and management systems. This paper focuses on the development and improvement of Battery Management Systems (BMS) to enhance operational performance and energy efficiency. Through system analysis, literature review, data-driven modeling, and design of an Automotive Plant Management System (APMS), this study proposes actionable recommendations to support EV adoption and manufacturing efficiency, with particular reference to Innoson Vehicle Manufacturing Ltd.

Li-ion batteries (LIBs) have become the preferred choice in electric vehicles (EVs) for reducing CO2 emissions, enhancing energy efficiency, and enabling rechargeability. They are extensively used in mobile electronics, EVs, grid storage, and other applications due to their high power, low self-discharge rate, wide operating temperature range, lack of memory effect, and environmental friendliness. However, commercial LIBs face safety and energy density challenges, primarily due to volatile and flammable liquid electrolytes and moderate energy densities. To address these issues, advanced materials are being explored for improved performance in battery components such as the anode, cathode, and electrolyte. All-solid-state batteries (ASSEBs) emerge as a promising alternative to liquid electrolyte LIBs, offering higher energy density, better stability, and enhanced safety. Despite challenges like lower ionic transport, ongoing research is advancing ASSEBs’ commercial viability. This paper critically reviews the state of the art in ASSEBs, including electrolyte compositions, production techniques, battery management systems (BMSs), thermal management systems, and environmental performance. It also assesses ASSEB applications in EVs, consumer electronics, aerospace, defense, and renewable energy storage, highlighting the potential for a more sustainable and efficient energy future.

The battery management system (BMS) is the heart of an electric vehicle. It is a fundamental device connected between the charger and the battery of the electric or hybrid systems. The BMS has several vital functions to perform such as safety, protection, battery management including estimation of charge, cell balancing for effective and smooth operation of the battery and vehicle. This paper aims at designing and implementation of a prototype for 3 level BMS in an EV. The significance of the proposed work is to use the charge of the battery pack in the most efficient and effective way. The software tools used are MATLAB/Simulink, proteus and Arduino IDE. The designed prototype is able to switch off the non-essential appliances including air conditioner, radio, etc., with reduction in speed range. Thus, battery management is successfully carried out. The driver also gets an alert regarding current state of battery, so that he may plan his journey accordingly.KeywordsBattery management system (BMS)Coulomb countingKalman filteringState of charge (SOC)Electric vehicle (EV)Cell balancing

In terms of electric vehicle architectures, the drivetrain offers unprecedented freedom, but it also creates new obstacles in terms of achieving all needs. The architecture of electric vehicles is simplified and adjustable at the component level because they don't have a combustion engine or fuel tank, only an electric motor and a battery. Implementing safe zones within electric vehicles (EVs) to accommodate battery packs necessitates significant adjustments to ensure the secure integration of the battery. A Battery EV, also known as a pure EV, solely relies on rechargeable battery packs as its source of energy, without any additional propulsion system. The Battery Management System (BMS) plays a significant role in maintaining the safety of electric vehicles by controlling the electronics of rechargeable batteries, whether they are individual cells or battery packs. The BMS plays crucial role in protecting both the user and the battery by monitoring and maintaining the cell's operation within safe limits. This research paper focuses on the control of solar‐powered charging for lithium‐ion batteries. An optimized FOPID controller is utilized to maximize power extraction from PV array and efficiently charge the battery. A hybrid optimization model is employed to optimize the gain parameters of the FOPID controller.

Edge computing for vehicle battery management: Cloud-based online state estimation

Battery-driven vehicle has prevailed recently. In the public transportation sector, prototype of battery-driven tram has been developed in Korea, Japan, and Europe. It is envisaged that battery-driven public transit will be adopted in many cities to make the city green and to suppress the emission of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . Battery Life is restricted mainly by over-charge, discharge, and the number of rapid charge. Over-charge and discharge are prevented by the battery management system (BMS) in vehicle. Because rapid charge is preferred to reduce charge time, it makes dominant effect to battery life in real operation environment. In this paper, we propose battery monitoring and management system (BMMS) for battery-driven public transit. It enables the battery to be effectively managed by reducing the frequency of rapid charge, and the transit operation center to continuously monitor the state of charge of all the vehicles with the aid of information & communication technology.

Electric vehicles are expected to occupy a significant portion of the automotive market share in the near future. An electric vehicle has only an electric drive-train powered by a battery, and an electric vehicle should provide a dozens of mile driving distance with single recharge of the battery. The cost and performance of the electric vehicle is primarily determined by the battery and its management system.

Battery and energy management system for vanadium redox flow battery: A critical review and recommendations

The Electric Vehicles (EV) Battery Management System (BMS) is a crucial part of EVs because it does important tasks like controlling charging and discharging, state detection, fault diagnosis and warning, data recording and analysis, etc. Nonetheless, new battery types are continually developing due to the quick advances in electrochemistry and materials related to batteries. An essential component for any system is the battery, which requires perfect maintenance to ensure optimal performance. It is now crucial to maintain the health of batteries in today's technologically advanced culture. In a fuel-cell/battery hybrid system, fuel cells, lithium-ion batteries, and related dc/dc converters are all included. Utilising energy management systems allows power sources (such as fuel cells and lithium-ion batteries) to be allocated according to demand. The state-machine approach is suggested due to the EV's limited compute capability and its simplicity in engineering implementation. Battery lifespan as well as efficiency can be significantly increased with the use of EB Power, AC/DC, and DC/DC converters for battery management. Furthermore, precise battery condition tracking and management are made possible by the combination of parts like PT/CT sensors, ADC converters, and PIC controllers. The use of an LCD screen and LM35 sensor improves accessibility for users, and the integration of IoT technology enables remote control and data collection and processing. Battery control is now an effective instrument for extending battery life and enhancing overall device performance due to its cutting-edge capabilities.

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions.

The selection of energy storage system is very crucial for electric vehicles. It should have good energy density, considerable power density and also it must be light weight. So a battery with considerably high energy density must be used in electric vehicles. Lithium ion batteries are very much preferred as electric vehicle batteries. They have high energy density, high life cycle and smooth operation. But the problem related with lithium batteries is they have high temperature sensitivity, and their operation will be affected by over current charging and over current discharging beyond their maximum rated values and also influenced by driving conditions and performance of motors used. So battery management, control and optimisation system is essential in electric vehicle energy storage battery packs. This paper is a review of the design of a novel battery management and control system for lithium ion batteries for performance improvement in electric vehicles.