Intelligence in Mobile Battery Applications Desk Research Report (original) (raw)
Related papers
The Rise of AI driven BMS Revolutionizing Li ion Battery Performance
IEEE, 2024
—Accurate measurement of the number of cycles of Li-Ion batteries is essential for the battery management system (BMS). Virtual State of Health (SOH) estimation is increasingly attracting attention because to the simplicity of its structure, its flexibility in adapting to online usage, and its independence on battery designs, in contrast to direct methods of measurement and model-based methods. At first, data-driven methods are used to derive a reliable health indicator (HI) that may be used to measure the status of health (SOH). Afterwards, a machine learning model is created to establish the relationship between the Health Index (HI) and the State of Health (SOH). This study proposes employing a partial charge and discharge current sequence based on data analytics of battery aging data. Next, the data is inputted into the KNN algorithm, known for its rapid learning speed and strong generalization capabilities. The estimation accuracy is fully tested by considering the selected charging and discharging capacity. The efficacy of the suggested strategy is confirmed by testing on an openly available dataset. This study offers an analysis of the methodologies used to estimate the state of health (SOH) of batteries, highlighting their primary benefits and identifying their limits in terms of real-time compatibility with automotive systems, particularly in hybrid electric applications.
ARTIFICIAL INTELLIGENCE-BASED BATTERY MANAGEMENT SYSTEMS FOR LI-ION BATTERIES
Trans Stellar Journals, 2021
Li-ion batteries are highly advanced as compared to any other commercially available rechargeable batteries due to their gravimetric and volumetric energy. With the rise of the electric vehicle market, battery management systems play a key role in the industry to deliver finer performance and support longer ranges. Battery packs need to be monitored in realtime to maintain the safety, efficiency and reliability of the overall system. The crucial parameters that need to be determined for efficient and safe working include State-Of-Charge. This is achieved by monitoring the current, voltage, temperature, etc. which are then processed using various algorithms. The SoC of the battery pack is determined using ANN/AI-ML based control system based on the dataset collected/simulated for the individual Li-ion cells. To ensure voltage/SoC remains the same for the multiple cells in the battery pack, a single inductor method of cell balancing is used. This paper focuses on the hardware aspects as well as the software aspects of the battery management system with a short analysis of basic requirements and topologies. Implementation and development aspects are also elaborated
Computer, 2000
This article introduces IntellBatt; a novel design of a multi-cell battery. IntellBatt exploits the cell characteristics to enhance battery lifetime, to ensure safe operation and to deliver better performance. Experiments were performed using Li ion cells and a portable DVD player. Simulation of the obtained current trace with IntellBatt have shown an enhancement of battery lifetime by 22%.
Artificial Intelligence Applied to Battery Research: Hype or Reality?
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteriesa current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.
IntellBatt: Towards smarter battery design
2008
Battery lifetime and safety are primary concerns in the design of battery operated systems. Lifetime management is typically supervised by the system via battery-aware task scheduling, while safety is managed on the battery side via features deployed into smart batteries. This research proposes IntellBatt, an intelligent battery cell array based novel design of a multi-cell battery that offloads battery lifetime management onto the battery. By deploying a battery cell array management unit, IntellBatt exploits various battery related characteristics such as charge recovery effect, to enhance battery lifetime and ensure safe operation. This is achieved by using real-time cell status information to selects cells to deliver the required load current, without the involvement of a complex task scheduler on the host system. The proposed design was evaluated via simulation using accurate cell models and real experimental traces from a portable DVD player. The use of a multi-cell design enhanced battery lifetime by 22% in terms of battery discharge time. Besides a standalone deployment, IntellBatt can also be combined with existing battery-aware task scheduling approaches to further enhance battery lifetime.
IntellBatt: Toward a Smarter Battery
Computer, 2000
This article introduces IntellBatt; a novel design of a multi-cell battery. IntellBatt exploits the cell characteristics to enhance battery lifetime, to ensure safe operation and to deliver better performance. Experiments were performed using Li ion cells and a portable DVD player. Simulation of the obtained current trace with IntellBatt have shown an enhancement of battery lifetime by 22%.
Energies
This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contri...
Using Machine Learning to Optimize Resource Use in Batteries and Engines: A Review
2023
Machine learning is the current hot topic in the technology industry with many seeing what potential uses it has across many different fields. One such potential application is in a specific subset of smart mobility, resource optimization. This paper analyzes the use of machine learning techniques to optimize the performance and design of batteries and engines. By analyzing other works a generalized overview of the topic is achieved alongside suggestions for future research.
Batteries
Powertrain electrification is bound to pave the way for the decarbonization process and pollutant emission reduction of the automotive sector, and strong attention should hence be devoted to the electrical energy storage system. Within such a framework, the lithium-ion battery plays a key role in the energy scenario, and the reduction of lifetime due to the cell degradation during its usage is bound to be a topical challenge. The aim of this work is to estimate the state of health (SOH) of lithium-ion battery cells with satisfactory accuracy and low computational cost. This would allow the battery management system (BMS) to guarantee optimal operation and extended cell lifetime. Artificial intelligence (AI) algorithms proved to be a promising data-driven modelling technique for the cell SOH prediction due to their great suitability and low computational demand. An accurate on-board SOH estimation is achieved through the identification of an optimal SOC window within the cell chargin...
Smart batteries for automotive applications
Integrated Computer-Aided Engineering
The US Army has been pursuing the development of a Smart Battery development as part of its ongoing efforts for reducing operation and maintenance costs of the Army's ground vehicles. As a consequence of this effort, it became evident that the smart battery has a much broader significance well beyond that of an operations and maintenance cost reduction exercise. In effect, the traditional concept of a battery as a simple energy storage unit yielded to that of a critical component in a power management subsystem. A smart battery is a standard battery having an embedded microcontroller that monitors key sensor inputs, processes and stores the derived information and reports the results on a shared data bus. In that arrangement, the smart battery serves the function of a status-reporting load-leveling component of the overall subsystem. As such, the smart battery becomes an essential contributor to a digital nervous system in vehicle systems. The smart battery is creating a new way of defining what a battery is. It is evident that the use of smart batteries has a direct bearing on vehicle readiness and enables system-wide planning, organization and control of the power and energy resources.