Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels. 

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

A Survey of Digital Twin Techniques in Smart Manufacturing and Management of Energy Applications

The use of technologies such as Internet of Things (IoT), data processing and blockchain have allowed companies to serve their customers with better quality, efficiency, reliability and in the shortest possible time. The growing adoption of electric vehicles on the market has increased the demand for batteries that may have numerous manufacturers. Life expectancy is affected on manufacture, but also on operational conditions. A large number of parameters have a role on battery's health and thousands of data need to be evaluated and combined. The present work investigates the scenario of the battery industry in order to implement a blockchain-based platform for the supply chain implementation thus allowing a better control on performance of batteries and environmental impact. To achieve this goal, the authors carried out a systematic review with the following steps: identification of relevant studies, evaluation and summary of similar studies, comparison and extraction of data from the papers. The main motivation of this work is the use of the literature for justifying the use of the blockchain technology to track batteries and for identifying the main challenges in the related markets that can be addressed by this technology. The results of this systematic review show that the development of a blockchain-based platform for battery tracking will allow for greater transparency across the entire supply chain: production, reuse, recycling, disposal. Trasparency and traceability prevent clandestine markets, misuse and release of pollutants. Adressing these topics forsters the successful implemention of electric vehicles in the market. 

Blockchain review for battery supply chain monitoring and battery trading

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared. Code and preprocessed data for generating all the results and figures presented in the battery digital twin case study in part 2 of this review are available on Github . 

A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies

In developing an efficient battery management system (BMS), an accurate and computationally efficient battery states estimation algorithm is always required. In this work, the highly accurate and computationally efficient model‐based state of X (SOX) estimation method is proposed to concurrently estimate the different battery states such as state of charge (SOC), state of energy (SOE), state of power (SOP), and state of health (SOH). First, the SOC and SOE estimation is performed using a new joint SOC and SOE estimation method, developed using a multi‐time scale dual extended Kalman filter (DEKF). Then, the SOP estimation using T‐method and 2RC battery model is performed to evaluate the non‐instantaneous peak power during charge/discharge. Finally, the battery current capacity estimation is performed using a simple coulomb counting method (CCM)‐based capacity estimation with a sliding window. The performance of the proposed SOX estimation method is compared and analyzed. The experimental results show that the estimated SOC and SOE error is less than 1% under considered dynamic load profile at three different temperatures. After the final convergence, the estimated capacity maximum value absolute error is ±0.08 Ah. In addition, the low value of evaluated mean execution time (MET) justifies the high computational efficiency of the proposed method.

Xu Kang

Papers

Digital twin and cloud-side-end collaboration for intelligent battery management system