Future Intelligent Systems Research Articles

Development of high-performance MXene/nickel cobalt phosphate nanocomposite for electrochromic energy storage system using response surface methodology

Integrating electrochromism and energy storage capabilities of materials into a supercapacitor has great potential for developing intelligent and sustainable power systems and electronics. An intriguing aspect of such materials is their ability to undergo a real-time color change, indicative of the supercapacitor's charge level. In this work, Ti3C2 MXene/nickel cobalt phosphate (MXene/NiCoP) nanocomposite was successfully prepared by electrodeposition followed by spin-coating. The MXene/NiCoP nanocomposite was optimized using response surface methodology/central composite design (RSM/CCD). The proposed quadratic model displayed a residual standard error (RSE) of <5 %, indicating good model predictability. The conformational investigations, which included FTIR, Raman, XRD, XPS, FESEM, mapping, and EDX show that MXene/NiCoP nanocomposite was successfully synthesized. The synergistic effect offered by MXene/NiCoP nanocomposite resulted in superior and remarkable electrochromic energy storage performances in terms of coloration efficiency (56 cm2/C), optical contrast retention (84.45 %), specific capacity (453 mAh/g) and cycling stability (94.12 % after 5000 cycles). The MXene/NiCoP nanocomposite has exhibited exceptional electrochromic and supercapacitive properties, making it a promising candidate for advanced electroactive materials in future intelligent hybrid energy storage systems.

Exploring Smart Standing Desks to Foster a Healthier Workplace

Sedentary behavior is endemic in modern workplaces, contributing to negative physical and mental health outcomes. Although adjustable standing desks are increasing in popularity, people still avoid standing. We developed an open-source plug-and-play system to remotely control standing desks and investigated three system modes with a three-week in-the-wild user study (N=15). Interval mode forces users to stand once per hour, causing frustration. Adaptive mode nudges users to stand every hour unless the user has stood already. Smart mode, which raises the desk during breaks, was the best rated, contributing to increased standing time with the most positive qualitative feedback. However, non-computer activities need to be accounted for in the future. Therefore, our results indicate that a smart standing desk that shifts modes at opportune times has the most potential to reduce sedentary behavior in the workplace. We contribute our open-source system and insights for future intelligent workplace well-being systems.

Intelligent Coaching Systems: Understanding One-to-many Coaching for Ill-defined Problem Solving

One-to-many coaching is a common, yet difficult, coaching technique used in environments with many novices learning to solve ill-defined problems. Intelligent systems might be designed to support 1-to-many coaching but designing such systems requires a 1-to-many coaching model that details novices' challenges, coaches' strategies, and coaches' goals. To build such a model, we conducted interaction analysis on 24 1-to-many coaching sessions with novices developing new products in a university incubator and conducted retrospective analyses with 3 coaches and 30 novices. We contribute a model that demonstrates that coaches in a 1-to-many setting not only need to help novices develop metacognitive skills (just as in 1-to-1 coaching), but also need to utilize the presence and expertise of a group of novices to learn from each other, to mitigate their fear of failures, and provide them accountability. Our model informs design implications for future intelligent coaching systems to (1) assist coaches in monitoring and comparing many novices' progress, learning, and expertise; (2) provide novices with checklists, templates, and scaffolds to help them self-evaluate, seek-help, and summarize learning; (3) showcase failures and growth; and (4) publicize planning and progress to provide accountability.

Intelligent Cockpit for Intelligent Vehicle in Metaverse: A Case Study of Empathetic Auditory Regulation of Human Emotion

Advances in technologies, such as intelligent connected vehicles and the metaverse are driving the rapid development of automotive intelligent cockpits. From the perspective of the cyber–physical–social system (CPSS), this study proposed the intelligent cockpit composition framework which includes three layers of perception, cognition and decision, and interaction. Meanwhile, we also describe the relationship between the intelligent cockpit framework and the outside environment. The framework can dynamically perceive and understand humans, and provide feedback on the understanding results, which is beneficial to provide a safe, efficient, and enjoyable experience for humans in the intelligent cockpit. In the cognition and decision layers of the proposed framework, we design a case study of active empathetic auditory regulation of driver anger, focusing on improving road traffic safety. We conducted an in-depth interview experiment and designed two auditory regulation materials of active empathy speech and text-to-speech (TTS) speech. Next, 30 participants were recruited, and they completed a total of 240 anger-regulated driving experiments in the straight and obstacle avoidance scenarios. Finally, we quantitatively analyzed and compared the participants’ subjective feelings, physiological changes, driving behaviors, and driving risks, as well as validated the driver anger regulation quality of AES and TTS. The proposed research methods results are beneficial to the design of future intelligent cockpit emotion regulation systems, toward a better intelligent cockpit.

Sign Language Gesture Recognition and Classification Based on Event Camera with Spiking Neural Networks

Sign language recognition has been utilized in human–machine interactions, improving the lives of people with speech impairments or who rely on nonverbal instructions. Thanks to its higher temporal resolution, less visual redundancy information and lower energy consumption, the use of an event camera with a new dynamic vision sensor (DVS) shows promise with regard to sign language recognition with robot perception and intelligent control. Although previous work has focused on event camera-based, simple gesture datasets, such as DVS128Gesture, event camera gesture datasets inspired by sign language are critical, which poses a great impediment to the development of event camera-based sign language recognition. An effective method to extract spatio-temporal features from event data is significantly desired. Firstly, the event-based sign language gesture datasets are proposed and the data have two sources: traditional sign language videos to event stream (DVS_Sign_v2e) and DAVIS346 (DVS_Sign). In the present dataset, data are divided into five classification, verbs, quantifiers, position, things and people, adapting to actual scenarios where robots provide instruction or assistance. Sign language classification is demonstrated in spike neuron networks with a spatio-temporal back-propagation training method, leading to the best recognition accuracy of 77%. This work paves the way for the combination of event camera-based sign language gesture recognition and robotic perception for the future intelligent systems.

Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted before.

Advanced Impacts of Nanotechnology and Intelligence

Fundamental contributions of nanotechnology include but are not limited to miniaturization, energy efficiency, higher efficiency and/or effectiveness. The exploration of new computing paradigms such as bioinspired computation and quantum computing belongs to the latter. Continuous advances in semiconductor technology include “more Moore” technology, which follows Moore's law of scaling, and “more than Moore” technology realized by hybrid integration with new materials. Much success appears in functionality and scaling in the fields of electronics, optics, sensors, and biomedical applications. In this article, we will show how one can further combine graphene, new 2D materials, and novel nanomaterials extending into the quantum realm that are at the cutting-edge of modern scientific and engineering research. This article demonstrates the impacts of nanotechnology and quantum computing including materials to devices, module demonstration, and the quantum era. In addition, a hybrid-transistor-based artificial reflex arc (ARA) and artificial pain modulation system (APMS) are discussed that illustrate future intelligent alarm systems, neuroprosthetics, and neurorobotics.

Making strides towards AI-supported regulation of learning in collaborative knowledge construction

Collaborative knowledge construction (CKC) requires individual group members to share information and resources, sustain the improvement of ideas through peer interactions, and the construction and development of collective knowledge at the group level. Investigations of this multi-level nature are of critical importance for developing AI-based regulation support systems for learners’ CKC. This paper proposes a framework using an integrated analytics approach (Hidden Markov Model combined with Lag sequential analysis and Frequent sequence mining) for analyzing multi-level characteristics of CKC at cognitive and regulative behaviour dimensions. The approach was applied to a process-oriented discourse dataset of groups working collaboratively on concept-mapping activities. The results showed that the suggested approach can reveal insights into the multi-level (i.e., regulation behaviours likely to start at the group level, move to the peer-level and occasionally to individual-level self-regulation) and the dynamic nature of CKC (i.e., CKC appears to start from a group-level regulation pattern to move to a cognitive behaviour pattern of perspective sharing at the individual and peer levels, going back to regulation at individual and peer-levels and most likely end with a cognitive behaviour pattern at the group level). The novel models presented here can be used to classify learner behaviours into CKC states, enabling personalized scaffolding opportunities based on detected patterns in future intelligent support systems.

The Impact of Adversarial Attacks on Interpretable Semantic Segmentation in Cyber–Physical Systems

The widespread adoption of deep learning (DL) models raises concerns about their trustworthiness and reliability. Adversarial attacks are cyber-related attacks that target the DL network's prediction by adding imperceptible perturbations to its input. Their deployment against critical artificial-intelligence-based systems, such as industrial cyber–physical systems (ICPSs), can result in substantial damage. Research on their scope and limitations can provide information that would help with their detection and prevention. In this article, the interconnection of adversarial attacks and interpretable semantic segmentation is investigated for potential applications in the ICPS in order to contribute to the safe use of future intelligent systems. We first explore gradient-based interpretability extensions to semantic segmentation on two industry-related cyber–physical system datasets. Then, two types of attacks on semantic segmentation networks are discussed. First, we apply the dense adversary generation attack on different segmentation outputs and evaluate its influence on the corresponding saliency maps. We then introduce a way to visualize the similarity of attacked saliency maps to the original with respect to the targeted attack's direction. Finally, we extend the application of adversarial attacks on saliency maps to semantic segmentation.

A Memetic Algorithm for Cooperative Complex Task Offloading in Heterogeneous Vehicular Networks

With the booming of intelligent connected vehicles as well as the emergence of edge computing paradigm, complex task offloading becomes a critical yet promising issue in vehicular networks to enable various real-time and scalable future intelligent systems. This article makes the first effort on proposing an end-edge-cloud cooperation architecture together with a tailored memetic algorithm for complex task offloading in heterogeneous vehicular networks. Specifically, we consider the scenario where a complex task consists of multiple subtasks, which require different amount of resources for being processed, and a task is completed only when all of its subtasks have been processed. Then, we formulate a Cooperative Complex Task Offloading (CCTO) problem by considering heterogeneous computation, communication and memory capacities of nodes in vehicular networks, as well as task dependency and mobility of vehicles, targeting at minimizing average service delay of the system. We prove that CCTO is NP-hard by constructing a polynomial time reduction from the parallel machines scheduling problem (PMSP). Further, we propose a memetic computing based algorithm named MDMA (Meme Dependency-aware Memetic Algorithm), which consists of a meme dependency based encoding solution, a mix-strategy for initialization, a dedicated offspring generation scheme, a meme recombined strategy for local search, and a task feature driven method for repairing infeasible solutions. Finally, we build a simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of MDMA on minimizing the service delay by best exploiting heterogeneous resources in vehicular networks.

Modeling of Single-Process 3D-Printed Piezoelectric Sensors with Resistive Electrodes: The Low-Pass Filtering Effect

Three-dimensional printing by material extrusion enables the production of fully functional dynamic piezoelectric sensors in a single process. Because the complete product is finished without additional processes or assembly steps, single-process manufacturing opens up new possibilities in the field of smart dynamic structures. However, due to material limitations, the 3D-printed piezoelectric sensors contain electrodes with significantly higher electrical resistance than classical piezoelectric sensors. The continuous distribution of the capacitance of the piezoelectric layer and the resistance of the electrodes results in low-pass filtering of the collected charge. Consequently, the usable frequency range of 3D-printed piezoelectric sensors is limited not only by the structural properties but also by the electrical properties. This research introduces an analytical model for determining the usable frequency range of a 3D-printed piezoelectric sensor with resistive electrodes. The model was used to determine the low-pass cutoff frequency and thus the usable frequency range of the 3D-printed piezoelectric sensor. The low-pass electrical cutoff frequency of the 3D-printed piezoelectric sensor was also experimentally investigated and good agreement was found with the analytical model. Based on this research, it is possible to design the electrical and dynamic characteristics of 3D-printed piezoelectric sensors. This research opens new possibilities for the design of future intelligent dynamic systems 3D printed in a single process.

Research on the Safety of the Left Hard Shoulder in a Multi-Lane Highway Based on Safety Performance Function

The left hard shoulder plays an important role in the event of an emergency on the inside of a multi-lane highway, but past studies have not been able to clarify the criteria for its installation or quantify the safety impact of its installation on the left side. In order to study the influence of the left hard shoulder on the safety of vehicles traveling on multi-lane highways, based on past studies that only studied the situation of four-lane highways, this paper firstly constructs a multi-lane highway simulation model under different numbers of lanes based on the VISSIM traffic simulation and uses Surrogate Safety Assessment Model (SSAM) to study the conflict characteristics of multi-lane highway vehicles under different numbers of lanes. Based on the above findings, this paper introduces the Safety Performance Function (SPF) to construct a multi-lane freeway accident prediction model, calibrates the model by adding the indexes affected by the left side hard shoulder to the basic prediction mode, and uses the historical accident data of the Badou-Shihu section of the Guangdong Northern Second Ring Highway as the basis to study the differences in accident rates of the investigated section before and after setting the left hard shoulder. The study showed that the average Time to Collision (TTC) increased by 57.2%, Maximum Deceleration (MaxD) increased by 19.2%, and Delta Speed (DeltaS) increased by 15.3% after setting hard shoulders on the left side of multi-lane freeways, and traffic conflicts on multi-lane freeways were significantly reduced, and safety was improved considerably. In addition, the rear-end conflict rate decreased by 0.17%, 0.75%, and 4.6% after setting hard shoulders on the left side of one-way three, four, and five lanes, respectively, indicating that hard shoulders on the left side are the most effective in improving the safety of one-way five-lane freeways. The accident prediction results show that within the reasonable setting range of the left hard shoulder width (0~4 m), the accident rate decreases by about 1.5% for every 0.5 m increase if only the influence of the left hard shoulder width is considered. Without considering other factors, increasing the width of the hard shoulder on the left side can reduce the number of accidents. This indicates a significant safety improvement for a one-way five-lane highway after setting the hard shoulder on the left side, and the conclusion is consistent with the simulation results. In this paper, based on past research, the research object is extended to one-way three-, four-, and five-lane highways. The findings of this paper can help the road authorities develop specifications for installing hard shoulders on the left side of multi-lane freeways and adopt strategies to improve the traffic safety level of multi-lane freeways. In addition, the models and methods used in this paper can also help build a framework for future intelligent networked vehicle avoidance systems and promote the development of intelligent networked technologies.

Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effectiveness and civility in online communication. It is, however, not an easy task in online discussion settings where written words are the primary communication channel. In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts. To satisfy these design goals, we analyzed and built a labeled dataset of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and evidence) in 164 arguments with high ratings on persuasiveness from ChangeMyView, a popular online discussion forum. We then designed an interactive visual system, Persua, which provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments. In particular, the system constructs portfolios of arguments based on different persuasive strategies applied to a given discussion topic. It then presents concrete examples based on the difference between the portfolios of user input and high-quality arguments in the dataset. A between-subjects study shows suggestive evidence that Persua encourages users to submit more times for feedback and helps users improve more on the persuasiveness of their arguments than a baseline system. Finally, a set of design considerations was summarized to guide future intelligent systems that improve the persuasiveness in text.

Hospitals of the Future: Designing Interactive Robotic Systems for Resilient Emergency Departments

The Emergency Department (ED) is a stressful, safety-critical environment, which is often overcrowded, noisy, chaotic, and understaffed. The built environment plays a key role in patient outcomes, experiences, and the mental health of healthcare workers (HCWs). However, once a space is built, it is difficult to change it; so the modularity and adaptability of new technologies such as robots could potentially help stakeholders mitigate some of these challenges; yet, there is a lack of research in this area, particularly in the ED. In this paper, we address this gap by engaging HCWs in a research-through-design process, utilizing design fiction, to envision a future resilient ED. Here, robots scurry along the ceiling, provide help at the bedside, and smart furniture and walls provide spaces for privacy and calm. We co-created design prototypes of future intelligent systems that can modify the built environment to support resilience, which we then used to co-create a Design Catalog with HCWs, which contains a collection of future technology prototypes contextualized within the ED. We found that HCWs envisioned many ways for intelligent systems to help them reimagine the built environment, including ways to enhance HCW-patient communication, improve patient experience, support both HCW and patient safety, and use reconfigurable spaces to support privacy. We hope our work inspires further exploration into using new technologies to reimagine and reconfigure the built environment to support resilient hospitals.

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.

Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.

Digital Twin System of Bridges Group Based on Machine Vision Fusion Monitoring of Bridge Traffic Load

Bridges play an important role in transportation infrastructure systems. Intelligent and digital management of bridges group is an essential part of the future intelligent transportation infrastructure system. This paper proposes a digital twin system for bridges group in the regional transportation infrastructure network, which is interconnected by measured traffic loads. In physical space, a full-bridge traffic load monitoring system based on information fusion of weigh-in-motion (WIM) and multi-source heterogeneous machine vision is set up on the target bridge to measure traffic loads, also lightweight sensors are employed on the bridges group for structural response information. Furthermore, by establishing mechanical analysis models in the corresponding digital space and using the measured traffic loads as links, the working condition perception and safety warning of all bridges in the regional transportation network is achieved, forming an important support for further intelligent transportation infrastructure system. The proposed digital twin system has been preliminarily implemented in a bridges group around Shanghai, China, demonstrating the feasibility of the technical framework proposed in this paper and the bright prospects.

Integrated visible light communication and positioning CDMA system employing modified ZCZ and Walsh code.

In this paper, an integrated visible light communication and positioning (VLCP) code division multiple access (CDMA) system is proposed for recovering original user data and obtaining positioning information simultaneously. A generalized modification method for balanced bipolar code set is presented, which can be applied to VLCP-CDMA system with intensity modulation. Both modified bipolar code sets, zero correlation zone (ZCZ) code set and Walsh code set, are employed and evaluated in VLCP-CDMA systems. When considering synchronous and quasi-synchronous (QS) systems, it is demonstrated that the modified ZCZ code set with ideal zero correlation zone properties performs better communication and positioning performance than the modified Walsh code set by system simulations and experiments. As a result, a bit error rate (BER) of 1.8×10-3 and an average positioning error of 1.50 cm are successfully obtained by adopting modified ZCZ code set in a 4-user real-time VLCP-CDMA system. It will offer a promising solution to meet both communication and positioning requirements of future intelligent systems.

Energy efficient MIMO–NOMA aided IoT network in B5G communications

To accelerate future intelligent wireless systems , we designed an energy-efficient Massive multiple-input-multiple-output (MIMO)- non-orthogonal multiple access (NOMA) aided internet of things (IoT) network in this paper to support the massive number of distributed users and IoT devices with seamless data transfer and maintain connectivity between them. Massive MIMO has been identified as a suitable technology to implement the energy efficient IoT network in beyond 5G (B5G) communications due to its distinct characteristics with large number of antennas. However, to provide fast data transfer and maintain hyper connectivity between the IoT devices in B5G communications will bring the challenge of energy deficiency. Hence, we considered a massive MIMO–NOMA aided IoT network considering imperfect channel state information and practical power consumption at the transmitter. The far users of the base stations are selected to investigate the power consumption and quality of service. Then, calculate the power consumption which is non-convex function and non-deterministic polynomial problem. To solve the above problem, fractional programming properties are applied which converted polynomial problem into the difference of convex function . And then we employed the successive convex approximation technique to represent the non-convex to convex function. Effective iterative based branch and the reduced bound process are utilized to solve the problem. Numerical results observe that our implemented approach surpasses previous standard algorithms on the basis of convergence, energy-efficiency and user fairness.

Secure and Efficient Blockchain-Based Knowledge Sharing for Intelligent Connected Vehicles

The emergence of Intelligent Connected Vehicles (ICVs) shows great potential for future intelligent traffic systems, enhancing both traffic safety and road efficiency. However, the ICVs relying on data driven perception and driving models face many challenges, such as the lack of comprehensive knowledge to deal with complicated driving context. In this paper, we investigate cooperative knowledge sharing for ICVs. We propose a secure and efficient blockchain based knowledge sharing framework, wherein a distributed learning based scheme is utilized to enhance the efficiency of knowledge sharing and a directed acyclic graph (DAG) system is designed to guarantee the security of shared learning models. To cater for the time-intense demand of highly dynamic vehicular networks, a lightweight DAG is designed to reduce the operation latency in terms of fast consensus and authentication. Moreover, to further enhance model accuracy as well as minimizing bandwidth consumption, an adaptive asynchronous distributed learning (ADL) based scheme is proposed for model uploading and downloading. Experiment results show that the DAG based framework is lightweight and secure, which reduces both chosen and confirmation delay as well as resisting malicious attacks. In addition, the proposed adaptive ADL scheme enhances driving safety related performance compared to several existing algorithms.

SOH prediction of lithium battery based on IC curve feature and BP neural network

Precise battery SOH (state of health) prediction and monitoring are of extreme importance for the future intelligent battery management system (BMS). In this paper, battery discharge experiments at different temperatures were carried out. A battery SOH prediction model based on incremental capacity analysis and BP neural network is proposed to predict battery SOH at different ambient temperatures. By analyzing the correlation between the characteristics of IC curve and SOH, the mapping relationship between temperature and IC curve characteristics is established by using the least square method, and the SOH prediction model at different temperatures is obtained. At the same time, combined with ICA, an online real-time correction prediction model is established, and the characteristic data is continuously updated to ensure the SOH prediction accuracy under different aging states. Finally, the feasibility of the prediction method proposed in this paper is verified by comparing the model test results and experimental results, the average error of the model prediction results is 1.16%. Thus, by establishing the relationship between temperature and IC curve characteristics, the battery SOH at different temperatures can be predicted.