The end-of-life power battery recycling & remanufacturing center location-adjustment problem considering battery capacity and quantity uncertainty.

The booming electric vehicle market has led to an increasing number of end-of-life power batteries. In order to reduce environmental pollution and promote the realization of circular economy, how to fully and effectively recycle the end-of-life power batteries has become an urgent challenge to be solved today. The recycling & remanufacturing center is an extremely important and key facility in the recycling process of used batteries, which ensures that the recycled batteries can be handled in a standardized manner under the conditions of professional facilities. In reality, different adjustment options for existing recycling & remanufacturing centers have a huge impact on the planning of new sites. This paper proposes a mixed-integer linear programming model for the siting problem of battery recycling & remanufacturing centers considering site location-adjustment. The model allows for demolition, renewal, and new construction options in planning for recycling & remanufacturing centers. By adjusting existing sites, this paper provides an efficient allocation of resources under the condition of meeting the demand for recycling of used batteries. Next, under the new model proposed in this paper, the uncertainty of the quantity and capacity of recycled used batteries is considered. By establishing different capacity conditions of batteries under multiple scenarios, a robust model was developed to determine the number and location of recycling & remanufacturing centers, which promotes sustainable development, reduces environmental pollution and effectively copes with the risk of the future quantity of used batteries exceeding expectations. In the final results of the case analysis, our proposed model considering the existing sites adjustment reduces the cost by 3.14% compared to the traditional model, and the average site utilization rate is 15.38% higher than the traditional model. The results show that the model has an effective effect in reducing costs, allocating resources, and improving efficiency, which could provide important support for decision-making in the recycling of used power batteries.

Consumer preference and willingness-to-pay for formal recycling of electric vehicle batteries: A discrete choice experiment in China.

The burgeoning electric vehicle (EV) market poses a substantial challenge to battery recycling systems, yet understanding EV battery recycling behavior from the demand side remains limited. Previous studies have analyzed perceptual or attitudinal factors, neglecting the observable attributes of EV battery recycling. To this end, we proposed a discrete choice model to investigate the differences between formal and informal recycling behaviors, identifying consumer preferences and willingness to pay. By analyzing 1190 sample data collected from Chongqing, China, we find that: (1) The formal recycling market exhibits greater sensitivity to prices compared to the informal recycling market. (2) The formal recycling market favors recycling by EV battery producers, whereas the informal recycling market shows the least preference for recycling by automobile producers. (3) Door-to-door recycling services are the most effective in facilitating the transition from informal to formal recycling markets for EV batteries. (4) Capacity subsidy policies outperform one-time fixed subsidy policies in incentivizing formal recycling. (5) The formal recycling market for EV batteries necessitates "traceability to the recycling outlet", as opposed to being untraceable. (6) The high-awareness group exhibits greater sensitivity to government policies compared to those with lower environmental concerns and less knowledge of EV battery recycling.

Circular economy strategies for mitigating metals shortages in electric vehicle batteries under China's carbon-neutral target.

Concerns over supply risks of critical metals used in electric vehicle (EV) batteries are frequently underscored as impediments to the widespread development of EVs. With the progress to achieve carbon neutrality by 2060 for China, projecting the critical metals demand for EV batteries and formulating strategies, especially circular economy strategies, to mitigate the risks of demand-supply imbalance in response to potential obstacles are necessary. However, the development scale of EVs in the transport sector to achieve China's carbon neutrality is unclear, and it remains uncertain to what extent circular economy strategies might contribute to the reduction of primary raw materials extraction. Consequently, we explore the future quantity of EVs in China required to achieve carbon neutrality and quantify the primary supply security levels of critical metals with the effort of battery cascade utilization, technology substitutions, recycling efficiency improvement, and novel business models, by integrating dynamic material flow analysis and national energy technology model. This study reveals that although 18%-30% of lithium and 20%-41% of cobalt, nickel, and manganese can be supplied to EVs through the reuse and recycling of end-of-life batteries, sustainable circular economy strategies alone are insufficient to obviate critical metals shortages for China's EV development. However, the supplementary capacity offered by second-life EV batteries, which refers to the use of batteries after they have reached the end of their first intended life, may prove adequate for China's prospective novel energy storage applications. The cumulative primary demand for lithium, cobalt, and nickel from 2021 to 2060 would reach 5-7 times, 23-114 times, and 4-19 times the corresponding mineral reserves in China. Substantial reduction of metals supply risks apart from lithium can be achieved by the cobalt-free battery technology developments combined with efficient recycling systems, where secondary supply can satisfy the demand as early as 2054.

Collaborative reverse logistics network for electric vehicle batteries management from sustainable perspective.

With the proposal of carbon neutralization, countries pay much more attention to environmental protection. As waste electric vehicle battery (WEVB) has an important impact on the environment, its reverse logistics process has been a vital issue, in which an excellent reverse logistics network (RLN) becomes a prerequisite for waste recycling, cost reduction, profit increasement and efficiency improvement. However, reverse logistics network design (RLND) for WEVBs is still a significant challenge. In general, the amount and quality of the returned product in a RLN are highly uncertain, and the WEVB market in China is no exception to this. From a circular economic and environmental perspective, this paper is devoted to designing a multi-participants-based RLN for WEVBs, considering recycling and remanufacturing processes. Then, a fuzzy optimization model is proposed to determine the number and location of facilities in this RLN, promote sustainable development, reduce environmental pollution, and consider carbon emissions and two types of WEVBs. Finally, the results from a case study in Chongqing show good performance in cost reduction, profit increasement and efficiency improvement, which could significantly support the decision-making of WEVBs recycling.

Deep learning-based state of charge estimation for electric vehicle batteries: Overcoming technological bottlenecks.

This study presents a novel deep learning-based approach for the State of Charge (SOC) estimation of electric vehicle (EV) batteries, addressing critical challenges in battery management and enhancing EV efficiency. Unlike conventional methods, our research leverages a diverse dataset encompassing environmental factors (e.g., temperature, altitude), vehicle parameters (e.g., speed, throttle), and battery attributes (e.g., voltage, current, temperature) to train a sophisticated deep learning model. The key novelty of our approach lies in its integration of real-world driving data from a BMW i3 EV, enabling the model to capture the intricate dynamics affecting SOC with remarkable accuracy. We conducted 72 tests using actual driving trip data, which included 25 types of environmental variables, to validate the feasibility and effectiveness of our proposed model. The deep learning network, designed specifically for SOC estimation, outperformed traditional models by demonstrating superior accuracy and reliability in predicting SOC values. Our findings indicate a significant advancement in SOC estimation techniques, offering actionable insights for both policymakers and industry practitioners aimed at fostering energy conservation, carbon reduction, and the development of more efficient EVs. The study's major contribution is its demonstrated capability to improve SOC estimation accuracy by understanding the complex interrelationships among various influencing factors, thereby addressing a pivotal challenge in EV battery management. By employing cutting-edge deep learning techniques, this research not only marks a significant leap forward from traditional SOC estimation methods but also contributes to the broader goals of sustainable transportation and environmental protection.

Hardware-in-the-loop testing of simple and intelligent MPPT control algorithm for an electric vehicle charging power by photovoltaic system.

In this paper, an electric vehicle (EV) charging station powered by a photovoltaic (PV) system has been created to charge EVs. Furthermore, the maximum available power from the PV system was extracted using a new algorithm called the simplified universal intelligent PID (SUIPID) controller, which has the advantages of simplicity in design and intelligence. The SUIPID controller was compared to the artificial neural networks (ANNs), the fuzzy logic controller (FLC) based on linear membership functions, and the FLC based on particle swarm optimization (PSO) to optimize its parameters under various conditions. The system simulation was first performed using MATLAB software, then an experimental set up was conducted to confirm the theoretical work. The proposed SUIPID responds 28.5%, 44.4%, and 61.5% faster than the PSO-FLC, ANN, and FLC, respectively, whereas the ITAE error was reduced by 27.3%, 52.9%, and 65.2%, respectively. Also, the charging procedure of the EV battery is precisely steady under different atmospheric conditions. The SUIPID controller has several advantages over other intelligent algorithms, most notably its ease of design and implementation.

FLC-based, PV-fed interleaved dual buck-boost converter for EV battery charging applications.

Fuzzy logic controller is developed for PV array fed interleaved dual buck-boost (IDBB) converter for EV battery charging applications. The inner dynamics of the IDBB converter is improved using the damping network. Irrespective of the change in solar insolation conditions, the developed fuzzy logic controller adjusts the duty ratio of the converter to obtain constant output voltage. Simulation studies have been carried out using MATLAB/Simulink software and Fuzzy Logic Controller modelled in MATLAB/Simulink software was interfaced with dSPACE1103 to carry out the experimental investigation in Hardware-in-loop methodology in dSPACE workstation. The experimental results are on-par with the simulated results validating the proposed system.

The adoption of internet of things in a circular supply chain framework for the recovery of WEEE: the case of lithium-ion electric vehicle battery packs.

The rapid growth in the sales of electric and electronic devices over recent decades is generating worldwide concern about the management of Waste Electrical and Electronic Equipment (WEEE). New methodologies to extend the useful life of products have long been sought, accelerating the shift from a linear to a Circular Economy (CE). When products reach the End-of-Life (EoL) stage, the Reverse Supply Chain (RSC) is responsible for managing operations, with greater efforts being needed to improve the associated information infrastructure. In fact, this has become increasingly feasible due to the emergence of a new digital revolution led by the Internet of Things (IoT). To shed light on this matter, we propose the Circular Supply Chain (CSC) framework for EoL management aimed at satisfying the information infrastructure requirements in a particular scenario for the recovery of Electric Vehicle Battery (EVB) packs. We present a qualitative evaluation of the CSC information requirements, and the capabilities of IoT to satisfy them. As a result, a heterogeneous IoT network deployment is proposed in pursuit of a digital CSC information infrastructure.