Lifetime Degradation Research Articles

Home energy management system for residential fuel cell-based combined heat and power system

The goal of this paper is to propose an optimization scheme for enhancing power dispatch and load scheduling for residential fuel cell-based combined heat and power systems (FC-CHPS) using mixed integer linear programming (MILP), considering the lifetime degradation of both the fuel cell system (FCS) and battery energy storage systems (BESS). The scheme is applied in the home energy management system that oversees the electric and thermal power of residential FC-CHPS. First, the nonlinearity in the relationship between the natural gas consumption and output electric power, and between the residual thermal power and output electric power of the fuel cell system (FCS) in the FC-CHPS is approximated using piecewise linearization. Then, the piecewise linearization is integrated with MILP to derive optimal power dispatch and load scheduling solutions, while accounting for the nonlinearity of the FCS. Next, cost functions representing the lifetime degradation of FCS and BESS are formulated. These cost functions result in nonlinear optimization constraints. Therefore, innovative methods are proposed to linearize these constraints, allowing the continued use of MILP as the optimization method and factoring in the lifetime degradation of FCS and BESS. Compared to the mixed-integer non-linear programming (MINLP) method that does not linearize the non-linearity in the FCS or the lifetime degradation of the FCS and BESS, the proposed MILP scheme achieves the identical objective function value. Furthermore, the computation time for the proposed MILP scheme is 99.94 % lower than that required by the MINLP method. The proposed scheme provides accurate results in short computation times.

Multi-objective adaptive energy management strategy for fuel cell hybrid electric vehicles considering fuel cell health state

This study proposes a multi-objective adaptive energy management strategy for fuel cell hybrid electric vehicles considering fuel cell health state. By integrating rule-based control with multi-objective optimization methods, the strategy aims to improve system efficiency, extend the lifespan of proton exchange membrane fuel cells (PEMFC), and reduce operating costs. Based on a comprehensive model of PEMFC output characteristics and lifetime degradation, this study introduces an optimized point line (OPL) strategy. This strategy dynamically adjusts operating constraints according to the state of health (SOH) of the PEMFC, ensuring optimal vehicle performance throughout its lifecycle. To optimize the OPL strategy parameters, a particle swarm optimization algorithm with compression factor was employed, enhancing the strategy’s optimization efficiency, adaptability, and robustness to better handle various real-world operating conditions. The strategy was evaluated under US06 and WLTC driving cycles and compared with traditional power following (PF) and point line (PL) strategies. Results show that compared to the PL strategy, the OPL strategy achieved a 36.4% and 34.2% reduction in operating costs under US06 and WLTC cycles, respectively. Moreover, PEMFC lifetime degradation decreased by 44.9% and 39.4% in these cycles. In high-power regions, the average operating efficiency of PEMFC improved by 2%. The strategy demonstrated good adaptability to different driving conditions, providing an effective solution for optimizing the performance and durability of fuel cell hybrid electric vehicles.

Device Modeling of 4H-SiC PIN Photodiodes with Shallow Implanted Al Emitters for VUV Sensor Applications

A numerical model is presented for the simulation of ultraviolet ion-implanted 4H-SiC photodiodes with shallow p- emitter doping profiles. An existing model for SiC pin photodiodes, taken from literature, is modified with a dedicated SiO2-SiC interface layer to account for degradation of carrier mobility and lifetime at the interface. Furthermore, aluminum compensation in 4H-SiC is included and its impact on the spectral response and carrier recombination is analyzed. The simulated spectral response in the wavelength range from 200 to 400 nm is compared to experimental data. While the existing model, taken from literature, fails to predict the performance of VUV photodiodes with a shallow p- emitter, the newly designed model successfully achieves high accuracy, even with a basic modeling approach featuring an abrupt material parameter transition.

Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles

The power transients caused by switching from drive mode to brake mode in fuel cell hybrid electric vehicles (FCHEV) can result in significant degradation cost losses to the fuel cell. To address this issue, this study proposes a self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy. First, a real-time self-learning Markov predictor (SLMP) based on the traditional offline training Markov improvement is designed to predict the demand power and combined with the sequential quadratic programming (SQP) optimization algorithm to solve for the inner optimal demand power based on its global cost function minimization characteristic. On this basis, the fuel cell gradient drop power (FGDP) strategy is proposed to optimize the operating state of the vehicle powertrain under vehicle mode switching. This involves establishing a power gradient drop step based on considering the fuel cell hydrogen consumption cost and its lifetime degradation cost to further obtain the outer fuel cell demand power at the optimal step. And three execution modes are designed to trigger the FGDP strategy. Finally, by combining the above efforts, the SLMP-FGDP optimization control strategy is constructed. The numerical verification and hardware in loop experiments results show that the proposed improved SLMP can predict the vehicle demand power more accurately. Compared with the non-FGDP system, the SLMP-FGDP strategy can effectively near-eliminate the fuel cell power transient due to any braking scenario, thus effectively controlling the fuel cell lifetime degradation cost in a lower range and realizing a reduction of up to 52.21% of the fuel cell usage costs without significantly sacrificing the hydrogen fuel economy.

Benefit to Harm (B2H) Ratio for Bulk Vehicle to Grid (V2G) Services Using Lifetime Degradation Digital Twin: Emulating Various Dominant Degradation Mechanism

Vehicle-to-grid (V2G) services, utilizing electric vehicle (EV) batteries for grid support, enhance reliability and reduce peak energy demand. We present a physics-based model tuned for three different cell chemistry families and examine the influence of bulk V2G services on EV battery life. We consider various duty cycles, cell chemistries with nickel content (NMCxyz and various anode graphites), and associated dominant degradation mechanisms. To quantify the impact of offering V2G services, we introduce the V2G benefit-to-harm ratio (B2H). We define the benefit as the Ah gained with V2G and the harm as the life lost in days due to V2G, both compared to the baseline noV2G.B2H=(normalized Ah gained by 70% capacity fade) divided by (time in days reduction by the time that capacity fade has reached 70%). Note that the 70% capacity fade can be changed for various vehicle models depending on the warranty definition of the % capacity or usable battery energy (UBE) at the end of the warranty.Our findings indicate that the dominant degradation mechanism plays a crucial role in the benefit-to-harm (B2H) ratio of V2G or vehicle-to-building (V2B) [1]. Specifically, we clarify that the relative contribution of calendar aging to overall capacity loss is pivotal in determining the degradation due to V2G and possible associated benefits.Our model takes into account four degradation mechanisms based on a single-particle model. SEI growth and cathode transition metal dissolution are part of the calendar aging loss of lithium inventory (LLICal). The remaining LLI is attributed to Li-plating and mechanical degradation caused by particle cracking.We calibrate the model for three families of cell chemistries and conditions. The primary degradation mechanism for one cell family is anode mechanical degradation (LAMNeg). The second family of cells predominantly ages due to SEI layer growth, and the third family is degrading due to both mechanisms [2].We show that, in cases with a higher contribution of calendar aging to degradation (LLICal/LLI), V2G can be potentially more beneficial (in Ah gained by 70% capacity fade) than harmful (lifetime reduction in days by the time that capacity fade has reached 70%). Furthermore, we have evidence that the V2G charging pattern can have a secondary effect on the B2H ratio. For example, being a risk taker and charging the battery late or being cautious and charging it as soon as possible can lead to different degrees of degradation depending on the chemistry and conditions of the battery [3].With our multiphysics reduced order model of battery lifetime degradation, we fully explain why previous studies [4] have shown that the impact on EV battery degradation varies from inconsequential [5] to projecting a need for early battery replacement [6]. Our results clarify that battery chemistry and usage patterns are important factors in determining whether or not to utilize a vehicle for grid support, as well as the overall financial impact on the owner and OEM warranty.

Battery Lifetime Prediction and Degradation Analysis with Machine Learning Method

Due to environmental pollution and climate crisis caused by fossil fuels, eco-friendly alternative energy sources are gaining attention. Particularly, electric vehicles (EVs) are being recognized as the next-generation means of transportation. The most crucial technologies in electric vehicles are lithium-ion batteries and Battery Management Systems (BMS). However, the extended lifespan of lithium-ion batteries leads to prolonged assessment periods, making it challenging to measure and improve performance over time. Additionally, the operation of batteries under various conditions makes it difficult to measure state of charge (SOC) and state of health (SOH) in real-time, introducing many variables. With recent advancements in predictive technology through machine learning, accurate predictions can be made without complex physical knowledge, leveraging high-quality data.In this study, machine learning techniques were applied using charge/discharge data from lithium-ion LiNi0.5Mn0.3Co0.2O2 (NMC532)/graphite pouch cell batteries. The values measured during the initial charge/discharge cycles were converted into variables that could assess battery degradation modes. Machine learning algorithms were then applied, followed by model tuning to reduce overfitting and enhance accuracy. This research contributes to real-time battery management system data using machine learning technology to predict the remaining useful life (RUL) of lithium-ion batteries with high accuracy, using only initial cycles. This approach enhances the efficiency of BMS data in real-world scenarios and contributes to the overall time efficiency of battery research. Acknowledgement This research was supported by National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT(NRF-2021K1A4A8A01079455).

Kinetics of Carrier Lifetime Degradation in High‐Temperature 1 MeV Electron‐Irradiated Cz n‐Si Associated with the Formation of Divacancy‐Oxygen Defects

Kinetics of degradation of the nonequilibrium charge carrier lifetime (τ) in Czochralski‐grown (Cz) n‐Si irradiated with 1 MeV electrons at different temperatures in the range from 20 to 285 °C are experimentally and theoretically investigated. It is established that changes in τ are qualitatively and quantitatively determined by the temperature of electron irradiation. Analysis of experimental data using the Shockley‐Read‐Hall (SRH) theory shows that the formation of recombinationally active divacancy‐oxygen (V2O) and vacancy‐oxygen (VO) complexes is the main mechanism of τ degradation in this experiment.

P‐195: Analysis of Impurities in OLED Materials

We conducted combustion ion chromatography, TOFSIMS, and LC/MS measurements to identify impurities in OLED materials affecting device lifetime decreases. The results of various analyses indicated that the synthesis lot with a low device lifetime contained a significant amount of brominated Host‐1 (Host‐1‐Br), and we successfully identified the compound responsible for the degradation of the device lifetime.

Cluster-based wireless sensor network framework for denial-of-service attack detection based on variable selection ensemble machine learning algorithms

A Cluster-Based Wireless Sensor Network (CBWSN) is a system designed to remotely control and monitor specific events or phenomena in areas such as smart grids, intelligent healthcare, circular economies in smart cities, and underwater surveillance. The wide range of applications of technology in almost every field of human activity exposes it to various security threats from cybercriminals. One of the pressing concerns that requires immediate attention is the risk of security breaches, such as intrusions in wireless sensor network traffic. Poor detection of denial-of-service (DoS) attacks, such as Grayhole, Blackhole, Flooding, and Scheduling attacks, can deplete the energy of sensor nodes. This can cause certain sensor nodes to fail, leading to a degradation in network coverage or lifetime. The detection of such attacks has resulted in significant computational complexity in the related works. As new threats arise, security attacks get more sophisticated, focusing on the target system's vulnerabilities. This paper proposed the development of Cluster-Based Wireless Sensor Network and Variable Selection Ensemble Machine Learning Algorithms (CBWSN_VSEMLA) as a security threats detection system framework for DoS attack detection. The CBWSN model is designed using a Fuzzy C-Means (FCM) clustering technique, whereas VSEMLA is a detection system comprised of Principal Component Analysis (PCA) for feature selection and various ensemble machine learning algorithms (Bagging, LogitBoost, and RandomForest) for the detection of grayhole attacks, blackhole attacks, flooding attacks, and scheduling attacks. The experimental results of the model performance and complexity comparison for DoS attack evaluation using the WSN-DS dataset show that the PCA_RandomForest IDS model outperforms with 99.999 % accuracy, followed by the PCA_Bagging IDS model with 99.78 % accuracy and the PCA_LogitBoost model with 98.88 % accuracy. However, the PCA_RandomForest model has a high computational complexity, taking 231.64 s to train, followed by the PCA_LogitBoost model, which takes 57.44 s to train, and the PCA_Bagging model, which takes 0.91 s to train to be the best in terms of model computational complexity. Thus, the models surpassed all baseline models in terms of model detection accuracy on flooding, scheduling, grayhole, and blackhole attacks.

A new shared energy storage business model for data center clusters considering energy storage degradation

In recent years, the energy consumption of data centers (DCs) has shown a sharp upward trend. Given the high investment cost of energy storage, this study introduces the concept of energy sharing within a data center cluster (DCC) and proposes a novel shared energy storage (SES) business model. The model realizes the co-optimization for DCC and SES and is divided into three main layers. Firstly, considering the SES's economy and the DCC's cleanliness, the upper layer establishes a novel multi-objective optimization configuration scheme for SES with lifetime degradation. In addition, a two-stage robust optimization technique for DCC is proposed in the middle layer, considering the demand response mechanism for data loads. Subsequently, the concept of contribution degree (CD) is defined, and a novel benefit distribution mechanism based on cooperative game and CD is presented in the lower layer. The simulation results demonstrate that the proposed business model positively enhances the economic benefits of the SES and DCC. The constructed uncertainty optimization model exhibits strong robustness, and the proposed benefit allocation scheme is fairer and more reasonable.

Tracking Hydrogen During Poly-Si/SiOx Contact Fabrication: An Infrared Spectroscopy Analysis of Si–H Bonds Configurations

The hydrogenation step contributing to the high efficiencies (>25%) reached with poly-Si/SiOx passivated contacts solar cells is still poorly understood. In this study, Fourier transform infrared spectroscopy (FTIR) is used to follow the different bonding configurations of H during the fabrication process. The carrier lifetime degradation upon annealing is correlated to an important loss of Si–H bonds, from both the a­‑Si:H film and the SiOx interfaces. The subsequent hydrogenation step results in the formation of a small number of Si–H bonds near the crystalline silicon c-Si/SiOx interface, associated with the low stretching mode (LSM) and correlated to a significant lifetime improvement. These bonds feature a preferential orientation, as shown by polarized measurements.

Influence of AlOx Interlayers on LeTID Kinetics in Ga-Doped Cz-Si

Light and elevated temperature-induced degradation (LeTID) is causing a reduction in efficiency especially in p-type silicon based solar cells. It is assumed to be strongly influenced by the hydrogen content in the bulk material. The presented work focuses on the impact of differently thick (5-25 nm) atomic layer-deposited aluminum oxide (AlOx) interlayers underneath the hydrogen-rich silicon nitride (SiNy:H) capping layer. The interlayer acts as a diffusion barrier for H during the firing step. It is demonstrated that the AlOx interlayer has a comparable effect on the LeTID kinetics in Ga-doped Cz-Si (Cz-Si:Ga) as it is observed in B-doped Cz-Si (Cz-Si:B). Additionally, it substantially minimizes lifetime degradation in the Cz-Si:Ga sample. With a determined ratio of electron to hole capture cross sections k=26(3), the degradation phenomena are attributed to the LeTID kinetics. Deposition of AlOx barrier layers exceeding 10 nm in thickness does not yield additional positive effects. Resistivity measurements revealed that the change in hole concentration correlates with the defect density for varying AlOx layer thicknesses. The doping concentration seems to influence the change in maximum defect density for varying AlOx layer thicknesses.

(Invited) Designing Sustainable Battery Technologies

Sustainability in battery technologies needs to be considered from the outset, however sustainability means many things, and needs to be considered holistically from materials, manufacturing, life-time and recyclability. Three different aspects of sustainable considerations within lithium-ion and sodium-ion technology development are discussed; materials developments, cell lifetime and optimisations, and design for recycling.The cell is comprised of a nickel based layered oxide material cathode with hard carbon anode. To maximise energy density and life-time degradation of the bulk and surface of the O3-type oxides require stabilisation.1 We discuss the optimisation of a facile method for manufacturing a stabilised O3- type layered oxide via simultaneous doping and surface coating. A higher average voltage is obtained, which stabilised the high voltage phase transition to higher voltages, and results in longer-cycle life.2 To understand the sodium-ion cell in more detail, we have performed a detailed electrochemical analysis of a hard-carbon and layered oxide cathode and extracted parameters for multi-physics models. The kinetics and thermodynamics have been studied at different temperatures, and the limitations of the cell elucidated.Finally design for remanufacture is also discussed, utilising water-based binder systems we show differences in delamination and material recovery. Utilising different recycling schemes through shredding and physical processing through to disassembly processes improvements in recovery rates are required. Particularly for low value materials, where the techno-economics of the processes do not currently work. Direct recycling of materials reclaimed from the batteries, wherever possible allows value within the material design to be maintained, rather than relying directly upon elemental value. Aspects of direct loop recycling and short loop recycling are discussed with respect to current lithium-ion technologies, and the ability to directly translate this to sodium-ion.3 In conclusion three aspects for sustainability considerations are discussed with respect to the materials life-cycle; active material optimisation, longevity and life-time when in use and electrode developments for recycling.41 T. Song and E. Kendrick, J. Phys. Mater., 2021, 4, 32004.2 T. Song, L. Chen, D. Gastol, B. Dong, J. F. Marco, F. Berry, P. Slater, D. Reed and E. Kendrick, Chem. Mater., 2022, 34, 4153–4165.3 G. D. J. Harper, E. Kendrick, et al. J. Phys. Energy, 2023, 5, 021501.4 Picture created by DALL-E 2 algorithm with Nightcafe Figure 1

Critical Data Backup with Hybrid Flash-Based Consumer Devices

Hybrid flash-based storage constructed with high-density and low-cost flash memory has become increasingly popular in consumer devices in the last decade due to its low cost. However, its poor reliability is one of the major concerns. To protect critical data for guaranteeing user experience, some methods are proposed to improve the reliability of consumer devices with non-hybrid flash storage. However, with the widespread use of hybrid storage, these methods will result in severe problems, including significant performance and endurance degradation. This is caused by the fact that the different characteristics of flash memory in hybrid storage are not considered, e.g., performance, endurance, and access granularity. To address these problems, a critical data backup (CDB) design is proposed to ensure critical data reliability at a low cost. The basic idea is to accumulate two copies of critical data in the fast memory first to make full use of its performance and endurance. Then, one copy will be migrated to the slow memory in the stripe to avoid the write amplification caused by different access granularity between them. By respecting the different characteristics of flash memory in hybrid storage, CDB can achieve encouraging performance and endurance improvement compared with the state-of-the-art. Furthermore, to avoid performance and lifetime degradation caused by the backup data occupying too much space of fast memory, CDB Pro is designed. Two advanced schemes are integrated. One is making use of the pseudo-single-level-cell (pSLC) technique to make a part of slow memory become high-performance. By supplying some high-performance space, data will be fully updated before being evicted to slow memory. More invalid data are generated which reduces eviction costs. Another is to categorize data into three types according to their different life cycles. By putting the same type of data in a block, the eviction efficiency is improved. Therefore, both can improve device performance and lifetime based on CDB. Experiments are conducted to prove the efficiency of CDB and CDB Pro. Experimental results show that compared with the state-of-the-arts, CDB can ensure critical data reliability with lower device performance and lifetime loss whereas CDB Pro can diminish the loss further.

Duty ratio drive prediction model of lifetime degradation for organic light emitting diode-on-silicon microdisplay.

A duty ratio drive prediction (DRDP) model of luminance degradation for organic light emitting diodes (OLED) microdisplay is proposed in this paper. The traditional stretched exponential decay (SED) model is not applicable for OLED driven by duty ratio. The DRDP model introduces the duty ratio as the variables affecting the lifetime of OLED. By fitting the undetermined coefficients with the measured luminance data, the quantitative relationships among the initial luminance, duty ratio, and OLED lifetime are obtained. Meanwhile, the model quantifies the phenomenon of spontaneous luminance recovery, which occurs when OLED switches from bright to dark. Finally, the DRDP model is used to compensate the luminance degradation of OLED driven by duty ratio. The experimental results show that the average prediction accuracy of DRDP model for white, red, green, and blue (W/R/G/B) OLED degradation trend is 0.9623. The average prediction accuracy of W/R/G/B OLED lifetime is 0.6119, which is greater than that of SED model. The lifetime is extended by 89.83% after compensation.

Activation of Al2O3 surface passivation of silicon: Separating bulk and surface effects

Understanding surface passivation arising from aluminium oxide (Al2O3) films is of significant relevance for silicon-based solar cells and devices that require negligible surface recombination. This study aims to understand the competing bulk and surface lifetime effects which occur during the activation of atomic layer deposited Al2O3. We demonstrate that maximum passivation is achieved on n- and p-type silicon with activation at ∼ 450 °C, irrespective of annealing ambient. Upon stripping the Al2O3 films and re-passivating the surface using a superacid-based technique, we find the bulk lifetime of float-zone and Czochralski silicon wafers degrade at annealing temperatures > 450 °C. By accounting for this bulk lifetime degradation, we demonstrate that the chemical passivation component associated with Al2O3 remains stable at activation temperatures of 450─500 °C, achieving an SRV of < 1 cm/s on n- and p-type silicon. In conjunction with the thermal stability, we show that films in the range of 3–30 nm maintain an SRV of < 1 cm/s when annealed at 450 °C. From atomic-level energy dispersive X-ray analysis, we demonstrate that, post deposition, the interface has a structure of Si/SiO2/Al2O3. After activation at > 300 °C, the interface becomes Si/SixAlyO2/Al2O3 due to diffusion of aluminium into the thin silicon oxide layer.

Data-driven rapid lifetime prediction method for lithium-ion batteries under diverse fast charging protocols

Accurate lithium-ion battery lifetime prediction is essential for equipment maintenance and safety assurance in practical applications. The influence of different charge protocols on the lifetime varies greatly, which raises tremendous challenges for rapid lifetime prediction. In this paper, a comprehensive data-driven rapid lifetime prediction method for batteries under diverse fast charging protocols is proposed. It straightforwardly establishes the relationship between the charging conditions and the impacts on battery lifetime. A deep neural network model with the Bayesian optimization algorithm for hyperparameter search (BOA-DNN) and three traditional shallow machine learning algorithms consisting of support vector machine (SVM), Gaussian process regression (GPR), and extreme gradient boosting (XGBoost) are employed. The verification is performed on an authoritative and popular dataset with 69 types of distinct fast charging conditions. The eventual results indicate that the proposed approach has attained reliable performance of battery lifetime degradation trajectory and battery end-of-life (EOL) prediction, in which the BOA-DNN model outperforms SVM, GPR, and XGBoost. Especially in battery EOL prediction, the prediction mean absolute percentage error of the BOA-DNN model is below 3.58%.

Optimization of a GaAs/AlGaAs p-i-n heterojunction nanowire solar cell for improved optical and electrical properties

GaAs/AlGaAs based nanowires are promising candidates for photovoltaic applications due to their high absorption coefficient, low surface reflection, and efficient collection of photogenerated carriers. This study focuses on optimizing the performance of p-i-n GaAs/AlGaAs nanowire solar cell arrays having a radial junction using optoelectronic simulations. The research investigates the optimal doping for the GaAs core and AlGaAs shell, as well as the impact of shell thickness and junction positions on solar cell performance. Additionally, the study examines the effect of various surface effects, including the presence of surface traps, surface recombination velocities, and associated lifetime degradation. Our studies find that a high doping density for the shell and core region is crucial for achieving an appropriate band configuration and carrier extraction. It also highlights that having a larger doping density is more important than having a larger lifetime. Finally, the research examines the effect of different aluminum compositions on photogeneration inside the nanowire and shows that having a high aluminum composition can confine most photogeneration to inner GaAs regions, potentially allowing for thicker AlGaAs shells, which can efficiently prevent surface recombination.

Plasma Induced Charging Damage Causing MOS Device Reliability Lifetime Degradation Originating From Well Charging of a Technology With Deep Trench Isolation

Plasma Induced Charging Damage Causing MOS Device Reliability Lifetime Degradation Originating From Well Charging of a Technology With Deep Trench Isolation

A microwave-driven deep-UV ZnI2 light source for degrading organic wastewater: comparison with mercury lamp

The traditional cathode low-pressure Hg lamp is the most mainstream UV light source in degrading organic wastewater but with significant limitations of short lifetime, environmental hazard and poor degradation efficiency. Here, we evaluate a self-designed microwave-driven ZnI2 deep-UV lamp on degrading Rhodamine B (RhB), methylene blue (MB), and p-nitrophenol (PNP). An electrodeless Hg lamp was used as control. First we investigated the emission wavelength, spatial radiation distribution, the effect of power on radiation intensity, and relative permittivity of ZnI2 lamp. Then the degradation rate, kinetics and energy yield on degrading RhB, MB and PNP as well as the cost were studied. ZnI2 lamp showed shorter wavelengths, approaching UVC radiation intensity, approaching attenuation factor and higher microwave coupling capability to Hg lamp. After treatment with ZnI2 lamp for 10, 6 and 15 min, 5 mg/L of RhB, MB and PNP can reach 100% removal rates. Moreover, higher degradation rate and kinetic constants of ZnI2 lamp for 3 compounds than the Hg lamp in this paper and in the literature were achieved. With the assistance of microwave and properties of environmental-friendly and low cost, ZnI2 lamp provides a new alternative to traditional Hg lamp and wastewater photochemical treatment method.