Evolutionary framework design in formulation of decision support models for production emissions and net profit of firm: Implications on environmental concerns of supply chains

Garg, Akhil; Gao, Liang; Li, Wei; Singh, Surinder; Peng, Xiongbin; Cui, Xin; Fan, Zhun; Singh, Harpreet; Chin, Christina

doi:10.1016/j.jclepro.2019.05.300

Cited by 4 publications

(9 citation statements)

References 31 publications

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“…The models in the initial generation are built as per the given data. Reproduction, subtree hybridisation (crossover) and transformation (mutation) are the main genetic operators . Objective functions are utilised for the comprehensive assessment (fitness degree and structural complexity) and ranking of models in a generation.…”

Section: System Identificationmentioning

confidence: 99%

“…Reproduction, subtree hybridisation (crossover) and transformation (mutation) are the main genetic operators. 17 Objective functions are utilised for the comprehensive assessment (fitness degree and structural complexity) and ranking of models in a generation. Models with top rankings will exist in the next generation, while the remaining have to experience genetic operations to generate new models.…”

Section: Theory Of Gpmentioning

confidence: 99%

“…In other words, an assumption of model structure is necessary for the application of RSM, which implies RSM is not suitable for identification of a system with unknown behaviour and complexity. Moreover, RSM may not work for the data beyond the training range . For multi‐physical identification of unknown systems, artificial intelligence (AI) methods are highly popular.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, RSM may not work for the data beyond the training range. 17 For multi-physical identification of unknown systems, artificial intelligence (AI) methods are highly popular. Haifeng et al 19 concluded that a combination of stress measurement and mathematical models is an effective way for SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

“…For the generation of decision‐support models, response surface methodology (RSM) is an efficient method which can achieve solutions easily without complicated computational burden . In other words, an assumption of model structure is necessary for the application of RSM, which implies RSM is not suitable for identification of a system with unknown behaviour and complexity.…”

Section: Introductionmentioning

confidence: 99%

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Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Garg

Gao

et al. 2019

Int J Energy Res

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Summary In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters‐based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies.

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Section: System Identificationmentioning

confidence: 99%

Section: Theory Of Gpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Garg

Gao

et al. 2019

Int J Energy Res

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A novel procedure combining computational fluid dynamics and evolutionary approach to minimize parasitic power loss in air cooling of Li‐ion battery for thermal management system design

Jishnu

Garg

et al. 2020

Energy Storage

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Air‐cooling‐based battery thermal management system (BTMS) is a research hotspot for electric vehicles because of lower cost and simpler design. Past research works have immensely concentrated on the enhancement of heat removal from Li‐ion battery, but the minimum consideration has been given to minimize the parasitic power. In this paper, a novel procedure is proposed to predict the operating parameters (inlet velocity, working time of fan, and range of heat generation rates) of air‐cooling design for minimizing parasitic power and ensuring the battery temperature do not exceed the upper threshold limit simultaneously. Based on findings via computational fluid dynamics analysis, an empirical model of air‐cooling BTMS is further developed using an evolutionary approach of model selection criteria approximated genetic programming (MSC‐GP). The model is then optimized to determine operating parameters of air cooling which causes minimum parasitic power while keeping the average temperature of battery cells within the limit. Further, sensitivity analysis and parameter interaction (2D and 3D) analysis is also performed to study the effect and identify the contribution of various operating parameters on parasitic power. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only. Finally, the optimal values of operating parameters for various heat generation rate per cell (function of discharge rate) are obtained. The optimized parasitic power was observed to be nonlinearly increasing with heat generation rate. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However, parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only.

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Multi-objective design optimization of battery thermal management system for electric vehicles

et al. 2021

Applied Thermal Engineering

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Evolutionary framework design in formulation of decision support models for production emissions and net profit of firm: Implications on environmental concerns of supply chains

Cited by 4 publications

References 31 publications

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

A novel procedure combining computational fluid dynamics and evolutionary approach to minimize parasitic power loss in air cooling of Li‐ion battery for thermal management system design

Multi-objective design optimization of battery thermal management system for electric vehicles

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