Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm

Lü, Xueqin; Wu, Yinbo; Lian, Jie; Zhang, Yangyang; Chen, Chao; Wang, Peisong; Meng, Lingzheng

doi:10.1016/j.enconman.2020.112474

Cited by 457 publications

(167 citation statements)

References 118 publications

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“…Power battery is the core component of the power system of new energy vehicles, and lithium‐ion battery is the most widely used power battery for electric vehicles at present. It has the advantages of high energy density, long service life, low self‐discharge rate, green environmental protection etc 1‐4 . The capacity of lithium‐ion pouch cells is 5% to 15% higher than that of steel or aluminum lithium cells with the same size, and the weight is lighter.…”

Section: Introductionmentioning

confidence: 99%

A study of external surface pressure effects on the properties for lithium‐ion pouch cells

et al. 2020

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Summary Lithium‐ion pouch cells are widely used in electric vehicles because of their high energy density than other structures. There is an unavoidable external surface pressure between the cells in the process of packing and driving of electric vehicles. The influence of external surface pressure on the main properties of the lithium‐ion pouch cell has been studied, which is of great significance to packing batteries and reusing retired cells. In this study, a testing device applied for the measurement of constant external surface pressures of lithium‐ion pouch cells was first proposed and the different pressure stress‐strain distribution on the external surface of cells under semirigid material pads were analyzed by simulation. The effects of pressure on the capacity, internal resistance, and open circuit voltage of fresh and aged LiNixCoyMnzO2 (NCM) lithium‐ion pouch cells are analyzed through experiments under 1 Mpa external surface pressure. The results show that the internal resistance of fresh cells tends to decrease. The average percentage of the maximum reduction of internal resistance is 13.28%. The experiments also demonstrate that the capacity of aging cells increased by 2.3%. Irreversibility of capacity improvement indicates applying appropriate external surface pressure can improve the secondary utilization efficiency of aging cells.

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Section: Introductionmentioning

confidence: 99%

A study of external surface pressure effects on the properties for lithium‐ion pouch cells

et al. 2020

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“…GA is inspired by natural selection and genetic mechanism in biological evolution. It is a stochastic optimization method that was introduced and developed by Professor Holland in 1975 [45]. Essentially, GA is a global search algorithm that aims to find the best solution for optimized problems.…”

Section: Optimization With Genetic Algorithm (Ga)mentioning

confidence: 99%

Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System

et al. 2020

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“…Genetic algorithm (GA) is a heuristic search and optimization technique guided by the concepts of genetics and natural selection (Krishnakumar & Goldberg, 1992;Meena & Devanshu, 2017). GA finds practical application in the following domains: multi-vehicle task assignment in a drift field (Bai et al, 2018), path planning (Nazarahari et al, 2019), energy management of hybrid electric vehicle (Lü et al, 2020). In this study, the objective function is the PID command function which is dependent on the predictive error and three gain parameters…”

Section: Genetic Algorithm Based Pidmentioning

confidence: 99%

Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system

Okafor

Udekwe

Ibrahim

et al. 2020

Journal of Information and Telecommunication

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The manual tuning of controller parameters, for example, tuning proportional integral derivative (PID) gains often relies on tedious human engineering. To curb the aforementioned problem, we propose an artificial intelligence-based deep reinforcement learning (RL) PID controller (three variants) compared with genetic algorithm-based PID (GA-PID) and classical PID; a total of five controllers were simulated for controlling and trajectory tracking of the ball dynamics in a linearized ball-and-plate (B&P) system. For the experiments, we trained novel variants of deep RL-PID built from a customized deep deterministic policy gradient (DDPG) agent (by modifying the neural network architecture), resulting in two new RL agents (DDPG-FC-350-R-PID & DDPG-FC-350-E-PID). Each of the agents interacts with the environment through a policy and a learning algorithm to produce a set of actions (optimal PID gains). Additionally, we evaluated the five controllers to assess which method provides the best performance metrics in the context of the minimum index in predictive errors, steady-state-error, peak overshoot, and timeresponses. The results show that our proposed architecture (DDPG-FC-350-E-PID) yielded the best performance and surpasses all other approaches on most of the evaluation metric indices. Furthermore, an appropriate training of an artificial intelligencebased controller can aid to obtain the best path tracking.

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Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm

Cited by 457 publications

References 118 publications

A study of external surface pressure effects on the properties for lithium‐ion pouch cells

A study of external surface pressure effects on the properties for lithium‐ion pouch cells

Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System

Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system

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