Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm

Abstract

This paper proposes an optimal grouping method for battery packs of electric vehicles (EVs). Based on modeling the vehicle powertrain, analyzing the battery degradation performance and setting up the driving cycle of an EV, a genetic algorithm (GA) is applied to optimize the battery grouping topology with the objective of minimizing the total cost of ownership (TCO). The battery capacity and the serial and parallel amounts of the pack can thus be determined considering the influence of battery degradation. The results show that the optimized pack grouping can be solved by GA within around 9 min. Compared with the results of maximum discharge efficiency within a fixed lifetime, the proposed method can not only achieve a higher discharge efficiency, but also reduce the TCO by 2.29%. To enlarge the applications of the proposed method, the sensitivity to driving conditions is also analyzed to further prove the feasibility of the proposed method.

Challenges of the Deployment of Electric Cars in Brazil

“…Is the result in increased system capacity while keeping the pressure in the end is the battery [31], [32]. However, this terminal voltage, which is high as the number of connections in series increases, is essential for the good performance of the vehicle.…”

“…It should if noting also that there are problems as an increase of serial links to increase the tension in the end is the battery (keeping the system capacity) [31], [32]. Multiple connections in series, in turn, can meters generate a non -equalizing voltage during charging due to variation of internal resistance, and also the loss of the chemical properties cells [33].…”

Modeling of battery pack sizing for electric vehicles

“…However, a parametric approach for estimating vehicle energy consumption has been introduced by Simpson et al in reference [24]. The model developed by them is known as Parametric Analytical Vehicle Energy Consumption (PAMVEC).…”

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

“…Besides, some neural network algorithms have not been applied to battery sorting. For example, reference [114] uses genetic algorithm to optimize the battery grouping topology, thereby reducing the cost of The neural network algorithm is widely used, while SOM neural network algorithm is subject to some limitations. For example, it can easily be caught up in a local minimum when the initial conditions are poor.…”

“…Besides, some neural network algorithms have not been applied to battery sorting. For example, reference [114] uses genetic algorithm to optimize the battery grouping topology, thereby reducing the cost of battery packs and increasing life, reference [115] uses a deep convolutional neural network (DCNN) to estimate long-term battery cycle data, reference [116] is based on the DCNN algorithm to measure the voltage, current, and charging capacity of a part of the charging cycle, so as to realize the capacity estimation of the battery, reference [117] uses a combined convolutional neural network (CNN) to infer the state of charge (SOC) of the battery, and reference [118] uses the feed forward neural networks (FFNN) model and the extended Kalman filter algorithm to implement SOC estimation. Reference [119] employs a feed-forward neural network, convolutional neural network, and long short-term memory to estimate battery capacity and health status.…”