Prediction and Analysis of the Driving Range of Electric Bus

2013 ◽  
Vol 427-429 ◽  
pp. 787-792
Author(s):  
Kan Zhao ◽  
Cong Zhu ◽  
Hong Wen Xia ◽  
Cheng Zeng
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Pack ◽  
Driving Cycles ◽  
Electric Bus ◽  
Driving Range ◽  
Electrochemical Model ◽  
Battery Module

In this paper, a method used to predict the driving range of electric bus based on electrochemical model of lithium ion battery was presented. Using a electric bus powered by lithium ion battery as an example, the driving ranges under three different driving cycles including American UDDS, European EUDC and Japanese 1015 were respectively predicted by the proposed method, and the effects of the temperature of battery pack and the number of battery module on the lowest state of charge SOCL required by the bus to travel a given distance were also analyzed.

scholarly journals A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2120 ◽  
Author(s):  
Wei He ◽  
Michael Pecht ◽  
David Flynn ◽  
Fateme Dinmohammadi
Keyword(s):  
Lithium Ion Battery ◽  
Solid Phase ◽  
Chemical Properties ◽  
Lithium Ion ◽  
State Of Charge ◽  
Critical Parameters ◽  
Estimation Accuracy ◽  
Moving Window ◽  
Battery Management ◽  
Electrochemical Model

State-of-charge (SOC) is one of the most critical parameters in battery management systems (BMSs). SOC is defined as the percentage of the remaining charge inside a battery to the full charge, and thus ranges from 0% to 100%. This percentage value provides important information to manufacturers about the performance of the battery and can help end-users identify when the battery must be recharged. Inaccurate estimation of the battery SOC may cause over-charge or over-discharge events with significant implications for system safety and reliability. Therefore, it is crucial to develop methods for improving the estimation accuracy of battery SOC. This paper presents an electrochemical model for lithium-ion battery SOC estimation involving the battery’s internal physical and chemical properties such as lithium concentrations. To solve the computationally complex solid-phase diffusion partial differential equations (PDEs) in the model, an efficient method based on projection with optimized basis functions is presented. Then, a novel moving-window filtering (MWF) algorithm is developed to improve the convergence rate of the state filters. The results show that the developed electrochemical model generates 20 times fewer equations compared with finite difference-based methods without losing accuracy. In addition, the proposed projection-based solution method is three times more efficient than the conventional state filtering methods such as Kalman filter.

Design and Simulation of an Accurate Neural Network State-of-Charge Estimator for Lithium Ion Battery Pack

2017 ◽  
Vol 10 (2) ◽  
pp. 186 ◽  
Author(s):  
Youssef Cheddadi ◽  
Omar Diouri ◽  
Ahmed Gaga ◽  
Fatima Errahimi ◽  
Najia Es-Sbai
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Pack ◽  
Network State
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