Online State of Charge Estimation for Battery of Electric Vehicle Using Sigma-Points Kalman Filters

2013 ◽  
Vol 427-429 ◽  
pp. 824-829
Author(s):  
Li Cun Fang ◽  
Gang Xu ◽  
Tian Li Li ◽  
Ke Min Zhu
Keyword(s):  
Electric Vehicle ◽  
Kalman Filters ◽  
Hybrid Electric Vehicle ◽  
Lithium Battery ◽  
Cell Model ◽  
Operating Conditions ◽  
State Of Charge ◽  
Battery Cell ◽  
Soc Estimation ◽  
Simulation And Experiment

An accurate state-of-charge (SOC) estimation of the hybrid electric vehicle (HEV) and electric vehicle (EV) battery pack is a difficult task to be performed online in a vehicle because of the noisy and low accurate measurements and the wide operating conditions in which the vehicle battery can operate. A Sigma-points Kalman Filters (SPKF) algorithm based on an improved Lithium battery cell model to estimate the SOC of a Lithium battery cell is proposed in this paper. The simulation and experiment results show the effectiveness and ease of implementation of the proposed technique.

scholarly journals State-of-Charge Estimation of Lithium-Ion Battery Based on Extended Kalman Filter Algorithm

2020 ◽  
pp. 420-437
Author(s):  
Wu Xiaogang ◽  
Xuefeng Li ◽  
Nikolay I. Shurov ◽  
Alexander A. Shtang ◽  
Michael V. Yaroslavtsev ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Battery Management ◽  
Soc Estimation ◽  
Ternary Polymer

As the core component of electric vehicle, lithium-ion battery needs to adopt effective battery management method to prolong battery life and improve the reliability and safety. The accurate estimation of the battery SOC can be used to prevent the battery over charge and over discharge, reduce damage to the battery and improve battery performance, which plays a vital role in the battery management system. The study of battery SOC estimation mainly focused on the battery model construction and SOC estimation algorithm. Aiming at the problem that the state of charge (SOC) of electric vehicle is difficult to be accurately estimated under complex operating conditions, based on the parameter identification of the equivalent circuit of a ternary polymer lithium-ion battery, an Extended Kalman Filter (EKF) algorithm was used to estimate the SOC of the ternary polymer lithium-ion battery. Simulation and experimental results show that the estimation of SOC can be carried out by using the EKF algorithm under the conditions of China Passenger Car Condition (Chinacar) and new European driving cycle (NEDC) Compared with the coulomb counting method, the average error of SOC estimation can be realized is 1.042% and 1.138% respectively, the maximum error within 4%. Application of this algorithm to achieve SOC estimation has good robustness and convergence

Modified Strong Tracking Filter for the SOC Estimation of Lithium Battery in Electric Vehicle

2012 ◽  
Vol 236-237 ◽  
pp. 1362-1367
Author(s):  
Ze Cheng ◽  
Yu Hui Zhang ◽  
Yan Li Liu
Keyword(s):  
Electric Vehicle ◽  
Lithium Battery ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Original Algorithm ◽  
Soc Estimation ◽  
Tracking Ability ◽  
Strong Tracking Filter ◽  
Vehicle Management ◽  
Tracking Filter

In electric vehicle management system, the key technology is to estimate the SOC (state of charge) of battery in dynamic process. This paper builds the state space equation of lithium battery based on improved Randle’s model, realizes the tracking estimation of SOC using modified STF(strong tracking filter) algorithm. The simulation result proves that compared with the original algorithm, modified algorithm can greatly improve the system’s tracking ability as well as the estimation accuracy of SOC.

Estimation of Battery Soc for Hybrid Electric Vehicle using Coulomb Counting Method

2018 ◽  
Vol 19 (2) ◽  
Author(s):  
Bachir Zine ◽  
Khoudir Marouani ◽  
Mohamed Becherif ◽  
Said Yahmedi
Keyword(s):  
Electric Vehicle ◽  
Electric Charge ◽  
Hybrid Electric Vehicle ◽  
State Of Charge ◽  
Experimental Results ◽  
Generic Model ◽  
Battery Electric Vehicle ◽  
Counting Method ◽  
Soc Estimation ◽  
Battery State Of Charge

Abstract:The autonomy of the Battery Electric Vehicle is a key point in the development and commercialization of this kind of vehicle. The requested autonomy is directly linked to the amount of the stored and remaining energy in the battery which is the State of Charge (SOC).This paper presents battery state of charge (SOC) estimation using coulomb counting method. So, the quantity of electric charge is calculated during the battery cycle of charge and discharge and compared to the estimated value based on the battery generic model. Also, experimental results are carried out in order to validate this study.

Modeling of Thermal Dynamics of a Connected Hybrid Electric Vehicle for Integrated HVAC and Powertrain Optimal Operation

2019 ◽  
Author(s):  
Nehal Doshi ◽  
Drew Hanover ◽  
Sadra Hemmati ◽  
Christopher Morgan ◽  
Mahdi Shahbakhti
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Optimal Operation ◽  
Operating Conditions ◽  
System Level ◽  
Electric Heater ◽  
Ambient Conditions ◽  
Ic Engine ◽  
Thermal Dynamics ◽  
Hybrid Electric

Abstract Integrated energy management across system level components in electric vehicles (EVs) is currently an under-explored space. Opportunity exists to mitigate energy consumption and extend usable range of EVs through optimal control strategies which exploit system dynamics via controls integration of vehicle subsystems. Additionally, information available in connected vehicles like driver schedules, trip duration and ambient conditions can be leveraged to predict the operating conditions for a vehicle when a validated model of the vehicle is known. In this study, data-driven and physics-based models for heating, ventilation and air-conditioning (HVAC) are developed and utilized along with the vehicle dynamics and powertrain (VD&PT) models for a hybrid electric vehicle (HEV). The integrated HVAC and VD&PT models are then validated against real world data. Next, an integrated relationship between the internal combustion (IC) engine coolant and the cabin electric heater is established and used to promote potential energy savings in cabin heating when the operating schedule is known. Finally, an optimization study is conducted to establish a control strategy which maximizes the HVAC energy efficiency whilst maintaining occupant comfort levels according to ASHRAE standards and improving usable range of the vehicle relative to its baseline calibration.

scholarly journals Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model

Batteries ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 4 ◽  
Author(s):  
Arun Chandra Shekar ◽  
Sohel Anwar
Keyword(s):  
Particle Swarm Optimization ◽  
Single Cell ◽  
Real Time ◽  
Cell Model ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Swarm Optimization ◽  
Soc Estimation ◽  
Electrochemical Model

With the ever-increasing usage of lithium-ion batteries, especially in transportation applications, accurate estimation of battery state of charge (SOC) is of paramount importance. A majority of the current SOC estimation methods rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation under different operating conditions or when the battery ages. This paper presents a novel real-time SOC estimation of a lithium-ion battery by applying the particle swarm optimization (PSO) method to a detailed electrochemical model of a single cell. This work also optimizes both the single-cell model and PSO algorithm so that the developed algorithm can run on an embedded hardware with reasonable utilization of central processing unit (CPU) and memory resources while estimating the SOC with reasonable accuracy. A modular single-cell electrochemical model, as well as the proposed constrained PSO-based SOC estimation algorithm, was developed in Simulink©, and its performance was theoretically verified in simulation. Experimental data were collected for healthy and aged Li-ion battery cells in order to validate the proposed algorithm. Both simulation and experimental results demonstrate that the developed algorithm is able to accurately estimate the battery SOC for 1C charge and 1C discharge operations for both healthy and aged cells.

Sign in / Sign up

Export Citation Format

Share Document