A Novel Active Online State of Charge Based Balancing Approach for Lithium-Ion Battery Packs during Fast Charging Process in Electric Vehicles

Abstract

Non-uniformity of Lithium-ion cells in a battery pack is inevitable and has become the bottleneck to the pack capacity, especially in the fast charging process. Therefore, a balancing approach is essentially required. This paper proposes an active online cell balancing approach in a tfast charging process using the state of charge (SOC) as balancing criterion. The goal of this approach is to complete pack balancing within the limited charging time. An adaptive extended Kalman filter (AEKF) is applied to estimate the pack cell SOC during the charging process to obtain accurate results under modeling errors and measurement noises. To implement the proposed AEKF, only one additional current sensor is required to obtain the current of each cell required for the SOC estimation. An experimental platform is established to verify the effectiveness of the proposed approach. The results show that the proposed balancing approach with the SOC as a balancing criterion can overcome the challenges of non-uniformity and flat voltage plateau and charge more capacity into a LiFePO 4 battery pack than those with the terminal voltage as a balancing criterion in the fast charging process.Keywords: lithium-ion battery pack; the active cell balancing for battery packs; fast charging with balancing; electric vehicles; adaptive extended Kalman filter; pack SOC estimation Highlights:• Adaptive extended Kalman filter (AEKF) is employed to estimate the pack cell state of charge (SOC) in real time, which is used as a balancing criterion to equalize cells in a LiFePO 4 battery pack in the fast charging process.• Only one additional current sensor in the chosen balancing circuit is required to accurately estimate pack cell SOC, leading to low cost implementation.• Balancing in the fast charging process based on online estimated SOC overcomes non-uniformity and allows more pack capacity to be charged. •Experimental platform is established to demonstrate that the performances based on the SOC criterion is better than those based on the terminal voltage criterion in terms of extra charged capacity of 2.07 Ah, equivalent to 13% of the nominal capacity of the chosen battery pack.

On-Board State-of-Charge Estimation of Li-Ion Battery in Hybrid Electric Aircraft Vehicles Using State Estimators – Case Study

“…Since in a Li-Ion battery pack the parameters are extracted once and used in the later estimations, an accumulated modelling error is generated. The novelty of the improved version AEKF SOC estimator is the use of "a fading memory factor to increase the adaptiveness for the modelling errors and the uncertainty of Li-Ion battery SOC estimation, as well as to give more credibility to the measurements", as is stated in [8]. When process errors and measurement output noises are considered, the discrete-time state space equation of the 3RC EMC Li-Ion battery dynamic model given in (1), and (2) can be generalized as:…”

“…Since in "real life" the dynamics of the battery is seriously affected by temperature, an improvement is done by considering a collection of three 3RC Li-Ion batteries whose the optimal values of the parameters are extracted for three different temperatures (5°C, 15°C and 20°C) as is shown in [7], Table 3.1, p.51, that can be updated dynamically based on a thermal model described also in [7]. Moreover, the reason to make this EMC battery model selection is to benefit of its simplicity and ability to capture accurately the entire dynamics of Li-Ion battery, as well as its easy realtime implementation with acceptable range of performance [8]. Also, "this choice is due to the early popularity of BMS for portable electronics, where the approximation of the battery model with the proposed EMC is appropriate", as is mentioned in [9].…”

“…More precisely, the AEKF SOC estimator is an EKF developed in details in [4][5] with the performance improved in [8]. In the same way as for EKF the noises and errors are taken into consideration in AEKF SOC estimator gain computation to obtain the optimal SOC estimation results.…”

“…As is stated in [4][5][6][7]10], experimental data and curve fitting techniques are used to find empirical equations relating the parameters with the operating conditions. The Li-Ion battery model can be simplified as is done in [7][8][9][10], the simplifying model procedure details can be found in [7], pp.45 -46, and also used in [10]. For simulation purpose, we combine this procedure with the new modeling approach introduced in [7], pp.…”

“…The first estimator, detailed also in [8], is an improved version of an extended Kalman filter (EKF), well documented in [4][5][6] and suggested also in [10], such that to estimate the Li-Ion battery SOC much accurately. The second SOC estimator is a nonlinear observer described in detail in [9] that is more simple to be implemented in real-time compared to the one described in [13].…”