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This article presents an efficient and fast echelon battery equalization method based on wide voltage range bidirectional converter combined with DBSCAN (density-based spatial clustering of applications with noise) clustering control strategy. Thanks to the wide voltage range and bidirectional buck-boost characteristics of the four-switch bidirectional converter, the battery energy can be reasonably redistributed between the battery and the super capacitor. The converter operating in synchronous rectification mode can not only achieve bidirectional energy flow in a wide voltage range, but also has a high energy conversion efficiency. The DBSCAN clustering algorithm is used to divide all battery cells into groups, and each group may contain one or more adjacent and nonadjacent battery cells. First, the nonadjacent single cells are balanced and integrated into adjacent group with the closest voltage. Then, the groups with different voltages are balanced by group-to-group, thereby realizing rapid balance of the entire battery pack. In order to verify the effectiveness of the proposed method, simulations and experimental verification were carried out. The experimental results show that, comparing with traditional battery balancing methods, the proposed method achieves shorter balancing time and higher balancing efficiency. [ABSTRACT FROM AUTHOR]

A pack of batteries is one of the most important and expensive assemblies for an electric vehicle. A pack of batteries is comprised of several batteries connected in series. The number of the batteries connected depends on the operating voltage of the vehicle's on-board system as well as on the individual characteristics of the batteries used, e.g. the operating voltage of a single cell. One or several cells of a pack of batteries could be damaged if improperly exploiting an electric vehicle- excessively discharging the batteries or overloading the electric vehicle. If a self-converted vehicle does not use an intellectual BMS (battery management system) that can identify and register voltage drop for any individual cell in the high-load regime, e.g. when accelerating, it is difficult to identify and change the cells damaged. In case a cell does not demonstrate a complete failure, it is almost impossible to identify a defect in any regime other than the load regime. The research developed and compared three different methods for identifying defective battery cells. The methods were approbated on a converted Renault Clio. The experiment involved making voltage measurements in road tests, running the electric vehicle on a roll test bench and making voltage measurements of maximally discharged batteries in the no-load regime. A comparison of the measurement results revealed that the measurements made in the road tests were the most accurate and useful. After the experiment, the defective battery cells were replaced, thereby restoring the performance of the battery pack. [ABSTRACT FROM AUTHOR]

Variations in cell properties are unavoidable and can be caused by manufacturing tolerances and usage conditions. As a result of this, cells connected in series may have different voltages and states of charge that limit the energy and power capability of the complete battery pack. Methods of removing this energy imbalance have been extensively reported within literature. However, there has been little discussion around the effect that such variation has when cells are connected electrically in parallel. This work aims to explore the impact of connecting cells, with varied properties, in parallel and the issues regarding energy imbalance and battery management that may arise. This has been achieved through analysing experimental data and a validated model. The main results from this study highlight that significant differences in current flow can occur between cells within a parallel stack that will affect how the cells age and the temperature distribution within the battery assembly. [ABSTRACT FROM AUTHOR]

Abstract: Because of the inevitable inconsistency during manufacture and use of battery cells, cell variations in battery packs have significant impacts on battery pack capacities, durability and safety for electric vehicles (EVs). To reduce cell variations and increase pack capacity, cell equalization is essentially required. In the series of two papers, we discover that dissipative cell equalization (DCE) using dissipative resistances is a feasible on-line equalization method for battery packs in EVs. We subsequently propose on-line equalization algorithms for lithium-ion battery packs based on charging cell voltage curves (CCVCs). The objective of these algorithms is to maximize pack capacities by conditioning CCVCs. As the first paper of the series, we first briefly review equalization topologies and algorithms. We discover cell remaining charging capacity (RCC) can be on-line estimated and further propose DCE algorithm based on remaining charging capacity estimation (RCCE). We establish a pack model with 8 cells in series and simulate 4 scenes with different cell variations. RCCE–DCE algorithm is proved to be effective by comparing pack capacities with/without RCCE–DCE algorithm. The equalization capability and over-equalization prevention are further examined, and the result shows that RCCE–DCE algorithm is suitable for on-line equalization in EVs. [Copyright &y& Elsevier]

Abstract: In the first part of this work, we propose dissipative cell equalization (DCE) algorithm based on remaining charging capacity estimation (RCCE) and establish a pack model with 8 cells in series. The results show that RCCE-DCE algorithm is suitable for on-line equalization in electric vehicles (EVs) and no over-equalization happens. However, 1% pack capacity difference from the DCE theoretical pack capacity is observed with RCCE-DCE algorithm. Therefore, as the second part of the series, we propose fuzzy logic (FL) DCE algorithm based on charging cell voltage curves (CCVCs). Cell capacities and SOCs are fuzzily identified in FL-DCE algorithm by comparing cell voltages at the beginning and end of charging. Adaptive FL-DCE is further improved to prevent over-equalization and maintain the equalization capability. The simulation results show that pack capacity difference from the DCE theoretical pack capacity with the adaptive FL-DCE is smaller than that with RCCE-DCE algorithm, and the duration of the infant stage is also shorter. The proposed adaptive FL-DCE is suitable for on-line equalization in EVs and well prevents over-equalization. [Copyright &y& Elsevier]

Lithium-ion (Li-ion) batteries are nowadays the best tradeoff between performance, cost, and lifetime to store electric power and energy effectively. The reliable determination of the batteries’ instantaneous power capability and energy content is mandatory for many mobile high-energy and power-consuming applications. Herein, the battery impedance and state of charge (\SOC) are the relevant values. In energy storage systems, these values are to be considered for each battery cell individually, due to arising limitations caused by cell-specific variations. Simple algorithms to determine the battery system’s impedance and \SOC are presented including parameter and state-estimation techniques. Furthermore, methods are derived making the impedance and \SOC determination possible for a large number of particular cells in a battery system. The applicability of the demonstrated algorithms for battery control unit implementation is proved incorporating data achieved from load scenarios applied to a cell stack Li-ion battery module. Cell impedance and \SOC variations could be detected precisely. Moreover, a combination of impedance and \SOC spread was identified during typical battery operation. [ABSTRACT FROM PUBLISHER]

A dynamic simulation model for a battery pack is an important prerequisite for the simulation of electric vehicles. As the battery pack is a highly non-linear system, and their dynamic behaviour depends on different parameters, it is difficult to establish the relationship between the load voltage and the current under different temperatures and states of charge. Furthermore, the input and output data usually contain noise; therefore, the traditional neuralnetwork- based model is affected while training the parameters. To solve the problem, a stochastic fuzzy neural network (SFNN) which has a filtering effect on the noisy input is used to model the battery non-linear dynamics in this paper. In the parameter-learning algorithm for the SFNN, a novel cost function which contains the error variables is studied. Then, the modelling test is performed on an 80 A h nickel-metal hydride (Ni-MH) battery pack and the SFNN-based model for the Ni-MH battery pack is set up. The Federal Urban Driving Schedule cycle is performed to test the model. Compared with the traditional neural-network-based model, the SFNN model can simulate the battery dynamic better and has a filtering effect on the noisy input which is more suitable for practical applications. [ABSTRACT FROM AUTHOR]