An Active Balancing Method Based on SOC and Capacitance for Lithium-Ion Batteries in Electric Vehicles

An active balancing method based on the state of charge (SOC) and capacitance is presented in this article to solve the inconsistency problem of lithium-ion batteries in electric vehicles. The terminal voltage of each battery is collected first. Then, each battery SOC is accurately estimated by an extended Kalman filter (EKF) algorithm. In the experiment, the maximum absolute error of SOC evaluation is only 0.0061, and the mean absolute error is 0.0013 when the initial battery SOC is clear. Meanwhile, the maximum absolute error of SOC evaluation is 0.5 and the average absolute error of SOC is 0.0015 when the initial battery SOC is not clear. Afterward, an active balancing circuit based on the estimated battery SOC and capacitance is designed. The energy of capacitance is charged by the battery whose SOC is higher than the other batteries through the circuit to avoid the battery being overcharged. Then, the SOC of batteries gradually turn consistent. In the simulation experiment, the SOC difference of batteries is 7% before the balancing. Meanwhile, the SOC difference of batteries reduces to 0.02% after the balancing and the consuming time is merely 272s, which manifests that the proposed balancing method has a fast balancing speed and better balancing efficiency.

Active battery balancing system for electric vehicles based on cell charger

Cell imbalance can cause negative effects such as early stopping of the battery charging and discharging process which can reduce its capacity. In the previous active balancing research, the energy used for the balancing process was taken from the cell or battery pack, resulting in drop of electric vehicle driving range. In this paper, a cell charger based battery balancing system is proposed with a reduction in the number of switches. The use of a cell charger aims to increase the usable energy of the battery pack, since the energy used for the balancing process is taken directly from the grid. The use of fewer switches aims to reduce the cost and space used on the battery management system (BMS) hardware. The charger used for the balancing process has a maximum current of 3 A and a maximum voltage of 3.65 V while the number of switches used is <em>n</em>+5 for <em>n</em> batteries. A 15S1P 200 Ah LiFePO₄ battery pack consists of 15 cells used for testing purpose. The test results show that the time needed to equalize the 15 cell battery voltage reaches 6 hours from the difference between the highest and lowest battery cell voltages of 145.1 mV to 15.1 mV.

A Consensus Algorithm for Multi-Objective Battery Balancing

Batteries stacks are made of cells in certain series-parallel arrangements. Unfortunately, cell performance degrades over time in terms of capacity, internal resistance, or self-discharge rate. In addition, degradation rates are heterogeneous, leading to cell-to-cell variations. Balancing systems can be used to equalize those differences. Dissipative or non-dissipative systems, so-called passive or active balancing, can be used to equalize either voltage at end-of-charge, or state-of-charge (SOC) at all times. While passive balancing is broadly adopted by industry, active balancing has been mostly studied in academia. Beyond that, an emerging research field is multi-functional balancing, i.e., active balancing systems that pursue additional goals on top of SOC equalization, such as equalization of temperature, power capability, degradation rates, or losses minimization. Regardless of their functionality, balancing circuits are based either on centralized or decentralized control systems. Centralized control entails difficult expandability and single point of failure issues, while decentralized control has severe controllability limitations. As a shift in this paradigm, here we present for the first time a distributed multi-objective control algorithm, based on a multi-agent consensus algorithm. We implement and validate the control in simulations, considering an electro-thermal lithium-ion battery model and an electric vehicle model parameterized with experimental data. Our results show that our novel multi-functional balancing can enhance the performance of batteries with substantial cell-to-cell differences under the most demanding operating conditions, i.e., aggressive driving and DC fast charging (2C). Driving times are extended (>10%), charging times are reduced (>20%), maximum cell temperatures are decreased (>10 °C), temperature differences are lowered (~3 °C rms), and the occurrence of low voltage violations during driving is reduced (>5×), minimizing the need for power derating and enhancing the user experience. The algorithm is effective, scalable, flexible, and requires low implementation and tuning effort, resulting in an ideal candidate for industry adoption.

ANALYSIS OF BATTERIES ACTIVE BALANCE SCHEMES EFFICIENCY

The paper reviews the existing circuit solutions of devices for balancing electric batteries. The balancing principle on the basis of capacitive and inductive buffer elements has been described. It was shown the features of their work and the basic calculations for each device type. For circuits with transformer topology, the calculated values for determining the balancing current are indicated. Based on the circuit solutions analysis, the efficiency of using solutions based on inductive buffer elements is numerically determined and proved. Powerful batteries for power supply systems are used in the form of stacks, consisting of a series-parallel connection of single cells. During their operation, there is a problem of uneven discharge or charge, to compensate which it is necessary to make voltage levels balancing in the stack batteries. For safely using electrochemical batteries the using of specialized balancing devices is required. The most efficient, from an energy point of view, are active balancing systems. The analysis of the mathematical model of two types (capacitive and inductive) buffer elements operation allowed to give a qualitative assessment of their efficiency. The first, in comparison with inductive - not only have worse energy characteristics, but also do not allow to perform "scaling" of the device without significant complication of the control system. The current amplitude value in circuits with a capacitive buffer element is limited only by the internal parasitic resistances of the circuit elements, therefore, with a relatively large value of imbalance, in circuit elements (including batteries) takes place a significant energy loss in the form of heat which negatively effects on rechargeable battery parameters. The current amplitude value in the circuit based on inductive buffer elements is limited by the inductance value. It can be calculated at the device design stage. In addition, providing the control system with intermittent converter operation allows to reduce switching losses in the circuit power switches and increases the overall operation efficiency. With a large number of batteries (more than three) should be preferred transformer balancing systems, as a special case of inductive topology.