Electric vehicles use electricity as power instead of fossil fuels and are
the only long-term solution for future transportation. The power battery system
is the heart of electric vehicles. Only by fully understanding it can the smooth
promotion of electric vehicles be achieved. This article starts from the
perspective of the development trend of the main on-board power batteries for
electric vehicles at home and abroad, and focuses on analyzing the more
promising lithium-ion batteries and their battery management systems.
Unbalanced charging of the lithium-ion battery pack charger can easily
cause overcharge and discharge problems, seriously damaging its service life.
This article proposes a new smart charger charging mode that allows the battery
pack to be charged more safely and reliably, extending its service life,
increasing safety, and reducing usage costs.
1Vehicle lithium-ion battery management system
As the monitoring "brain" of electric vehicle batteries, the battery
management system (BMS) in hybrid electric vehicles can monitor the remaining
battery power and predict the power intensity of the battery, which facilitates
understanding of the entire battery system and control of the entire vehicle
system. .
In pure electric vehicles, BMS has intelligent adjustment functions such as
predicting remaining battery power, predicting driving mileage, and fault
diagnosis. BMS plays a particularly obvious role in lithium-ion batteries, which
can improve battery usage, extend battery life, and increase battery safety. BMS
will be a key technology for the development of electric vehicles in the
future.
As shown in Figure 1, the data acquisition module in the BMS measures the
voltage, current and temperature of the battery pack, and then transmits the
collected data to the thermal management module and safety management module
respectively for data display. The thermal management module controls the
temperature of the battery cells to ensure that the battery pack is within the
optimal temperature range.
The safety management module judges the voltage, current, temperature and
state of charge (SOC) estimation results of the battery pack. When a fault
occurs, it issues a fault alarm and promptly takes emergency protection measures
such as circuit breakage. The state estimation module estimates SOC and state of
health (SOH) based on the collected battery state data.
At present, it is mainly SOC estimation, and SOH estimation technology is
not yet mature. The energy management module controls the charging and
discharging process of the battery, including battery power balancing
management, to eliminate the problem of inconsistent power of each cell in the
battery pack. The data communication module uses CAN communication to realize
communication between the BMS and on-vehicle equipment and non-vehicle
equipment.
The core functions of BMS are SOC estimation, equalization management and
thermal management. In addition, it also has other functions such as charge and
discharge management, precharger charging management, etc. During the battery
charging and discharging process, it is necessary to manage according to
relevant parameters such as environmental status and battery status, and set the
optimal charging and discharging curve of the battery, such as setting the
charger charging current, charger upper limit voltage value, discharge lower
limit voltage value, etc. The capacitive load present in the high-voltage system
circuit of an electric vehicle is equivalent to a short circuit at the moment of
power-on. Therefore, pre-charger charging management is required to prevent the
transient current impact of high-voltage circuit power-on.
2 Core functions of battery management system
2.1SOC estimation
SOC is used to describe the remaining power of the battery and is one of
the most important parameters during battery use. SOC estimation is the basis
for judging battery overcharge and overdischarge. Accurate estimation can avoid
overcharge and discharge problems of the battery pack to the greatest extent,
allowing it to operate more reliably.
The estimation of battery SOC shows very strong nonlinearity under the
influence of changes in the internal working environment and external use
environment. There are many internal and external factors that affect battery
capacity, such as battery temperature, battery life, battery internal
resistance, etc. It is very difficult to accurately estimate SOC.
The existing SOC estimation methods are as follows:
(1) Ampere-hour measurement method. The ampere-hour measurement method does
not consider changes in the internal structure and state of the battery, so it
has the advantages of simple structure and convenient operation. However, the
accuracy of this method is not high. If the current measurement accuracy is not
high, the cumulative SOC error will continue to increase as time goes by,
affecting the final result. This method is suitable for measuring battery SOC on
electric vehicles. If the measurement accuracy can be improved, it can be
regarded as a simple and reliable SOC measurement method.
(2) Open circuit voltage method. The open circuit voltage of lithium-ion
batteries has an approximately linear relationship with SOC, which can be used
to determine the internal state of the battery. However, due to the strict
measurement requirements, which require the battery to rest for at least 1 hour,
it is not suitable for use alone for online real-time detection of batteries in
electric vehicles. Under normal circumstances, because the open circuit voltage
method has a higher accuracy in estimating the value at the beginning and end of
charging, the open circuit voltage method is often used in combination with the
ampere-hour measurement method.
(3) Kalman filtering method. With its excellent ability to correct errors,
the Kalman filter method is particularly suitable for hybrid batteries with
severe current fluctuations. The disadvantage of this estimation method is that
it requires high system processing speed.
(4) Neural network method. Neural networks have the characteristics of
distributed parallel processing, nonlinear mapping and adaptive learning, so
they can be used to simulate battery dynamic characteristics and estimate SOC.
However, this method requires a large amount of reference data for the neural
network to learn, and the data and training method requirements are high,
otherwise it will cause unacceptable errors.
2.2 Balanced management
There are many processes to go through in the production of batteries, and
differentiation will lead to inconsistent conditions. The difference between
battery cells is mainly reflected in the differences in their internal
resistance and capacity over time and temperature changes. Large differences
between cells are more likely to cause overcharge or overdischarge, causing
battery damage. Achieving battery balancing can maximize the effectiveness of
power batteries, extend battery life, and increase safety. At this stage, the
mainstream equilibrium methods at home and abroad are as follows:
(1) Resistance balancing method. This method is the main representative of
the energy dissipation equalization method. It is simple and low-cost, but the
energy loss is relatively large and the efficiency is low. It is only suitable
for systems with small current charging and discharging.
(2) Switched capacitor method. This method is the main representative of
non-energy dissipative equalization method, which makes up for the shortcomings
of resistance equalization. However, its control circuit is complex, the
equalization speed is slow, and it takes a long time, so it is not suitable for
high current use.
(3) Transformer balancing method. This method is an active balancing
control method for series battery packs based on a symmetrical multi-winding
transformer structure. Its disadvantages are that the circuit is complex, there
are many components, and the volume is too large, making it difficult to expand
the battery pack. Generally suitable for high current charging and
discharging.
(4) Centralized equilibrium. This method can quickly enable the entire
battery pack to transfer energy to the battery cells, and the centralized
balancing module is smaller in size. However, the balancing operation of
multiple cells cannot be performed in parallel and requires a large number of
cable connections, which is not suitable for battery packs with a large number
of cells.
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