CN118769989B - A hybrid vehicle battery management system - Google Patents

A hybrid vehicle battery management system

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Publication number
CN118769989B
CN118769989B CN202411068867.0A CN202411068867A CN118769989B CN 118769989 B CN118769989 B CN 118769989B CN 202411068867 A CN202411068867 A CN 202411068867A CN 118769989 B CN118769989 B CN 118769989B
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battery
battery pack
module
balancing
parameters
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CN118769989A (en
Inventor
鲍英木
代立宏
徐弋谦
靳子龙
江涛
郭荣璨
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Chery Automobile Co Ltd
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Chery Automobile Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Secondary Cells (AREA)

Abstract

The invention relates to a battery management system of a hybrid electric vehicle, which comprises a battery parameter real-time monitoring module, a data processing and analyzing module and a control and optimizing module which are sequentially connected, wherein the battery parameter real-time monitoring module is used for collecting battery parameters of a battery pack in real time through a sensor and sending the battery parameters to the data processing and analyzing module, the data processing and analyzing module is used for receiving the battery parameters and analyzing and processing the battery parameters to obtain current state information of the battery pack, and the control and optimizing module is used for accurately controlling and optimizing the battery pack according to the current state information of the battery pack. According to the method and the device, parameters of the battery pack are collected in real time, analyzed and processed, the battery pack is accurately controlled and optimized according to data processing and analysis results, fault diagnosis and early warning are achieved, and therefore the service efficiency, safety and service life of the battery are improved.

Description

Battery management system of hybrid electric vehicle
Technical Field
The disclosure belongs to the technical field of new energy automobiles, and particularly relates to a battery management system of a hybrid electric vehicle.
Background
Along with the rapid development of new energy automobile technology, hybrid automobiles are an important new energy automobile type, and the performance and the application range of the hybrid automobiles are widely focused. However, the performance of a Battery Management System (BMS) of a hybrid vehicle as a core management component of a battery pack directly affects the overall performance and service life of the hybrid vehicle. Therefore, how to design an efficient and accurate BMS system to realize accurate management and optimization of the hybrid electric vehicle battery pack is a problem to be solved in the technical field of the current new energy vehicles.
Disclosure of Invention
The present disclosure is directed to a battery management system for a hybrid vehicle, which solves the above-mentioned problems.
The technical scheme of the disclosure is as follows:
The battery management system of the hybrid electric vehicle comprises a battery parameter real-time monitoring module, a data processing and analyzing module and a control and optimizing module which are connected in sequence;
The battery parameter real-time monitoring module is used for collecting battery parameters of the battery pack in real time through a sensor and sending the battery parameters to the data processing and analyzing module;
the data processing and analyzing module is used for receiving the battery parameters, analyzing and processing the battery parameters to obtain the current state information of the battery pack;
And the control and optimization module is used for accurately controlling and optimizing the battery pack according to the current state information of the battery pack.
As a further optimization of the present disclosure, the battery parameters include voltage, current, and temperature.
As a further optimization scheme of the present disclosure, the current state information includes remaining power and battery health state information.
As a further optimization scheme of the disclosure, the battery parameter real-time monitoring module is also used for fault diagnosis and early warning functions, and abnormal conditions of the battery pack can be timely found and processed.
As a further optimization scheme of the present disclosure, the controlling and optimizing module accurately controls and optimizes the battery pack includes:
And according to the current state information and the control requirement of the battery pack, automatically adjusting a charging and discharging strategy to prevent the overcharge and overdischarge of the battery.
As a further optimization scheme of the present disclosure, according to the current state information and the control requirement of the battery pack, automatically adjusting the charge and discharge strategy includes:
when the battery power is lower than a preset value, the power output of the engine is preferentially ensured, the discharging speed of the battery is slowed down, and when the battery power reaches a preset sufficient power, the battery is preferentially used for driving so as to reduce the fuel consumption and the discharge.
As a further optimization scheme of the present disclosure, the control and optimization module is further configured to ensure parameter consistency between the battery cells through an equalization control algorithm, so as to further improve the service efficiency and the service life of the battery pack.
As a further optimization scheme of the present disclosure, the equalization control algorithm includes a passive equalization algorithm and an active equalization algorithm;
The passive balancing algorithm adopts a resistance balancing algorithm, discharges a battery with higher voltage in a resistance discharging mode, releases electric quantity in a heat form, and realizes balancing of the whole group of voltages;
the active equalization algorithm comprises an inductive equalization algorithm, a bidirectional DC-DC equalization algorithm or a capacitance-based equalization algorithm, and energy is transferred from one single battery to another single battery through an inductor, a capacitor or a DC-DC converter to realize equalization.
The beneficial effects of the present disclosure are:
according to the method and the device, parameters of the battery pack are collected in real time, analyzed and processed, the battery pack is accurately controlled and optimized according to data processing and analysis results, fault diagnosis and early warning are achieved, and therefore the service efficiency, safety and service life of the battery are improved.
Drawings
Fig. 1 is a block diagram of a system architecture of the present disclosure.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings, wherein it is to be understood that the following detailed description is for the purpose of further illustrating the application only and is not to be construed as limiting the scope of the application, as various insubstantial modifications and adaptations of the application by those skilled in the art can be made in light of the foregoing disclosure.
As shown in fig. 1, a battery management system of a hybrid electric vehicle comprises a battery parameter real-time monitoring module, a data processing and analyzing module and a control and optimizing module which are connected in sequence;
The battery parameter real-time monitoring module is used for collecting battery parameters of the battery pack in real time through a sensor, wherein the battery parameters comprise voltage, current and temperature, the battery parameters are sent to the data processing and analyzing module, the battery parameter real-time monitoring module is further used for fault diagnosis and early warning functions, abnormal conditions of the battery pack can be timely found and processed, and the voltage data comprise total voltage of the whole battery pack and voltage of single battery units. These data are used to monitor the state of charge and health of the battery, current data include real-time measurements of current into and out of the battery, to help calculate state of charge (SOC) and state of discharge (SOD), and to detect over-current conditions, and temperature data include temperature information collected from multiple points of the battery pack, which is critical to prevent battery overheating and cold start problems.
The data processing and analyzing module is used for receiving the battery parameters, analyzing and processing the battery parameters to obtain current state information of the battery pack, wherein the current state information comprises residual electric quantity and battery health state information. The resolving and processing process comprises the following steps:
And (3) checking and filtering the data, namely checking the acquired original data, ensuring the accuracy and reliability of the data, removing noise through a filtering algorithm and improving the accuracy of the data.
Algorithm calculation:
And estimating the current charge level (SOC) of the battery based on algorithms such as a current integration method, an open circuit voltage method, a Kalman filtering method and the like and combining parameters such as the voltage, the current, the temperature and the like of the battery.
SOH assessment-the state of health (SOH) of a battery is assessed by analyzing factors such as capacity decay, internal resistance increase, etc. of the battery.
Power and energy calculation, namely calculating the output power of the battery and the total energy stored according to parameters such as the voltage, the current and the like of the battery.
Fault diagnosis and prediction by analyzing various parameters of the battery, monitoring abnormal conditions such as overcharge, overdischarge, overcurrent, overheat, etc., and taking corresponding precautions such as disconnection, limiting power, or issuing a warning. Meanwhile, based on data trend analysis, possible faults of the battery are predicted, and maintenance is performed in advance.
The processed and analyzed current state information of the battery is sent to the whole vehicle controller or other related systems by the BMS through a bus (such as a CAN bus), and meanwhile, the current state information is displayed to a driver on an instrument panel of the vehicle, and the method comprises the following steps:
And the SOC information is used for displaying the current charge level of the battery and helping a driver to know the remaining endurance mileage.
SOH information, which reflects the health condition of the battery and provides a reference for battery maintenance for a driver.
And the fault and warning information is used for reminding a driver of paying attention in time when the battery is faulty or abnormal.
And the control and optimization module is used for accurately controlling and optimizing the battery pack according to the current state information of the battery pack.
The control and optimization module performs accurate control and optimization on the battery pack, and the control and optimization module comprises the following steps:
According to the current state information and the control requirement of the battery pack, the charging and discharging strategy is automatically adjusted, and the overcharge and overdischarge of the battery are prevented, so that the service efficiency and the service life of the battery are improved.
According to the current state information and the control requirement of the battery pack, the automatic adjustment of the charge and discharge strategy comprises the following steps:
when the battery power is lower than a preset value, the power output of the engine is preferentially ensured, the discharging speed of the battery is slowed down, and when the battery power reaches a preset sufficient power, the battery is preferentially used for driving so as to reduce the fuel consumption and the discharge.
The control and optimization module is also used for guaranteeing the parameter consistency among the battery monomers through an equilibrium control algorithm, so that the service efficiency and the service life of the battery pack are further improved.
The specific implementation steps of the equalization control algorithm are as follows:
And detecting the battery state, namely monitoring each battery in the battery pack by the BMS to acquire key parameters such as the voltage, the temperature, the residual capacity (SOC) and the like of the battery.
And judging the balance condition, namely judging whether the BMS needs to perform balance management or not according to the monitoring result of the battery state. This is typically based on preset equalization conditions such as cell voltage differences, temperature differences, etc.
And (3) balancing control, namely if balancing management is needed, the BMS selects a dynamic balancing mode or a static balancing mode according to specific conditions and realizes balancing by controlling the balancing circuit. Dynamic equalization occurs during charge and discharge, while static equalization typically occurs after the battery pack is fully charged.
In the equalization process, the BMS can precisely control the transferred power and speed to ensure that the equalization effect reaches the expected value.
And monitoring the equalization effect, namely continuously monitoring the states of all batteries by the BMS in the equalization process so as to ensure that the equalization effect reaches the expected value. This includes monitoring the change in parameters such as voltage, temperature, etc. of the cells.
And finishing the balancing management, namely stopping the balancing management once the balancing reaches the expected value, and waiting for balancing again when the next balancing condition is met.
The equalization control algorithm comprises a passive equalization algorithm and an active equalization algorithm;
The passive balancing algorithm adopts a resistance balancing algorithm, discharges a battery with higher voltage in a resistance discharging mode, releases electric quantity in a heat form, and realizes balancing of the whole group of voltages;
the active equalization algorithm comprises an inductive equalization algorithm, a bidirectional DC-DC equalization algorithm or a capacitance-based equalization algorithm, and energy is transferred from one single battery to another single battery through an inductor, a capacitor or a DC-DC converter to realize equalization.
Thermal management is also included to control the battery cooling system to maintain the battery within an optimal operating temperature range.
The foregoing examples have expressed only a few embodiments of the present disclosure, which are described in more detail and detail, but are not to be construed as limiting the scope of the present disclosure. It should be noted that variations and modifications can be made by those skilled in the art without departing from the spirit of the disclosure, which are within the scope of the disclosure.

Claims (4)

1.一种混动汽车电池管理系统，其特征在于，包括依次连接的电池参数实时监测模块、数据处理与分析模块和控制与优化模块；1. A hybrid vehicle battery management system, characterized in that it comprises a real-time battery parameter monitoring module, a data processing and analysis module, and a control and optimization module connected in sequence; 所述电池参数实时监测模块用于通过传感器实时采集电池组的电池参数，并将所述电池参数发送至所述数据处理与分析模块；The real-time battery parameter monitoring module is used to collect battery parameters of the battery pack in real time through sensors and send the battery parameters to the data processing and analysis module; 所述数据处理与分析模块用于接收所述电池参数，并进行解析和处理，得到电池组的当前状态信息；The data processing and analysis module is used to receive the battery parameters, parse and process them to obtain the current status information of the battery pack; 所述控制与优化模块根据电池组的所述当前状态信息，对电池组进行精准控制和优化，包括：根据电池组当前的所述当前状态信息和控制需求，自动调整充放电策略，防止电池过充、过放发生，包括：在电池电量低于预设值时，优先保证发动机的动力输出，同时减缓电池放电速度；在电池电量达到预设充足电量时，优先使用电池驱动，以减少燃油消耗和排放；所述控制与优化模块还用于通过均衡控制算法，保证电池单体之间的参数一致性，进一步提高电池组的使用效率和寿命；所述均衡控制算法包括被动均衡算法和主动均衡算法；所述被动均衡算法采用电阻式均衡算法，通过电阻放电的方式，对电压较高的电池进行放电，以热量形式释放电量，实现整组电压的均衡；所述主动均衡算法包括电感式均衡算法、双向DC-DC均衡算法或基于电容的均衡算法；通过电感、电容或DC-DC转换器将能量从一个单体电池转移到另一个单体电池，实现均衡。The control and optimization module performs precise control and optimization of the battery pack based on its current state information. This includes automatically adjusting the charging and discharging strategy according to the current state information and control requirements of the battery pack to prevent overcharging and over-discharging. Specifically, when the battery charge is below a preset value, priority is given to ensuring engine power output while slowing down the battery discharge rate; when the battery charge reaches a preset sufficient level, battery power is prioritized to reduce fuel consumption and emissions. The control and optimization module also uses a balancing control algorithm to ensure parameter consistency between individual battery cells, further improving the battery pack's efficiency and lifespan. The balancing control algorithm includes passive and active balancing algorithms. The passive balancing algorithm uses a resistive balancing algorithm, discharging the higher-voltage batteries through resistor discharge to release electricity as heat, achieving voltage balance across the entire pack. The active balancing algorithm includes an inductive balancing algorithm, a bidirectional DC-DC balancing algorithm, or a capacitor-based balancing algorithm; it transfers energy from one individual battery cell to another through inductors, capacitors, or DC-DC converters to achieve balancing. 2.根据权利要求1所述的一种混动汽车电池管理系统，其特征在于，所述电池参数包括电压、电流和温度。2. The hybrid vehicle battery management system according to claim 1, wherein the battery parameters include voltage, current and temperature. 3.根据权利要求1所述的一种混动汽车电池管理系统，其特征在于，所述当前状态信息包括剩余电量和电池健康状态信息。3. A hybrid vehicle battery management system according to claim 1, wherein the current status information includes remaining power and battery health status information. 4.根据权利要求1所述的一种混动汽车电池管理系统，其特征在于，所述电池参数实时监测模块还用于故障诊断和预警功能，能够及时发现并处理电池组的出现的异常情况。4. The hybrid vehicle battery management system according to claim 1, wherein the real-time battery parameter monitoring module is further used for fault diagnosis and early warning functions, and can promptly detect and handle abnormal situations occurring in the battery pack.
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CN117577975A (en) * 2023-11-25 2024-02-20 四川诺乐电动科技有限公司 A lithium battery BMS remote management system

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CN117577975A (en) * 2023-11-25 2024-02-20 四川诺乐电动科技有限公司 A lithium battery BMS remote management system

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