A hybrid vehicle battery management system

Abstract

The present disclosure relates to a hybrid vehicle battery management system, which comprises a battery parameter real-time monitoring module, a data processing and analysis module and a control and optimization module connected in sequence; 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 analysis module; the data processing and analysis module is used for receiving the battery parameters and performing analysis and processing to obtain current state information of the battery pack; the control and optimization module performs accurate control and optimization on the battery pack according to the current state information of the battery pack. The present disclosure collects parameters of the battery pack in real time, performs analysis and processing on the parameters, performs accurate control and optimization on the battery pack according to the data processing and analysis result, realizes fault diagnosis and early warning, and thus improves the use efficiency, safety and service life of the battery.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of new energy vehicles, and particularly relates to a hybrid vehicle battery management system. BACKGROUND

[0002] With the rapid development of new energy vehicle technology, hybrid vehicles as an important type of new energy vehicles have attracted widespread attention in terms of performance and application range. However, the battery management system (BMS) of a hybrid vehicle, as the core management component of the 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 achieve precise management and optimization of the battery pack of a hybrid vehicle is a problem that needs to be solved in the current new energy vehicle technology field. SUMMARY

[0003] The purpose of the present disclosure is to provide a hybrid vehicle battery management system to solve the above problems.

[0004] The present disclosure achieves the above-mentioned purpose through the following technical solutions:

[0005] A hybrid vehicle battery management system, comprising a battery parameter real-time monitoring module, a data processing and analysis module, and a control and optimization module connected in sequence;

[0006] The battery parameter real-time monitoring module is used to collect battery parameters of the battery pack in real time through a sensor and send the battery parameters to the data processing and analysis module;

[0007] The data processing and analysis module is used to receive the battery parameters and analyze and process them to obtain current state information of the battery pack;

[0008] The control and optimization module performs precise control and optimization of the battery pack according to the current state information of the battery pack.

[0009] As a further optimization scheme of the present disclosure, the battery parameters include voltage, current, and temperature.

[0010] As a further optimization scheme of the present disclosure, the current state information includes remaining power and battery health state information.

[0011] As a further optimization scheme of the present disclosure, the battery parameter real-time monitoring module is also used for fault diagnosis and early warning functions, which can timely discover and handle abnormal conditions of the battery pack.

[0012] As a further optimization scheme of the present disclosure, the precise control and optimization of the battery pack by the control and optimization module include:

[0013] According to the current state information and control requirements of the battery pack, the charging and discharging strategy is automatically adjusted to prevent overcharging and overdischarging of the battery.

[0014] As a further optimization scheme of the present disclosure, the automatic adjustment of the charging and discharging strategy according to the current state information and control requirements of the battery pack includes:

[0015] When the battery power is lower than the preset value, the power output of the engine is preferentially guaranteed, and the battery discharge speed is slowed down; when the battery power reaches the preset sufficient power, the battery is preferentially used for driving to reduce fuel consumption and emissions.

[0016] As a further optimization scheme of the present disclosure, the control and optimization module is further configured to ensure the parameter consistency among the battery monomers by a balancing control algorithm, and further improve the use efficiency and life of the battery pack.

[0017] As a further optimization scheme of the present disclosure, the balancing control algorithm includes a passive balancing algorithm and an active balancing algorithm.

[0018] The passive balancing algorithm adopts a resistance type balancing algorithm, discharges the battery with higher voltage by resistance discharge to release the power in the form of heat, and realizes the balancing of the whole group voltage.

[0019] The active balancing algorithm includes an inductance type balancing algorithm, a bidirectional DC-DC balancing algorithm or a balancing algorithm based on a capacitor; energy is transferred from one monomer battery to another monomer battery through inductance, capacitance or a DC-DC converter to realize balancing.

[0020] The present disclosure has the following beneficial effects:

[0021] The present disclosure realizes accurate control and optimization of the battery pack according to the data processing and analysis results by collecting the parameters of the battery pack in real time and analyzing and processing the parameters, realizes fault diagnosis and early warning, and thus improves the use efficiency, safety and life of the battery. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is a system structure block diagram of the present disclosure. DETAILED DESCRIPTION

[0023] The following detailed description of the present application will be further described with reference to the accompanying drawings, and it is necessary to point out here that the following detailed description is only used to further illustrate the present application, and cannot be understood as limiting the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application according to the above application content.

[0024] As Figure 1As shown, a hybrid vehicle battery management system includes a battery parameter real-time monitoring module, a data processing and analysis module, and a control and optimization module connected in sequence.

[0025] The battery parameter real-time monitoring module is used to collect battery parameters of the battery pack in real time through sensors, including voltage, current and temperature; and send the battery parameters to the data processing and analysis module; the battery parameter real-time monitoring module is also used for fault diagnosis and early warning function, which can timely find and handle the abnormal situation of the battery pack; the voltage data includes the total voltage of the whole battery pack and the voltage of a single battery unit. These data are used to monitor the state of charge and health of the battery; the current data include the real-time measurement of the current in and out of the battery, which helps to calculate the state of charge (SOC) and the state of discharge (SOD), and detect the overcurrent condition; the temperature data include the temperature information collected from multiple points of the battery pack, which is crucial for preventing battery overheating and cold start problems.

[0026] The data processing and analysis module is used to receive the battery parameters and analyze and process them to obtain the current state information of the battery pack; the current state information includes the remaining power and the battery health status information. The analysis and processing process includes:

[0027] Data verification and filtering: verify the collected raw data to ensure its accuracy and reliability, and remove noise through filtering algorithm to improve the accuracy of the data.

[0028] Algorithm calculation:

[0029] SOC estimation: based on algorithms such as current integration method, open circuit voltage method, Kalman filter method, combined with parameters such as voltage, current and temperature of the battery, to estimate the current charging level (SOC) of the battery.

[0030] SOH evaluation: by analyzing factors such as capacity degradation and internal resistance increase of the battery, to evaluate the health status (SOH) of the battery.

[0031] Power and energy calculation: according to the parameters such as voltage and current of the battery, to calculate the output power and total energy stored by the battery.

[0032] Fault diagnosis and prediction: by analyzing various parameters of the battery, to monitor abnormal conditions such as overcharge, overdischarge, overcurrent, overheating, etc., and take appropriate preventive measures such as disconnecting, limiting power or issuing warnings. At the same time, based on data trend analysis, to predict possible faults of the battery and perform maintenance in advance.

[0033] The current state information of the battery after processing and analysis will be sent by the BMS to the vehicle controller or other related systems through the bus (such as CAN bus), and also displayed on the instrument panel of the vehicle for the driver, including:

[0034] SOC information: Displays the current charge level of the battery, helping the driver understand the remaining range.

[0035] SOH information: Reflects the health of the battery, providing the driver with a reference for battery maintenance.

[0036] Fault and warning information: When the battery has a fault or anomaly, it will promptly remind the driver to pay attention.

[0037] The control and optimization module accurately controls and optimizes the battery pack based on the current state information of the battery pack.

[0038] The control and optimization module accurately controls and optimizes the battery pack, including:

[0039] According to the current state information and control requirements of the battery pack, automatically adjust the charging and discharging strategy to prevent overcharging and overdischarging of the battery, thereby improving the efficiency and life of the battery.

[0040] According to the current state information and control requirements of the battery pack, automatically adjust the charging and discharging strategy, including:

[0041] When the battery power is lower than the preset value, preferentially ensure the power output of the engine, while slowing down the battery discharge speed; when the battery power reaches the preset sufficient power, preferentially use the battery drive to reduce fuel consumption and emissions.

[0042] The control and optimization module is also used to ensure the parameter consistency between battery monomers through the equalization control algorithm, further improving the efficiency and life of the battery pack.

[0043] The specific implementation steps of the equalization control algorithm are:

[0044] Detect battery status: BMS first monitors each battery in the battery pack to obtain key parameters such as battery voltage, temperature, remaining capacity (SOC), etc.

[0045] Judge equalization conditions: Based on the monitoring results of the battery status, BMS will determine whether equalization management is needed. This is usually based on preset equalization conditions such as single battery voltage difference, temperature difference, etc.

[0046] Equalization control: If equalization management is needed, BMS will choose dynamic equalization or static equalization mode according to the specific situation, and realize equalization through control of the equalization circuit. Dynamic equalization is performed during charging and discharging, while static equalization is generally performed after the battery pack is fully charged.

[0047] During the equalization process, BMS will accurately control the transferred power and speed to ensure that the equalization effect reaches the expected value.

[0048] Monitoring the balancing effect: During the balancing process, the BMS will continuously monitor the status of each battery to ensure that the balancing effect meets the expectations. This includes monitoring changes in parameters such as the voltage and temperature of individual cells.

[0049] Ending the balancing management: Once the balancing reaches the expected level, the BMS will stop the balancing management and wait for the next time the balancing conditions are met to perform balancing again.

[0050] The balancing control algorithm includes passive balancing algorithm and active balancing algorithm;

[0051] The passive balancing algorithm uses a resistive balancing algorithm, which discharges the battery with higher voltage through resistive discharge, releases the electric quantity in the form of heat, and realizes the balancing of the whole group voltage;

[0052] The active balancing algorithm includes inductive balancing algorithm, bidirectional DC-DC balancing algorithm or capacitor-based balancing algorithm; through inductance, capacitance or DC-DC converter, the energy is transferred from one single battery to another, realizing balancing.

[0053] It also includes thermal management, controlling the battery cooling system to keep the battery within the optimal working temperature range.

[0054] The above embodiments only express several embodiments of the present disclosure, and the description is more specific and detailed, but it cannot be understood as a limitation on the scope of the present patent. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present disclosure, a number of modifications and improvements can be made, which are within the scope of protection of the present disclosure.

Claims

1. A hybrid vehicle battery management system, characterized by, The battery parameter real-time monitoring module, the data processing and analysis module, and the control and optimization module are sequentially connected. The battery parameter real-time monitoring module is configured to collect battery parameters of the battery pack in real time through a sensor and send the battery parameters to the data processing and analysis module. The data processing and analysis module is configured to receive the battery parameters and analyze and process the battery parameters to obtain current state information of the battery pack. The control and optimization module is configured to accurately control and optimize the battery pack according to the current state information of the battery pack, including automatically adjusting a charging and discharging strategy according to the current state information of the battery pack and a control requirement to prevent overcharging and overdischarging of the battery, including preferentially ensuring power output of the engine and slowing down a battery discharging speed when the battery power is lower than a preset value, and preferentially using the battery to drive to reduce fuel consumption and emissions when the battery power reaches a preset sufficient power; the control and optimization module is further configured to ensure parameter consistency between the battery monomers through a balancing control algorithm to further improve use efficiency and a service life of the battery pack; the balancing control algorithm includes a passive balancing algorithm and an active balancing algorithm; the passive balancing algorithm adopts a resistance balancing algorithm, discharges a battery with a higher voltage in a resistance discharge manner to release power in the form of heat, and balances the whole group voltage; the active balancing algorithm includes an inductance balancing algorithm, a bidirectional DC-DC balancing algorithm, or a balancing algorithm based on a capacitor; the energy is transferred from one battery monomer to another battery monomer through an inductor, a capacitor, or a DC-DC converter to achieve balancing.

2. The hybrid vehicle battery management system of claim 1, wherein, The battery parameters include voltage, current, and temperature.

3. The hybrid vehicle battery management system of claim 1, wherein, The current state information includes residual power and battery health state information.

4. The hybrid vehicle battery management system of claim 1, wherein, The battery parameter real-time monitoring module is further configured to have a fault diagnosis and early warning function, and can timely find and handle abnormal conditions of the battery pack.

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