278 results about "Lithium ion battery pack" patented technology

The invention relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system based on dynamic optimization control and a multi-mode energy scheduling method. Comprising a renewable energy power generation unit which comprises at least two distributed power supplies with different power generation characteristics; the multi-stage hydrogen production device is configured to receive the electric energy output of the renewable energy power generation unit and perform proton exchange membrane water electrolysis hydrogen production; the hybrid energy storage system comprises a super capacitor bank and a lithium ion battery pack which are connected in parallel to the direct current bus through a bidirectional DC / DC converter; the dynamic optimization controller is internally provided with a multi-time-scale coordination algorithm and is used for acquiring meteorological prediction data, load demand data and an energy storage SOC state in real time; the multi-mode scheduling module comprises three working modes including an island operation mode, a grid-connected operation mode and an emergency standby mode; according to the scheme, hydrogen production economy, equipment durability and power supply reliability can be considered at the same time.

A machine learning method of estimating State of Charge (SOC) and State of Health (SOH) of a battery pack, the method including collecting time-series data of a battery pack; processing the collected data utilizing a reconstruction algorithm to reconstruct a first phase state space reconstruction; training a first neural network model using the first phase state space reconstruction for predicting the SOH; feeding the time-series data to the first neural network to predict an estimated SOH value; processing the time-series data utilizing the reconstruction algorithm to reconstruct a second phase state space reconstruction; training a second neural network model using the second phase state space reconstruction, taking into account the estimated SOH value, for predicting the SOC, and feeding the time-series data and the estimated SOH value to the second neural network to obtain an estimated SOC value. A system configured to perform the above method is also disclosed.

The invention relates to the technical field of lithium ion battery charging, and discloses a multi-device compatible intelligent fast-charging lithium ion battery system and a protocol matching method, the system comprises a charging device, the charging interface of which is used for being connected with a target device to charge a lithium ion battery pack of the target device; the battery management equipment acquires an initial communication signal of the connected target equipment in real time when the charging interface is connected with the target equipment, so as to extract a voltage fluctuation feature, a handshake time sequence feature and a protocol identification feature for generating an equipment feature vector; performing similarity matching on the equipment feature vector and the protocol feature parameter to obtain a candidate protocol set; according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load state of the target equipment, obtaining an optimal charging protocol and a corresponding charging curve template; and charging the lithium ion battery pack according to the optimal charging protocol and the charging curve template. And efficient, flexible and safe charging control can be realized.

The invention discloses a rapid equalizing charging method for a lithium ion battery pack, and belongs to the field of battery charging. The method comprises the following steps: step 1, establishing a first-order enhanced equivalent circuit model, constructing an aging model in combination with an Arrhenius equation, and comprehensively establishing an electric heating aging fusion model; 2, constructing a battery pack model through series connection based on a single battery model of the electrothermal aging fusion model; 3, configuring a deep reinforcement learning environment on the basis of the electrothermal aging coupling model, wherein the deep reinforcement learning environment is used for simulating the real state of interaction between a deep deterministic policy gradient algorithm (DDPG) and the battery pack in deep reinforcement learning; 4, a deep reinforcement learning Actor network, a Critic network and a reward function are constructed, and the target of the reward function is defined as the shortest charging time of the battery pack, SOC (state of charge) balance and minimum aging loss so as to support operation and result evaluation of a DDPG algorithm; and 5, training the model to obtain the optimal fast equalization charging current.

The invention discloses a distributed light storage and charging integrated system, which comprises a photovoltaic power generation unit, an energy storage unit, a charging unit, a power grid interface unit, an energy management controller and a direct current bus, the photovoltaic power generation unit comprises a DC / DC converter, and the photovoltaic power generation unit is connected with the DC bus through the DC / DC converter; the energy storage unit comprises a lithium ion battery pack, a flywheel energy storage device, a bidirectional DC converter and a flywheel controller. The lithium ion battery pack and the flywheel energy storage device are connected to the DC bus through the bidirectional DC converter and the flywheel controller respectively. The charging unit comprises at least one direct-current quick charging pile, at least one alternating-current slow charging pile and a DC / AC inverter, the direct-current quick charging pile is directly connected with the direct-current bus, and the alternating-current slow charging pile is connected with the direct-current bus through the DC / AC inverter; the power grid interface unit comprises an AC / DC converter and is connected with the DC bus through the AC / DC converter. According to the invention, through collaborative design of a modular hardware architecture and an intelligent time-sharing strategy, comprehensive improvement of economy, reliability and functionality of the system is realized.

The invention discloses an energy storage battery pack self-discharge abnormity diagnosis method based on short-time charging data, and belongs to the technical field of battery safety monitoring. In order to overcome the defects that an existing method depends on a complete charging and discharging period and is difficult to adapt to a multi-stage variable working condition scene, multi-stage charging data features are extracted, self-adaptive extraction of multi-working condition features is achieved in combination with an auto-encoder, a data driving model fusing GCN-BiLSTM (Graph Convolution-Bidirectional Long Short-Term Memory) is constructed, and a multi-stage variable working condition scene is obtained. According to the method, the RCC (Remaining Charge Capability) of the single battery is accurately estimated, and the RCC of the single battery is accurately estimated. And finally, on the basis of the size, the distribution and the change condition of each single RCC in the battery pack, the overall inconsistency of the battery pack, the SOC inconsistency of the single batteries and the self-discharge fault can be diagnosed. And a guarantee is provided for safe operation of the energy storage system.

The present invention relates to a method for active balancing of lithium-ion battery packs based on time optimization, belonging to the field of lithium batteries. A hierarchical active balancing topology structure of module-to-module architecture and unit-to-module architecture is constructed; a relationship between the battery state of charge (SOC) and the pulse width modulation (PWM) duty cycle of the equalizer is established, and the balancing controller is made to act directly on the field effect transistor of the equalizer; by reducing the micro-cycle period between the charge and discharge of the intermediate battery, the SOC of the battery is ensured to converge to a point; a hierarchical balancing topology structure is also developed to simplify the battery pack structure and reduce computing costs. The present invention adopts a time-optimal model predictive control (MPC) strategy and designs a time-optimal method for reducing the micro-cycle of charge and discharge of the intermediate battery, overcoming the problems of large energy loss and rapid battery temperature rise when solving the problem of battery pack inconsistency, and improving the robustness of the system.

The present disclosure relates to a lithium-ion battery component and methods for manufacturing the lithium-ion battery component. The battery component, in some examples, includes a current collector, a porous deposit of first active material and first binder on the current collector, and a solvent-free electrode layer of second active material and second binder laminated with the porous deposit to at least partially occupy the pores of the porous deposit.

The invention discloses a thermal management control method and system for a lithium ion battery pack, and relates to the field of thermal management control, and the method comprises the steps: obtaining a heat production limit value of the lithium ion battery pack under the condition of a configuration combination scheme; simulating a reaction state when the lithium ion battery pack is subjected to thermal runaway, and evaluating the heat burst and combustion speed of the lithium ion battery pack in combination with a temperature change curve in the thermal runaway process; screening out an optimal arrangement mode of the lithium ion battery pack, and establishing test sample points taking the number of cooling channels and the thickness of the phase change material as design quantities; simulating the heat absorption process of the lithium ion battery pack in the optimal arrangement mode, and obtaining the optimal number of cooling channels and the thickness of a phase change material in a target range according to a simulation result; and establishing a target optimization model relationship according to the optimal number of cooling channels, adjusting the structure of the liquid cooling plate, and optimizing the thermal management process of the lithium ion battery pack. According to the invention, the battery pack can be kept in a safe temperature range in the rapid charging and discharging process.

The invention relates to the technical field of hydrogen production, in particular to an off-grid micro-grid hydrogen production system containing hybrid energy storage and a residual electricity consumption method. Comprising a renewable energy power generation unit, a hybrid energy storage unit, a hydrogen production unit, a standby load unit and a central controller, the renewable energy power generation unit is connected with the hybrid energy storage unit through a direct current bus, the hybrid energy storage unit comprises a super capacitor bank and a lithium ion battery pack, the super capacitor bank and the lithium ion battery pack are connected to the direct current bus in parallel through a bidirectional DC / DC converter, and power distribution of the super capacitor bank and the lithium ion battery pack is dynamically complementary based on real-time fluctuation frequency; the hydrogen production unit comprises an electrolytic cell and a hydrogen storage device, the electrolytic cell is connected with the direct current bus through a first DC / DC converter, and the standby load unit is connected with the direct current bus through a second DC / DC converter; the scheme can accurately quantify the polarization effect and dynamically correct the hydrogen production power.

The invention relates to a lithium ion battery pack internal short circuit fault diagnosis method based on relaxation time and an adaptive clustering algorithm, and belongs to the technical field of batteries. The method comprises the following steps: firstly, establishing a battery operation database, then screening out relaxation voltage of each monomer in a standing stage after charging of a battery pack is finished, establishing a relaxation time curve, and taking statistical characteristics of the relaxation time curve as fault characteristics; taking the fault feature of each monomer as the input of a self-adaptive DBSCAN algorithm, obtaining a critical neighborhood radius between an abnormal point and a normal point of each monomer, and taking the critical neighborhood radius as an outlier index of each monomer; and calculating a fault score of the corresponding monomer according to the outlier index of each monomer, and realizing diagnosis and positioning of an internal short circuit fault according to a set fault score threshold. According to the method, the effective features of the short-circuit fault in the battery are extracted, and the adaptive clustering algorithm is adopted for quantitative evaluation, so that the recognition accuracy is improved.

The invention provides a lithium ion battery cabinet of an energy storage power station. The lithium ion battery cabinet comprises a cabinet body with an accommodating cavity, an energy storage lithium ion battery pack and a cooling circulation system, the water circulation system comprises a cooling medium inlet located in the lower portion of the cabinet body, a cooling medium outlet located in the upper portion of the cabinet body, and a cooler which is connected with the cooling medium inlet and the cooling medium outlet and located outside the cabinet body. The cooling medium inlet and the cooling medium outlet are both connected with the containing cavity of the cabinet body, and the energy storage lithium ion battery pack is immersed in the cooling medium. According to the battery cabinet disclosed by the invention, the lithium ion batteries in the cabinet body are directly cooled by the cooling medium mainly comprising water, the batteries are all immersed in the cooling medium, the cooling efficiency is high, the safety of the batteries is good, and fire accidents of the batteries can be avoided when thermal runaway occurs in the batteries.

The invention discloses an abnormal battery detection method based on voltage change characteristics and local abnormal factors, relates to the technical field of battery detection, and solves the technical problems that only abnormity of a battery pack can be detected and abnormal batteries in the battery pack cannot be determined in the prior art. The method comprises the following steps: acquiring real-time operation data of a lithium ion battery pack from a cloud database, and preprocessing the real-time operation data; extracting standard voltage data from the preprocessed real-time operation data, and screening out suspected abnormal batteries according to the standard voltage data by using a 3 sigma criterion; according to the standard voltage data, extracting feature data of a plurality of batteries in a PCA mode; calculating the internal resistance of the battery according to the real-time operation data; screening real abnormal batteries from the suspected abnormal batteries according to the characteristic data and the internal resistance of the plurality of batteries; the accuracy of an abnormal battery detection result can be improved, and thermal runaway early warning can be made in advance.

A computer-implemented method for modeling thermal and electrochemical properties of lithium-ion battery packs is disclosed. A physics-based thermo-electro-chemical battery model is received comprising electrochemical reaction equations and thermal transport equations. A computer aided design (CAD) file is imported defining physical cell arrangements within a battery pack. Thermal connectivity matrices are automatically generated from the CAD file, where the matrices define heat transfer pathways between adjacent cells. A scaling algorithm transforms single-cell Single-Particle Model (SPM) equations into multi-cell coupled differential equations for the battery pack. The thermal and electrochemical behavior are simulated across the battery pack using the scaled equations to determine at least one battery performance characteristic based on the simulation.

The invention relates to the technical field of lithium ion battery safety monitoring, and discloses a method and a device for early warning and positioning thermal runaway of a lithium ion battery pack in advance. The method comprises the following steps: firstly, collecting real-time voltage, temperature, current and serial number data of each battery cell, and constructing a battery cell state observation tensor with a time-space label; running a battery cell thermoelectric state extended Kalman estimation algorithm based on the tensor to generate a battery cell health state partial sequence lattice; and then constructing a thermal runaway risk propagation phase space grid, finally executing manifold analysis-based local attractor region identification and early warning decision under the constraint of the grid, and outputting a high-risk source cell number and an early warning level. According to the invention, the problems of early warning lag, lack of cell group state consistency dynamic evaluation and incapability of accurately positioning fault cells caused by dependence on later physical symptoms in the prior art are solved, and the thermal runaway early warning timeliness and positioning accuracy are improved.

The invention belongs to the field of lithium ion battery micro short circuit fault diagnosis, and discloses a method and system for detecting and quantitatively evaluating micro short circuit in a series lithium ion battery pack, and the method comprises the steps: carrying out the micro short circuit detection of the lithium ion battery pack based on a dynamic time warping algorithm; after the position of the micro-short-circuit battery is detected, the micro-short-circuit degree of the lithium ion battery is quantitatively evaluated based on a charge-discharge cycle voltage characteristic and charge quantity difference method. The problems that an existing micro short circuit fault diagnosis method based on a lithium ion battery model is complex in calculation, and battery parameters after the battery is aged need to be updated are solved; and moreover, the consumption of computing resources is reduced by using a method of detecting the position of the fault battery by using a dynamic time warping algorithm and then quantitatively evaluating the micro-short-circuit degree of the lithium ion battery.

The system for dismantling the spent lithium-ion battery pack includes: a transfer section including transfer rails and a gantry robot on the transfer rails; a discharge processing section including an index table on which a discharging task of discharging a battery pack is performed; a battery disassembly section including a worktable on which a disassembly task is performed to cut connections of the battery pack, disassembly robots, and first transfer trolleys; and a cell extraction section including an extraction robot, a second transfer trolley configured to carry out battery cells, of which the connections have been cut by the disassembly task, to the outside, and a third transfer trolley configured to carry out a tray on which the battery cells are placed to the outside. The index table, the worktable, the second transfer trolley, and the third transfer trolley are arranged directly below the transfer rail.

An automatic charging power-off protection circuit for a lithium ion battery pack is composed of four cells, the lithium ion battery pack is connected with a charging interface and a discharging interface through a charging main loop and a discharging main loop, and the automatic charging power-off protection circuit is characterized by comprising a main control chip U1 with the model of BQ40Z50, a voltage acquisition circuit, a current acquisition circuit and a primary protection circuit, and the voltage acquisition circuit, the current acquisition circuit and the primary protection circuit are connected with the main control chip U1. The automatic charging power-off protection circuit of the lithium ion battery pack is composed of an integrated circuit BQ40Z50 (a main control unit and an accurate electric quantity algorithm) and a peripheral circuit thereof, and supports the use of 1-4 strings of lithium cells, including voltage, current and temperature protection, electric quantity display and the like; each battery cell is independently monitored, and when the charging / discharging voltage or the charging / discharging current exceeds a protection threshold value, turn-off can be quickly realized, so that the control precision is high; according to the automatic charging power-off protection circuit of the lithium ion battery pack, an integrated circuit BQ40Z50 is adopted, so that a peripheral circuit is simple in design and low in cost, and miniaturization design of devices is facilitated.

The invention discloses a lithium ion battery thermal runaway propagation path prediction and intelligent blocking system. The system comprises a lithium ion battery pack, a high-precision sensor group, an intelligent blocking module, a central controller, an algorithm regulation and control module, a data processing module and a monitoring and feedback module. The intelligent blocking module dynamically adjusts the battery state according to the prediction result, and thermal runaway diffusion is avoided; the algorithm regulation and control module analyzes sensor data by using a dynamic neural radiation field NeRF model and generates an optimal blocking scheme; according to the method, the occurrence and propagation path of thermal runaway can be accurately predicted by adopting a high-precision sensor and an intelligent algorithm, so that the temperature and current are intelligently regulated and controlled for blocking, the safety and reliability of a battery system are effectively improved, and meanwhile, potential risks caused by thermal runaway can also be reduced.

The invention discloses a lithium ion battery pack SOH prediction method based on multi-source data fusion and reinforcement learning optimization, and relates to the field of lithium ion batteries, and the method comprises the steps: obtaining the historical data of the operation of a plurality of battery packs of the same specification, and processing the historical data; constructing a regression equation according to historical data; according to the regression equation, obtaining a regression coefficient in combination with historical data; acquiring a loss function according to the regression equation and the regression coefficient; updating the regression coefficient according to the loss function; acquiring current data of the plurality of to-be-predicted battery packs; according to the multiple groups of current data, the regression coefficient and the updated regression equation, the predicted capacity of the battery pack to be predicted is obtained; according to the method, the current data of the to-be-predicted battery pack can be obtained in real time, the predicted capacity is rapidly calculated in combination with the updated regression equation, the method is suitable for lithium ion battery pack SOH prediction in various actual scenes, and errors of prediction results are reduced.

The invention provides a lithium ion battery pack temperature prediction method and system and a storage medium, and the method comprises the steps: obtaining historical operation data of a battery pack liquid cooling system, carrying out the preprocessing of the historical operation data, and obtaining an operation data set; constructing a thermal management model according to the operation data set, and obtaining a training residual sequence of the thermal management model; training a neural network model according to the operation data set and the training residual sequence, and performing parameter optimization on the neural network model; combining the neural network model after parameter optimization with the thermal management model to obtain a battery pack temperature prediction model; and obtaining to-be-measured operation data of the battery pack liquid cooling system, and inputting the to-be-measured operation data into the battery pack temperature prediction model for temperature prediction to obtain a predicted temperature value. According to the embodiment of the invention, the to-be-measured operation data is input into the battery pack temperature prediction model for temperature prediction, so that the battery temperature of the to-be-measured lithium ion battery pack can be effectively predicted, and the temperature prediction efficiency of the lithium ion battery pack is improved.

The invention discloses an electric power storage system and equipment, and relates to the technical field of electric power systems. The system comprises a data acquisition and prediction module, a hybrid energy storage equipment group, an intelligent control module, a collaborative interaction module and a safety protection module. The data acquisition and prediction module acquires historical and predicted environment, power consumption and power supply quantity data; the intelligent control module calculates a storage stability coefficient and a power consumption matching index based on the data, and dynamically controls a hybrid energy storage device group consisting of a lithium ion battery pack, a super capacitor and a liquid flow battery pack to charge and discharge; the collaborative interaction module realizes power interaction with new energy equipment and a power grid, and the safety protection module ensures reliable operation of the system. The method can adapt to the new energy volatility, improves the power storage efficiency and the system stability, and is suitable for the scenes of new energy consumption, power grid peak regulation and the like.

The efficient heat exchanger comprises an integrated U-shaped aluminum shell, a topological optimization liquid cooling system, a built-in heat pipe heat dissipation system and a trigger type semiconductor refrigeration system, and all the systems achieve structural coupling with the U-shaped aluminum shell as a carrier. The flow channel of the topological optimization liquid cooling plate is generated through a machine learning model. According to the topological optimization liquid cooling plate in the heat exchanger liquid cooling system, the heat exchange efficiency is remarkably improved, and meanwhile the effects of light weight and material saving can be achieved. The whole battery adopts a U-shaped structure, so that the contact area between the battery and the heat exchanger is increased, and the heat exchange efficiency is improved. When the battery is in thermal runaway, the semiconductor refrigeration system can be used for quickly cooling, so that safety accidents are prevented. In addition, the heat pipe is used for heat dissipation, local overheating is avoided, and quick response can be achieved for heat dissipation. Meanwhile, the aluminum shell is filled with the heat pipes, the space utilization rate is high, the overall structure is compact, and the occupied space is small.

The invention provides a battery equalization control system, and relates to the technical field of lithium ion battery pack equalization, and the method comprises the steps: firstly, employing terminal voltage, temperature and internal resistance data, carrying out the cooperative online estimation of the SOC and SOH of a single body through an extended Kalman filtering method and an internal resistance increment method, and introducing a temperature compensation function to carry out the real-time correction of an estimation error; and constructing a comprehensive consistency index to dynamically trigger the equalization demand. Carrying out dimensionality reduction on the high-dimensional state of the battery pack by adopting intrinsic orthogonal decomposition, extracting a dominant mode, sending the dominant mode into a lightweight LSTM network, and outputting an optimal current sequence and duration taking balance time and energy consumption into consideration through small sample training; during online operation, model input is updated in a rolling manner every preset time, a path is recalculated, and rapid convergence and fine adjustment are completed through double-stage self-adaptive current control in an offline scene. The invention aims to solve the problems of low equalization speed, low efficiency and strong dependence on big data samples in the prior art.

The invention discloses a method for separating a positive electrode active material and a current collector of a waste lithium ion battery, and belongs to the technical field of recycling of the waste lithium ion battery. The method comprises the following steps: discharging and disassembling the waste lithium ion battery pack, and mechanically crushing to obtain a positive electrode current collector loaded with an active material; and activating the positive current collector loaded with the active material by adopting an aqueous solution containing an activating agent, adjusting the pH value of the system to be acidic, adding a positive separating agent to form an interface layer with the positive current collector, stripping, filtering, sorting and drying to obtain the positive current collector and the positive active material. The wettability of the positive electrode active material is improved by adopting the activating agent, so that hydrogen ions and the positive electrode separating agent can enter the aluminum foil layer to form a new interface layer, and the positive electrode active material is stripped. The method is high in separation efficiency, simple and convenient to operate and free of damage to the positive electrode active material.

The invention belongs to the field of battery thermal runaway early warning, and particularly relates to an early warning method and device for thermal runaway of a lithium ion battery pack, and the method comprises the steps: firstly collecting the voltage, temperature and current data of a battery cell, and judging the grade of a current working condition according to a preset rule; self-adaptively matching a corresponding alternating current excitation strategy according to the working condition grade and applying excitation; synchronously acquiring the compensated excitation current and the voltage response of each battery cell through a sampling chip configured with a compensation strategy; based on the response information, a multi-dimensional impedance anomaly judgment strategy is combined, and a judgment parameter set containing impedance deviation, thermal runaway risk, voltage and temperature deviation flag bits is calculated and generated; and finally, determining the thermal runaway risk level of the battery pack through an evaluation algorithm according to the parameter set, and packaging and reporting the thermal runaway risk level through CAN communication. According to the invention, on-line and accurate sensing and early warning of the internal state of the battery under various working conditions such as standing and dynamic operation are realized.

The invention discloses an automatic feeding device for lithium ion battery assembly, and particularly relates to the technical field of lithium ion battery assembly.The automatic feeding device comprises a base, a conveying belt used for conveying pole pieces, a suction cup and a suction cup controller used for controlling the suction cup to adsorb the pole pieces; a fixing frame is arranged on the base, a central processing unit is arranged on one side of the fixing frame, angle recovery mechanisms are arranged on the two sides of the base and used for recovering the angle of a pole piece moving to a material taking station, and a position adjusting mechanism is arranged on one side of the base and used for adjusting the position of a material taking point on a suction cup. The device further comprises a pole piece position capturing module. According to the invention, through a cooperative mode of pole piece position capture, intelligent calculation analysis and mechanical dynamic adjustment, angle and position deviations in a pole piece conveying process are accurately corrected, and accurate alignment of sucking disc material taking is ensured, so that the material taking success rate is improved, the production efficiency is optimized, and the lithium ion battery assembly quality and consistency are improved.

The invention relates to the technical field of battery equalization, in particular to a fuzzy control method for inter-pack equalization in a battery pack, which comprises the following steps of: acquiring voltage, current and SOC (State of Charge) values of each battery; calculating an SOC difference value and a voltage difference value of adjacent batteries in the group; when the SOC difference value is greater than a first SOC threshold value or the voltage difference value is greater than a first voltage threshold value, outputting a control signal through an in-group self-adaptive fuzzy PID controller, and controlling the on and off of an in-group equalization switch tube; and when the SOC difference value is smaller than the first SOC threshold value and the voltage difference value is smaller than the first voltage threshold value, the intra-group equalization is ended. The equalization problem caused by inconsistency of the single batteries in the existing series lithium ion battery pack is solved.

The invention relates to the technical field of lithium ion battery operation safety evaluation, relates to the technical field of battery safety, and aims to reasonably match working battery groups required in an actual application scene through reasonable grouping, prolong the service life of batteries through grouping operation, and avoid single battery modules adjacent in physical position as much as possible during grouping. According to the lithium battery pack, rapid heat rising and concentration during operation of the working battery pack are avoided, a buffer area exists in the lithium battery pack as much as possible during operation, the buffer area is beneficial to heat dissipation and serves as a preparation pack of the next-stage working battery pack, and through real-time monitoring of temperature / voltage abnormity of the battery module pack and monitoring of the adjacent battery pack, the energy consumption of the lithium battery pack is reduced. And dynamically adjusting the working priority. The system has the advantages that it is ensured that the system responds to the load requirement through the optimal battery module combination, the output capacity is maximized on the premise that safety is ensured, and the reliability and safety are improved when products with the high power and the high energy storage capacity are used for meeting the small electricity consumption scene.

The present application relates to a kind of lithium ion battery pack online floating charge method, comprising the following contents: when lithium battery pack is charged to 100%SOC, the charging voltage of charging equipment is adjusted to floating voltage V floating;When lithium battery pack is in discharge state, and the SOC of lithium battery pack is less than 90%, the working state of charging equipment is adjusted to constant-current charging to SOC reaches 90%, and then long-term floating is charged with floating voltage V floatingThe present application is measured and calibrated floating voltage V floating by long-time standing voltage under multiple working conditions, avoids the problems such as battery overcharge, positive material phase transition, internal resistance rise caused by traditional fixed high-voltage floating, greatly reduces the damage of long-term floating to battery, effectively improves the cycle service life of lithium ion battery pack in online scene such as UPS, base station, standby power supply and the like.