402 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 lithium ion battery pack multi-fault diagnosis method, a lithium ion battery pack multi-fault diagnosis device, terminal equipment and a computer readable storage medium. The lithium ion battery pack multi-fault diagnosis method comprises the steps of acquiring terminal voltage of each first battery monomer in a lithium ion battery pack to be diagnosed; extracting a first feature value of each first battery monomer under the target time domain feature; inputting the first feature value into a preset fault diagnosis model, so that the model calculates the distance between the first feature value and a second feature value of each second battery monomer in the model under the target time domain feature, and screening out a plurality of target feature values with the closest distance according to a preset closest number; voting is carried out on the target characteristic values, and the fault type of the second single battery corresponding to the target characteristic value with the maximum votes is used as the fault type of the current first single battery to be diagnosed; and determining the fault of the lithium ion battery pack according to the fault types of all the to-be-diagnosed first battery cells. According to the invention, multi-fault diagnosis in the lithium ion battery can be realized.

The invention relates to a lithium ion battery pack liquid cooling system temperature prediction method and system based on improved PSO-ABC-BP, and the method comprises the steps: carrying out the lumped modeling of a lithium ion battery pack liquid cooling system, and obtaining a lithium ion battery pack liquid cooling system model; acquiring operation data of the liquid cooling system of the lithium ion battery pack, and performing data preprocessing to form an operation data set of the liquid cooling system of the lithium ion battery pack; designing the structure of a BP neural network according to the battery pack temperature and the temperature influence parameters of the lithium ion battery pack liquid cooling system operation data set; using an improved PSO-ABC algorithm to optimize the initial weight and the threshold value of the BP neural network to obtain a PSO-ABC-BP lithium ion battery pack liquid cooling system temperature prediction model; the method comprises the following steps: acquiring the latest acquired actual operation data of the lithium ion battery pack liquid cooling system, inputting temperature influence parameters into a PSO-BP lithium ion battery pack liquid cooling system temperature prediction model to obtain a temperature prediction value, and combining an improved PSO-ABC algorithm with a BP neural network to seek a better parameter combination, thereby improving the accuracy of a prediction result.

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 relates to an SOC (State of Charge) self-equalization converter of a series lithium ion battery pack, which comprises an n-path output isolation type Sepic converter, n-1 bridging capacitors and n lithium ion batteries, the n-path output isolation type Sepic converter comprises a power switch S1, an inductor Lp1, a capacitor Cp1, an excitation inductor Lm, a transformer T, a first-path output diode D1, an output capacitor C1, a second-path output diode D2, an output capacitor C2,..., and the like, and an nth-path output diode Dn and an output capacitor Cn. On the basis of transformer type equalization, the SOC self-equalization converter of the series battery pack is provided, a circuit comprises an n-winding output SEPIC converter, n-1 bridging capacitors and n lithium ion batteries, and the consistency of the secondary side of a transformer is achieved by adding one bridging capacitor between windings. Compared with other transformer equalization structures, a reset circuit or a reset winding does not need to be additionally arranged, and the control difficulty and cost of the circuit are reduced.

The invention discloses a battery pack thermal runaway early warning method and system, and relates to the technical field of lithium battery thermal runaway safety early warning. Comprising the following steps: acquiring an air pressure value, a CO value and a smoke value of a lithium ion battery pack in a set period, and taking the air pressure value, the CO value and the smoke value of the lithium ion battery pack at a single moment as data parameters; summarizing all data parameters to obtain a parameter matrix, and determining a variability index through an entropy weight method; determining a fusion data information score based on the variability index, and determining an air pressure value parameter, a CO parameter and a smoke parameter according to the fusion data information score; and comparing the fused data information score, the air pressure value parameter, the CO parameter and the smoke parameter with set thresholds respectively, judging the thermal runaway progress stage of the lithium ion battery pack according to a comparison result, and performing early warning according to a judgment result. According to the invention, the battery pack can be monitored in real time, so that workers can timely and fully know the thermal runaway progress stage of the battery, and the accuracy is high.

The invention provides a lithium ion battery assembly and a battery state detection method based on expansion pressure, and belongs to the technical field of lithium ion batteries. The battery assembly comprises: at least two battery cells; the rigid supporting plates and the ion capacitance type thin film sensors are clamped between every two battery monomers, each ion capacitance type thin film sensor comprises at least one sensing unit formed by an ion membrane layer and an electrode array layer arranged below the ion membrane layer, and the sensing units are used for measuring the expansion pressure of the battery monomers; the edge of the rigid supporting plate is convexly provided with a first guide butt joint part, and the locking column penetrates through the first guide butt joint part of the rigid supporting plate, so that each battery monomer and each rigid supporting plate are kept parallel and aligned, and the battery monomer has consistent pre-tightening force after being locked by the locking column. According to the invention, the consistency and the stress uniformity of each single battery in the module can be improved, and the service life and the safety of the battery assembly are prolonged.

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 a lithium ion battery pack internal short circuit fault detection and positioning method and device and a medium, and the method comprises the steps: designing a battery monomer voltage abnormity index based on the correlation analysis of the local resultant force of a lithium ion battery pack monomer voltage signal; battery pack temperature feature extraction based on kernel principal component analysis is carried out to obtain a single battery temperature square prediction error; and constructing a multi-scale fault detection statistic based on the voltage anomaly index and the temperature square prediction error to realize fault detection, and constructing a fault contribution degree function based on the temperature square prediction error to realize fault positioning. According to the method, the voltage anomaly index of the single battery is designed, the square prediction error of the temperature of the single battery is obtained through extraction, finally, the multi-scale fault detection statistics and the fault contribution degree function are constructed to achieve fault detection and positioning, and the method is a pure data driving method and is low in equipment cost, easy to operate and high in reliability. And the temperature and voltage signal changes of the battery monomers in the early stage of internal short circuit are sensitive.

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 provides a lithium ion battery assembly and a battery expansion pressure detection method, and belongs to the technical field of lithium ion batteries. The lithium ion battery assembly comprises at least two battery monomers and an ion capacitive film sensor clamped between every two battery monomers, the ion capacitance type thin film sensor comprises an ionic membrane layer and an electrode array layer arranged below the ionic membrane layer, and at least one first electrode unit in the electrode array and the ionic membrane layer are laminated to form at least one temperature sensing unit. At least one second electrode unit is in non-press contact with the ionic membrane layer to form at least one pressure sensing unit. The method can improve the calculation accuracy of the expansion pressure of the battery.

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 present technology relates to a method of producing a coated solid electrolyte comprising a metal-based coating deposited on at least a portion of a surface of the solid electrolyte, the method comprising the steps of: (i) depositing a precursor powder of a metal-based coating material on at least a portion of the surface of the solid electrolyte; (ii) subjecting the precursor powder of the metal-based coating material to a rapid heating process to produce a molten metal-based coating material; and (iii) curing the molten metal-based coating material to produce the coated solid state electrolyte. Coated solid state electrolytes obtained by the method and electrochemical cells and batteries comprising the coated solid state electrolytes are also described. For example, the battery pack may be a lithium battery pack or a lithium ion battery pack.

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 fault detection method comprises the following steps: acquiring a plurality of sample data groups and a plurality of corresponding reference voltage data of a normal lithium ion battery; training a deep learning algorithm by applying the sample data to obtain a first mapping relation and a second mapping relation respectively; and calculating a voltage prediction error threshold based on the voltage prediction values of the first mapping relation and the second mapping relation. And acquiring a plurality of test data groups and a plurality of corresponding real voltage data of the to-be-tested lithium ion battery, calculating to obtain a first error, a second error and a voltage prediction error by using the first mapping relation and the second mapping relation, and determining a fault judgment result of the to-be-tested lithium ion battery through a preset logic relation.

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 field of active equalization of a series lithium ion battery pack, in particular to an equalization topological structure of a DC-DC multi-winding flyback battery equalization topological unit. Comprising an inversion unit, a multi-winding transformer and M battery equalization topology units. The voltage of the battery pack is used for supplementing electric energy to the under-voltage battery after passing through the converter, so that the aim of equalization is fulfilled. Compared with the existing flyback equalization technology, the switching frequency is improved to ultrahigh frequency of 10MHz, the equalization response speed is greatly improved, and meanwhile, the soft switching technology is adopted, so that the full-control switches are switched on in a zero-voltage manner, and the diodes are switched off in a zero-current manner. Along with the improvement of the switching frequency, the passive device can be reduced into a small-capacity patch package, for example, a planar air-core transformer can be adopted as a transformer, so that the power density is effectively improved. The circuit has the advantages of simple and reliable structure, fast response capability, few passive devices, small circuit loss, strong expansibility and stable output of direct current.

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 lithium ion battery assembly production line comprises an assembly groove, a transfer mechanism, an overlapping mechanism and a correction mechanism, the transfer mechanism is used for establishing connection between the assembly groove and gluing equipment, and linear driving sliding grooves are formed in the inner walls of the two opposite sides of the assembly groove correspondingly; the linear driving sliding groove is connected with a bottom plate through a first sliding block, a plurality of battery blocks are stacked at the top end of the bottom plate, a penetrating groove is formed in the back of the assembling groove in a penetrating mode, a first hydraulic rod is arranged on one side of the penetrating groove, and the transmission mechanism and the stacking mechanism are connected to the platform. The overlapping mechanism comprises two clamping seats, the two clamping seats are used for clamping and fixing a battery block, a third hydraulic rod is connected between the two clamping seats at the same time, and the same sides of the two clamping seats are connected with a second transverse plate at the same time; the lithium ion battery assembly production line disclosed by the invention has the effect of enhancing the connection tightness of the battery blocks by optimizing the uniform distribution of glue and shortening the transfer path.

The present disclosure relates to a secondary solid-state battery, with a current collector configuration that includes a metal foam sandwiched between two microporous carbon layers. The current collector directly interfaces with both the negative electrode and the solid electrolyte separator. Alternatively, the current collector can comprise an elastomer sandwiched between a pair of metal foil layers, enabling responsive expansion and contraction in accordance with the electrodes' changes during charge-discharge cycles. Another embodiment contemplates a carbon fiber paper current collector situated between two microporous layers.

The present invention discloses a lithium-ion battery micro-short circuit diagnosis method and system based on clustering and charging voltage difference, including: extracting incremental capacity IC from the constant current charging stage, and smoothing the incremental capacity IC using moving average filtering to obtain high-dimensional features that can effectively characterize lithium-ion battery micro-short circuit faults; using principal component analysis PCA to map the high-dimensional features to a two-dimensional plane to obtain the principal components of the incremental capacity; building a fault detection model based on an unsupervised clustering algorithm, using the principal components of the incremental capacity as input, and detecting the monomers with micro-short circuit faults in the lithium-ion battery pack; based on the maximum charging voltage difference between adjacent charge and discharge cycles, calculating the micro-short circuit current and micro-short circuit resistance of the monomer. The present invention does not need to establish a battery model and set a diagnostic threshold in the process of detecting micro-short circuit monomers in a battery pack and calculating their short circuit resistance, thereby avoiding the difficulty of establishing an accurate battery model and determining an accurate diagnostic threshold.

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.