339 results about "Battery degradation" patented technology

The invention discloses a method for predicting the left capacity and the health status of a storage battery. According to the invention, predicting the left capacity of the battery based on an ampere hour integration method and an open-circuit voltage method is realized, an adjustment is carried out based on discharge currents and ambient temperature, a self correction is carried out in a process of continuous driving and influences from cell agedness on electric capacity prediction are corrected. Based on the working characteristic of a vehicle cell, a cell failure determination mode based on a combination of open-circuit voltage and ampere hour integration is put forward and the trouble of carrying out a check discharge test towards the cell is avoided. Experiments show that in the case of vehicle power storage battery which is not at a float status in a long term, the capacity obtained from the method has a high reliability. The invention is directed to the social background that motor-assisted bicycles are frequently used at present while most users know little about the cell status and has great application significances.

The rechargeable battery abnormality detection apparatus is provided with an internal short circuit detection section (20b) that monitors rechargeable battery (1) voltage change when no charging or discharging takes place, and detects internal short circuit abnormality when battery voltage drop during a predetermined time period exceeds a preset threshold voltage; a degradation appraisal section (20d) that judges the degree of rechargeable battery degradation; and a threshold control section (20c) that incrementally increases the threshold voltage according to the degree of degradation determined by the degradation appraisal section (20d).

A hybrid vehicle battery information display device 1 includes a battery service life display unit 2 which estimates a degree of battery degradation with respect to a useful life of a battery 16 from data on vehicle travel and the like based on battery degradation data measured from a vehicle travel test 19, to display information on a battery service life; a travel fuel consumption display unit 3 which evaluates, from the vehicle travel test 19, a degradation degree of actual travel fuel consumption data due to the battery degradation for each drive pattern based on test travel fuel consumption data set for each drive pattern, to display information on a travel fuel consumption; and a display control unit 4 which controls the display of information on the battery service life.

The rechargeable battery abnormality detection apparatus is provided with an internal short circuit detection section (20b) that monitors rechargeable battery (1) voltage change when no charging or discharging takes place, and detects internal short circuit abnormality when battery voltage drop during a predetermined time period exceeds a preset threshold voltage; a degradation appraisal section (20d) that judges the degree of rechargeable battery degradation; and a threshold control section (20c) that incrementally increases the threshold voltage according to the degree of degradation determined by the degradation appraisal section (20d).

Systems and methods for communicating notice to limit battery degradation within a battery pack are disclosed. In one example, a signal is generated and passed between modules of a battery pack to provide notice to limit battery pack degradation. The system and method may be particularly useful for a battery pack with distributed battery modules.

The invention relates to a screening grouping method of echelon utilization type lithium batteries, and belongs to the technical field of determination of parameters of the lithium batteries. According to the technical scheme, the screening grouping method of the echelon utilization type lithium batteries comprises the following steps: testing and calculating so as to obtain a battery dQ / dV-V curve, analyzing ageing reasons of return power batteries by adopting an ICA (Incremental Capacity Analysis) method, and firstly rejecting the return power batteries, negative active materials of which are lost; testing to obtain the capacities, ohmic internal resistance, polarization internal resistancs and self-discharge values of the echelon utilization type lithium batteries, and screening the batteries according to indexes; and regrouping the screened batteries. The screening grouping method of the echelon utilization type lithium batteries has the beneficial effects that serious declining batteries are rejected by screening the echelon utilization type lithium batteries so as to ensure that the echelon utilization grouping property of the batteries is not affected by individual damaged battery; the batteries which are similar in terms of capacity, ohmic internal resistance and polarization internal resistance are clustered together by using a weighting k-means clustering method, and due to the setting of weights, the capacity utilization rate of a battery group is ensured; the forming consistency of the echelon utilization type lithium batteries can be improved, and the service life of the echelon utilization type lithium batteries is prolonged.

The invention relates to a battery degradation state model-based lithium ion battery cycle life prediction method, aiming at solving the problem of difficulty in modeling existing in the conventional lithium ion battery cycle life prediction process. The battery degradation state model-based lithium ion battery cycle life prediction method comprises the following steps: 1, acquiring battery monitoring data and preprocessing the data; 2, obtaining a battery degradation state model according to battery degradation state model training; and 3, predicting the lithium ion battery cycle life according to the battery degradation state model obtained by the step 2 to obtain a lithium ion battery cycle life value so as to realize battery degradation state model-based lithium ion battery cycle life prediction. The battery degradation state model-based lithium ion battery cycle life prediction method is suitable for the field of batteries.

A method of detecting battery degradation level detects charging and discharging current flow in the battery and determines battery degradation level (state of health, SOH) from the charging and discharging current. The method detects the battery degradation level (SOH) from the root-mean-square of the charging and discharging current flow (Irms) in the battery, which is designated effective current.

A method and apparatus is used to confirm the charge amount and degradation state of a battery. Various cycle test battery measurements are conducted at prescribed time intervals until the end of the battery life, and the measured values are used to generate a determination table or determination tables showing relationships between battery charge amount and degradation state. To establish the charge amount and degradation state of a subject battery, the subject battery is measured and the results compared with determination table values. The existing charge amount and the state of battery degradation are estimated in accordance with a determination table location of matching values.

The method of detecting battery degradation level detects the change in residual capacity (ΔSOC) for one charging period or one discharging period of a battery 1 being charged and discharged. Weighting factors that establish degradation level according to the value of ΔSOC are pre-stored in memory, and based on the stored data; weighting factors are determined from the detected ΔSOC to determine the battery 1 degradation level from the weighting factors.

A hybrid vehicle battery information display device 1 includes a battery service life display unit 2 which estimates a degree of battery degradation with respect to a useful life of a battery 16 from data on vehicle travel and the like based on battery degradation data measured from a vehicle travel test 19, to display information on a battery service life; a travel fuel consumption display unit 3 which evaluates, from the vehicle travel test 19, a degradation degree of actual travel fuel consumption data due to the battery degradation for each drive pattern based on test travel fuel consumption data set for each drive pattern, to display information on a travel fuel consumption; and a display control unit 4 which controls the display of information on the battery service life.

A battery management system is provided. The battery management system includes a memory for storing program code. The battery management system further includes a processor for running the program code to extract features from battery operation data. The processor further runs the program code to train a deep learning model to model a battery degradation process of a battery using the extracted features. The processor also runs the program code to generate, using the deep learning model, a prediction of a battery capacity degradation based on the battery operation data and a current battery capacity of the battery. The processor additionally runs the program code to control an operation of the battery responsive to the prediction of the battery capacity degradation.

A method for determining the maximum capacity a battery to store charge for the benefit of state of charge and state of health determinations, otherwise known as the maximum capacity estimator, is described. In an embodiment, a memory storage unit is used to collect input data from a battery or battery pack over the life cycle of the battery. As the battery operates, discharge cycles are analyzed to determine the similarity between different cycles throughout the operational phase. The maximum capacity at a given time for storing charge is then determined by comparing the trend of capacity loss in similar cycles and then applying that trend to the reduction of the maximum capacity of the battery. This method allows state of health and state of charge measurements to be made and updated with respect to battery degradation without the need for scheduled maintenance checks-ups such as mandatory discharge cycles or impedance / resistance measurements.

A method and apparatus for estimating a battery life are provided. The apparatus includes a battery information collector, a model determiner, and a battery life estimator. The battery information collector is configured to collect battery information from a battery. The model determiner is configured to select a battery degradation model representing pattern information corresponding to the battery information. The battery life estimator is configured to estimate battery life information based on the selected battery degradation model.

The invention relates to a hybrid electric vehicle reinforcement learning energy management control method. According to the hybrid electric vehicle reinforcement learning energy management control method, by combining an equivalent consumption minimization strategy and a reinforcement learning algorithm and optimizing fuel consumption and battery capacity degradation, a target of optimal use cost of a whole hybrid electric vehicle is achieved. The hybrid electric vehicle reinforcement learning energy management control method comprises the following steps of 1) collecting vehicle state and battery state data; 2) establishing an ICE (Internal Combustion Engine) model, an EM (Electric Motor) model and a transmission system model of the hybrid electric vehicle, a battery internal resistance model and a battery degradation model; and 3) combining the equivalent consumption minimization strategy and the reinforcement learning algorithm, establishing a hybrid electric vehicle energy management strategy, calculating a multi-target optimization problem, and generating a control signal according to a calculation result to distribute engine power and motor power.

Systems and methods for improving operation of a hybrid vehicle are presented. In one example, driveline braking may transition from regenerative braking to engine braking to reduce the possibility of battery degradation.

A control apparatus for a hybrid vehicle includes a drive mode control portion and a battery degradation determination portion. The chive mode control portion sets the drive mode to a CD mode after the charging of a battery by a battery charger ends as the state of charge of the battery reaches a first predetermined range. The drive mode control portion switches the drive mode to a CS mode if, after the hybrid vehicle starts driving in the CD mode, the state of charge of the battery declines to a second predetermined range that is lower than the first predetermined range. The battery degradation determination portion performs determination regarding degradation of the battery on the basis of data collected while the vehicle is driving.

A method for reducing moisture in a battery enclosure is presented. The method may reduce battery degradation during some conditions. In one example, moisture is controlled in a battery enclosure by a desiccant material. In another example, moisture is controlled in a battery enclosure by periodically passing electrical current through a Peltier device.

The invention relates to a vehicle battery state of health (SOH) estimation method based on data driving, and belongs to the technical field of battery management. The method comprises the following steps: selecting a to-be-tested battery, and collecting and arranging technical parameters of the battery; carrying out a cyclic aging experiment of the battery according to the vehicle charging working condition and the constant current or dynamic discharging working condition, and establishing a battery aging database according to the voltage, current, temperature and other information of the battery; extracting health factors of other batteries according to the screened subsets, and estimating SOH estimation verification of the batteries under other different discharge working conditions through the trained regression model; and embedding the trained model into a vehicle battery management system, extracting health factors covered by the subset from the vehicle, and performing SOH estimation. According to the method, the optimal feature subset is selected for model training by using a fusion method, so that the calculation amount can be effectively reduced, the model precision is improved, and reference is provided for actual vehicles.

An apparatus and method for estimating state information of a battery are provided. The apparatus may estimate the state information from information, obtained by classifying and preprocessing battery information measured by a battery management system (BMS), using a pre-trained battery degradation model.

Rechargeable batteries, charging methods and systems for fast charging of the battery under all environmental temperatures and without causing battery degradation are disclosed.

The invention discloses a storage battery residual electric quantity detection method. The method comprises the following steps (1) conducting initializing; (2) judging whether a storage battery is in a charging state or a discharging state currently, conducting charging state electric quantity detection if the storage battery is in the charging state, and conducting discharging state electric quantity detection if the storage battery is in the discharging state; (3) dynamically setting the sampling frequency, and executing the step (2) again. According to the storage battery residual electric quantity detection method, on the premise that the hardware cost is not increased, the storage battery electric quantity detection result becomes quite accurate, the problem that a user of charging equipment cannot accurately know the equipment residual electric quantity is solved, the capacity of automatically correcting an accumulated error in the using process is achieved, the storage battery residual electric quantity detection method can automatically adapt to the influences of storage battery aging and environment temperature changes on the electricity storage capacity and the discharging capacity of the storage battery, the working frequency can be automatically adjusted so as to adapt to the changing working condition of the storage battery, frequency conversion sampling is achieved, and the storage battery residual electric quantity detection method can be widely applied to charging equipment for a smart phone, a tablet personal computer, a portable computer, an electric car and the like.

This invention provides a battery degradation determining apparatus capable of deduce the capacity of a storage battery. In the battery degradation determination method, the battery degradation state of the battery to be measured is determined. The apparatus reduces the voltage output from a rectifier supplying power to a load, measures current flowing from the rectifier for connecting the battery to be measured to the output side in parallel and the battery to the load connected to the output of the rectifier in parallel and a dummy load device connected to the output of the rectifier in parallel separately from the load; controls the load of the dummy load device, such that the measured current is a predetermined current value; and calculates the remaining capacity of the battery, based on the discharge time of the discharge current of discharging the battery to the load and the dummy load device and the terminal voltage of the battery, corresponding to the discharge current at the discharge time, by referring to a battery data storage section for storing battery data indicating the relation among the discharge time, the terminal voltage, and the residual capacity at a predetermined current of the battery to be measured.

The invention discloses an aging early warning method of a power lithium battery. A power type MOSFET and a current limit resistor are connected with two ends of a battery pack, the MOSFET tube is conducted by 2ms under the control action of a controller, a single pulse current with width of 2ms is generated in the battery and the current limit resistor, and internal ohmic resistance of the lithium battery can be acquired according to voltage difference and a resistance value of the current limit resistor. Abnormal value elimination and data smooth processing for the power lithium battery ohminternal resistance data acquired in real time can be carried out, integral data is selected to carry out linear trend fitting of acquired gradient contrast to determine an aging inflection point, andearly warning of the battery health state is carried out. The method is advantaged in that internal ohmic resistance measurement of the power lithium battery can be simply and rapidly carried out, aging early warning of the power lithium battery is realized through utilizing the measured internal ohmic resistance data, and the method is of great importance to facilitating healthy development of the electric automobile industry.

The invention discloses a battery pack capacity estimation method considering parallel battery difference. The method comprises steps that S1, based on the Kirchhoff's voltage law, a battery charging voltage expression is established; S2, a mathematics change method is utilized to make an aged charging voltage-capacity curve and a standard curve superpose; the aged charging voltage-capacity curve is made to realize vertical translation for deltaU to make the aged charging voltage-capacity curve and the standard curve superpose at a charging platform period, the battery aging curve after vertical translation is transversely stretched for k times and is then made to realize vertical translation to make the two curves superpose, and capacity of the single battery after aging can be expressed as C2=C1 / k; S3, according to the bucket effect principle, battery pack capacity is expressed as Cpack=min(Ci)=min(Cri)+Cc; and S4, on the hypothesis that parameters of single batteries of normal single batteries are respectively consistent, the battery pack capacity is estimated through utilizing capacity of the normal single battery, Cpack=C0*SOClimit_V. The method is advantaged in that influence of parallel single battery difference on single battery capacity is considered, and safety of each single battery is guaranteed.

A hybrid vehicle battery information display device (1) includes: battery service life display means (2) for estimating the battery degradation degree based on the service life of a battery (16) from the vehicle travel data according to the battery degradation data measured by a vehicle travel test (19) and displaying the information on the battery service life; travel fuel consumption display means (3) for evaluating lowering degree of the actual travel fuel consumption data based on the battery degradation for each operation pattern according to the test travel fuel consumption data set for each operation pattern from the vehicle travel test (19) and displaying information on the travel fuel consumption; and display control means (4) for controlling and displaying information on the battery service life.

A battery controller and method for controlling a battery include training parameters for a battery capacity prediction model based on usage pattern information that correlates usage of similar batteries with capacity information for the respective similar batteries. The model characterizes a capacity decay rate in accordance with a present value of the battery capacity. Future battery capacity is predicted for a battery under control based on the battery capacity prediction model and a present value of the battery capacity. One or more operational parameters of the battery under control are controlled based on the predicted future battery capacity to extend the battery's usable lifetime.

An apparatus for estimating a degree of aging of a secondary battery is configured to: determine a current and a temperature of the secondary battery; determine a state of charge from the current of the secondary battery; determine a degree of calendar aging by applying a cumulative degree-of-aging model to a degree-of-calendar-aging profile corresponding to the determined state of charge and the determined temperature while the secondary battery is in a calendar state; determine a degree of cycle aging by applying the cumulative degree-of-aging model to a degree-of-cycle-aging profile corresponding to the state of charge, the temperature, and the current of the secondary battery while the secondary battery is in a cycle state; and determine, as the degree of aging of the secondary battery, a weighted average value calculated for the determined degree of calendar aging and the determined degree of cycle aging based on calendar time and cycle time.

The invention relates to a hybrid vehicle team synergetic energy management method based on model prediction control, and belongs to the field of the new-energy vehicles. The method comprises the following steps: S1, forming an internet of vehicles network architecture taking traffic lights as nodes by using the vehicle-vehicle communication and vehicle-road communication layer, thereby forming anupper framework; S2, selecting weight w1(t) and w3(t), and w2(t) and w4(t) in a cost function; S3, establishing a HEV model and a battery state of health(SOH) model in parallel; S4, determining the cost function and considering a battery ageing model; and S5, placing a result obtained through an upper controller in a lower controller, solving a multi-target optimization problem through an MPC, and finding the optimal torque distribution rate. The algorithm disclosed by the invention is low in algorithm complexity and good in feasibility; the battery ageing SOH model is considered in the costfunction, the new thought is provided for the energy management, and the more-fined energy management policy of the intelligent networked vehicle can be further realized.

A hybrid vehicle battery life evaluating apparatus 1 includes an actual traveling measuring section 2 measuring, for each driving pattern, actual gasoline mileage data on a hybrid vehicle equipped with a battery as a power source, a test traveling storage section 3a storing test gasoline mileage data set for each driving pattern on the basis of a traveling test on the vehicle, a battery life evaluating section 5 comparing the measured actual gasoline mileage data with the test gasoline mileage data on the corresponding driving pattern to evaluate a degradation level of the battery on the basis of a value of a decrease in gasoline mileage set for each degradation level of the battery and transmitting information on battery degradation to a battery life display section 6, and the battery life display section 6 displaying the information on battery degradation.