As a professional in the field of new energy industry analysis, the next day will be based on an in-depth analysis of the new energy power battery field, sharing some basic knowledge of electric vehicle technology to everyone, and truly understand the essential technology of the industry. The SOC analysis of the power battery management system is based on the fact that SOC is the core of BMS, BMS is the core of power battery, and power battery is the core of new energy vehicle. SOC is very important for new energy vehicles; Because the new energy vehicle is too large, it is difficult to say deep, saying that the novel is better controlled and deeper. The SOC is the abbreviation of the current power battery capacity/capacity. The car knows the current state of power through the SOC. Through the SOC, we turn the comprehensive influencing factors into a macro system concept. One: analysis of the status quo If there is no accurate SOC, what will happen: 1, overcharge / over discharge situation, resulting in shortened battery life, armpits, etc.; 2, the consistency of the consistency effect is not ideal, reduce the output power, reduce the power performance; 3, in order to avoid the armpit, set too much redundant power, reduce the overall energy output; Therefore, the accurate estimation of SOC is of great significance. For the owner, the SOC directly reflects the current state of charge, and how far it can travel to ensure smooth arrival at the destination. For the battery itself, the accurate estimation of the SOC is involved. Non-linear effects of open circuit voltage, instantaneous current, charge and discharge rate, ambient temperature, battery temperature, parking time, self-discharge rate, Coulomb efficiency, resistance characteristics, SOC initial value, DOD, etc., and these external characteristics affect each other, Due to the influence of different materials and different processes, it is very important to accurately estimate the SOC value. The algorithm is also one of the core competitiveness of related companies. Next we will discuss the current state of SOC algorithm, in-depth analysis of its influencing factors and practical issues. Second: the current state of the algorithm At present, the mainstream estimation methods of SOC include discharge method, ampere-time integration method, open circuit voltage method, neural network method and Kalman filter method. â– Discharge method is to discharge the battery as a battery test, in order to release the amount of electricity for the battery capacity, but the actual amount of electricity remaining in the driving situation is used to drive, can not simply use the discharge results as a power estimation standard. â– The ampere-time integration method calculates the current power by the sum of the current and time integrals in the initial and working conditions. The current SOC accuracy mainly depends on the accuracy of the initial and instantaneous currents, but as time passes, the error accumulates seriously and cannot be corrected separately. . â– The open circuit voltage method is calculated based on the correspondence between the static open circuit voltage and the SOC of the battery of different material systems and processes. However, the accurate open circuit voltage needs to be statically restored for a period of time, because the charging and discharging process will cause the internal chemical reaction of the battery to continue for a period of time, prolong the partial polarization state, form a polarization potential, increase and decrease the instantaneous open circuit voltage, and make the simple open circuit voltage. It is not accurate to be disturbed by driving in the actual working condition. Therefore, the open circuit voltage measured under the working condition can only be used as a reference, and is not a true open circuit voltage. â– The neural network method forms the input layer from various instantaneous data such as local voltage, current, temperature, internal resistance, etc., and automatically summarizes the rules into hidden layers, and then forms the instantaneous SOC through the convergence and optimization of the output layer of the system model. The information of each layer is not communicated or connected with each other. However, the convergence, optimization and modeling techniques of commercial standards have not been solved yet. The cost is high and the stability is poor. The technology is still in the research stage. â– Kalman filtering is a digital filtering algorithm based on minimum mean square error proposed by HRK's REKalman in 1960 for optimal estimation of dynamic system states. The advantage is that the initial error of the pair has a strong correction effect. The disadvantage is that it requires a strong data processing capability, and the accuracy is determined by the battery model. The current research is very hot. In summary, the neural network method is too difficult. There are many researches on the Kalman filter method, but the actual technical operation data is not known. The discharge method cannot be practically used. The error of the integration time and the open circuit voltage method is very large. At present, the mainstream method is the combination of Anshi integral and open circuit voltage method, which is relatively easy to practice. The passenger car error of Huizhou Yineng, Ke Lie and CATL can be realized within 5%. There are also many influencing factors influencing the factors of the Anshi integral method and the open circuit voltage method. The analysis of these factors is very necessary for us to understand the battery characteristics in depth, and can also improve and improve the development direction of SOC accuracy through analysis. n20 motor,n20 gear motor,n20 micro gear motor Shenzhen Maintex Intelligent Control Co., Ltd. , https://www.maintexmotor.com
