With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

A GRU-RNN based momentum optimized algorithm for SOC estimation

The evolving global landscape for electrical distribution and use created a need area for energy storage systems (ESS), making them among the fastest growing electrical power system products. A key element in any energy storage system is the capability to monitor, control, and optimize performance of an individual or multiple battery modules in an energy storage system and the ability to control the disconnection of the module(s) from the system in the event of abnormal conditions. This management scheme is known as “battery management system (BMS)”, which is one of the essential units in electrical equipment. BMS reacts with external events, as well with as an internal event. It is used to improve the battery performance with proper safety measures within a system. Therefore, a safe BMS is the prerequisite for operating an electrical system. This report analyzes the details of BMS for electric transportation and large-scale (stationary) energy storage. The analysis includes different aspects of BMS covering testing, component, functionalities, topology, operation, architecture, and BMS safety aspects. Additionally, current related standards and codes related to BMS are also reviewed. The report investigates BMS safety aspects, battery technology, regulation needs, and offer recommendations. It further studies current gaps in respect to the safety requirements and performance requirements of BMS by focusing mainly on the electric transportation and stationary application. The report further provides a framework for developing a new standard on BMS, especially on BMS safety and operational risk. In conclusion, four main areas of (1) BMS construction, (2) Operation Parameters, (3) BMS Integration, and (4) Installation for improvement of BMS safety and performance are identified, and detailed recommendations were provided for each area. It is recommended that a technical review of the BMS be performed for transportation electrification and large-scale (stationary) applications. A comprehensive evaluation of the components, architectures, and safety risks applicable to BMS operation is also presented.

Review of Battery Management Systems (BMS) Development and Industrial Standards

A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator

Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter

The model predictive direct power control (MPDPC) has attracted significant attention due to its outstanding dynamic response and high power factor. However, the variable switching frequency makes the design of alternating current (ac) filter more challenging, and the heavy computational burden limits the application of MPDPC. This paper proposed a new cost function and four steps’ MPDPC (FSMPDPC) scheme for T-type inverters. The proposed cost function can reduce the number of division operations and does not require calculating the duty cycles of all vectors. Meanwhile, the four steps’ calculation process is divided into four steps to reduce the number of cycle calculations. The first three steps are assigned to adjust the active and reactive powers, and the neutral-point (NP) voltage is balanced in the fourth step. An experimental platform of a T-type inverter is established to demonstrate the superiorities of the proposed FSMPDPC. The results show that FSMPDPC improves the steady-state performance of the T-type inverter with lower current total harmonic distortion (THD) and lower ripples in the active and reactive powers. In addition, the proposed algorithm eases the computational burden of the digital signal processor (DSP).

Model Predictive Direct Power Control With Fixed Switching Frequency and Computational Amount Reduction

In a battery management system (BMS), battery equalizer is used to achieve voltage consistency between series connected battery cells. Recently, serious inconsistency has been founded to exist in retired batteries, and traditional equalizers are slow or inefficient to handle the situation. The multicell-to-multicell (MC2MC) topology, which can directly transfer energy from consecutive strong cells to consecutive weak cells, is promising to solve the problem, but its performance is limited by the existing converter. Therefore, this paper proposes an enhanced MC2MC equalizer based on a novel bipolar-resonant LC converter (BRLCC), which supports flexible and efficient operation modes with stable balancing power, can greatly improve the balancing speed without much sacrificing the efficiency. Mathematical analysis and comparison with typical equalizers are provided to illustrate its high balancing speed and good efficiency. An experimental prototype for 8 cells is built, and the balancing powers under different operation modes are from 1.426 W to 12.559 W with balancing efficiencies from 84.84% to 91.68%.

An Enhanced Multicell-to-Multicell Battery Equalizer Based on Bipolar-Resonant LC Converter

Model predictive power control (MPPC) has been receiving plenty of concern due to its fast dynamic response. However, the varying switching frequency makes the harmonics of output voltage vary over a wide scope, which adds difficulty in designing alternating current (ac) filter. Here, on the basis of the active and reactive power derivatives of each vector and the effect of switching states on neutral point (NP) voltage, we present an MPPC with dual vectors (MPPCDV) to control the three-level (3L) inverter. In this algorithm, a new switching sequence is provided to balance the NP voltage of 3L inverter, and two cost functions are derived to reduce the number of cycle computation. According to the first cost functions, the sector containing optimal switching sequence (OSS) is obtained. The second cost function is assigned to achieve the multiobjective optimization. Simulations and experiments are built to test the effectiveness of MPPCDV. When the 3L inverter adopts the MPPCDV, the majority of the harmonic frequencies in the output voltage is concentrated near the sample frequency, the steady-state performances are improved remarkably, and the dynamic performance is as fast as the conventional MPPC.

Model Predictive Power Control With Dual Vectors for Three-Level Inverter

Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The voltage and current data of the LIB during the constant current (CC) charging process are used to convert into IC curves. Taking into account the incompleteness of the actual charging process, this paper divides the IC curve into multiple voltage segments for SOH prediction. Corresponding BP neural network is established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out the aging experiment under different discharge conditions. Aging experiment data are used to establish the non-linear relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the maximum mean absolute error does not exceed 2%. The SOH estimation method proposed in this research makes it possible to embed the SOH estimation function in battery management system (BMS), and can realize high-precision SOH online estimation. 

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Longyun Kang

Papers

Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter

Model Predictive Direct Power Control With Fixed Switching Frequency and Computational Amount Reduction

An Enhanced Multicell-to-Multicell Battery Equalizer Based on Bipolar-Resonant LC Converter

Model Predictive Power Control With Dual Vectors for Three-Level Inverter

Online State-of-Health Estimation of Lithium-Ion Battery Based on Incremental Capacity Curve and BP Neural Network