Battery storage has been widely used in integrating large-scale renewable generations and in transport decarbonization. For battery systems to operate safely and reliably, the accuracy of state estimation is extremely crucial in battery management system (BMS). However, due to the complex working conditions and electrochemical reactions, it is difficult to accurately estimate the internal state of batteries. This survey focuses on categorizing and reviewing some of the most recent estimation methods for internal states, including state of charge (SOC), state of health (SOH) and internal temperature, of which internal temperature estimation methods have been rarely reviewed in the existing literature. The main advantages and disadvantages of these methods are compared, with a focus on the new sensing technologies for internal state estimations. Then, the future prospects of battery state estimation technologies are discussed.
AbuEldahabYESaadNHZekryA (2016) Enhancing the design of battery charging controllers for photovoltaic systems. Renewable and Sustainable Energy Reviews58: 646–655.
2.
AleksandraFTsaoMWilliardND, et al. (2017) Preliminary study on integration of fiber optic Bragg grating sensors in Li-ion batteries and in situ strain and temperature monitoring of battery cells. Energies10: 838.
3.
ArabsahnanabadiBTashakorNJavadiA, et al. (2018) Charging techniques in lithium-ion battery charger: Review and new solution. In: Proceedings of the 44th annual conference of the IEEE industrial-electronics-society (IECON), Washington, DC, 21–23 October, pp. 5731–5738. New York: IEEE.
4.
AungHLowKSGohST (2015) State-of-charge estimation of lithium-ion battery using square root spherical unscented Kalman filter (SQRT-UKFST) in nanosatellite. IEEE Transactions on Power Electronics30: 4774–4783.
5.
BaeCJManandharAKieselP, et al. (2016) Monitoring the strain evolution of lithium-ion battery electrodes using an optical fiber Bragg grating sensor. Energy Technology4: 851–855.
6.
BartlettAMarcickiJOnoriS, et al. (2016) Electrochemical model-based state of charge and capacity estimation for a composite electrode lithium-ion battery. IEEE Transactions on Control Systems Technology24: 384–399.
7.
CaiLMengJStroeDI, et al. (2020) Multiobjective optimization of data-driven model for lithium-ion battery SOH estimation with short-term feature. IEEE Transactions on Power Electronics35: 11855–11864.
8.
ChangCWangQJiangJ, et al. (2021) Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm. Journal of Energy Storage38: 102570.
9.
ChaouiHIbe-EkeochaCCGualousH (2017) Aging prediction and state of charge estimation of a LiFePo4 battery using input time-delayed neural networks. Electric Power Systems Research146: 189–197.
10.
CharkhgardMFarrokhiM (2010) State-of-charge estimation for lithium-ion batteries using neural networks and EKF. IEEE Transactions on Industrial Electronics57: 4178–4187.
11.
ChenSCWanCCWangYY (2005) Thermal analysis of lithium-ion batteries. Journal of Power Sources140: 111–124.
12.
ChenSCWangYYWanCC (2006) Thermal analysis of spirally wound lithium batteries. Journal of the Electrochemical Society153: A637–A648.
13.
ChenZZhaoHShuX, et al. (2021) Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive h-infinity filter. Energy228: 120630.
14.
ChouYSHsuNYJengKT, et al. (2016) A novel ultrasonic velocity sensing approach to monitoring state of charge of vanadium redox flow battery. Applied Energy182: 253–259.
15.
CoutoLDSchorschJJobN, et al. (2019) State of health estimation for lithium ion batteries based on an equivalent-hydraulic model: An iron phosphate application. Journal of Energy Storage21: 259–271.
16.
DuXMengJLiuK, et al. (2021) Online identification of lithium-ion battery model parameters with initial value uncertainty and measurement noise. Available at: https://europepmc.org/article/ppr/ppr401323
17.
FanXZhangWZhangC, et al. (2022) SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture. Energy256: 124612.
18.
FengFLuRZhuC (2014) A combined state of charge estimation method for lithium-ion batteries used in a wide ambient temperature range. Energies7: 3004–3032.
19.
FengXLiJOuyangM, et al. (2013) Using probability density function to evaluate the state of health of lithium-ion batteries. Journal of Power Sources232: 209–218.
20.
ForgezCDoDVFriedrichG, et al. (2010) Thermal modeling of a cylindrical LiFePo4/graphite lithium-ion battery. Journal of Power Sources195: 2961–2968.
21.
GabbarHAOthmanAMAbdussamiMR (2021) Review of battery management systems (BMS) development and industrial standards. Technologies9: 28.
22.
GoutamSTimmermansJMOmarN, et al. (2015) Comparative study of surface temperature behavior of commercial Li-ion pouch cells of different chemistries and capacities by infrared thermography. Energies8: 8175–8192.
23.
GuoPChengZYangL (2019) A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction. Journal of Power Sources412: 442–450.
24.
GuoZQiuXHouG, et al. (2014) State of health estimation for lithium ion batteries based on charging curves. Journal of Power Sources249: 457–462.
25.
HeHXiongRPengJ (2016) Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform. Applied Energy162: 1410–1418.
26.
HossainMSLipuMAHannanAH, et al. (2017) Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection. Journal of Renewable and Sustainable Energy9: 064102.
27.
HowDNTHannanMAHossain LipuMS, et al. (2019) State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review. IEEE Access7: 136116–136136.
28.
HuCJainGSchmidtC, et al. (2015) Online estimation of lithium-ion battery capacity using sparse Bayesian learning. Journal of Power Sources289: 105–113.
29.
HuXFengFLiuK, et al. (2019) State estimation for advanced battery management: Key challenges and future trends. Renewable and Sustainable Energy Reviews114: 109334.
30.
HuXLiSEYangY (2016) Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles. IEEE Transactions on Transportation Electrification2: 140–149.
31.
HuXLiSEPengH, et al. (2012) Robustness analysis of state-of-charge estimation methods for two types of Li-ion batteries. Journal of Power Sources217: 209–219.
32.
JinYZhaoZMiaoS, et al. (2021) Explosion hazards study of grid-scale lithium-ion battery energy storage station. Journal of Energy Storage42: 102987.
33.
KangLZhaoXMaJ (2014) A new neural network model for the state-of-charge estimation in the battery degradation process. Applied Energy121: 20–27.
34.
KaurKGargACuiX, et al. (2020) Deep learning networks for capacity estimation for monitoring SOH of Li on batteries for electric vehicles. International Journal of Energy Research45: 3113–3128.
KimTWangYSahinogluZ, et al. (2014) State of charge estimation based on a realtime battery model and iterative smooth variable structure filter. In: Proceedings of the 2014 IEEE innovative smart grid technologies – Asia (ISGT ASIA), Kuala Lumpur, Malaysia, 20–23 May, pp. 132–137. New York: IEEE.
37.
KnoblochAKarpJPlotnikovY, et al. (2017) Novel thin temperature and expansion sensors for Li-ion battery monitoring. In: Proceedings of the 2017 IEEE sensors, Glasgow, 29 October–1 November, pp. 1–3. New York: IEEE.
38.
LeeSKimJLeeJ, et al. (2008) State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge. Journal of Power Sources185: 1367–1373.
39.
LiPZhangZXiongQ, et al. (2020a) State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network. Journal of Power Sources459: 228069.
40.
LiWCaoDJöstD, et al. (2020b) Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries. Applied Energy269: 115104.
41.
LiWFanYRingbeckF, et al. (2020c) Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter. Journal of Power Sources476: 228534.
42.
LiWSenguptaNDechentP, et al. (2021a) Online capacity estimation of lithium-ion batteries with deep long short-term memory networks. Journal of Power Sources482: 228863.
43.
LiYAbdel-MonemMGopalakrishnanR, et al. (2018) A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter. Journal of Power Sources373: 40–53.
44.
LiYChenJLanF (2020d) Enhanced online model identification and state of charge estimation for lithium-ion battery under noise corrupted measurements by bias compensation recursive least squares. Journal of Power Sources456: 227984.
45.
LiYLiKLiuX, et al. (2021b) Lithium-ion battery capacity estimation: A pruned convolutional neural network approach assisted with transfer learning. Applied Energy285: 1164101–11641013.
46.
LiYLiKLiuX, et al. (2022) A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements. Applied Energy325: 119787.
47.
LiW-LNiE-FLiX-H, et al. (2016) Influence of synthesis temperature on electrochemical performance of polyoxomolybdate as cathode material of lithium ion battery. Transactions of Nonferrous Metals Society of China26: 2687–2692.
48.
LimogeDWAnnaswamyAM (2020) An adaptive observer design for real-time parameter estimation in lithium-ion batteries. IEEE Transactions on Control Systems Technology28: 505–520.
49.
LinCMuHXiongR, et al. (2016) A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm. Applied Energy166: 76–83.
50.
LinCTangAXingJ (2017) Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles. Applied Energy: Transformative Innovations for a Sustainable Future – Part II207: 394–404.
51.
LiuCZhuQLiL, et al. (2017) A state of charge estimation method based on H∞ observer for switched systems of lithium-ion nickel–manganese–cobalt batteries. IEEE Transactions on Industrial Electronics64: 8128–8137.
52.
LiuKGaoYZhuC, et al. (2022a) Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries. Control Engineering Practice124: 105176.
53.
LiuKKangLIQiaoP, et al. (2019) A brief review on key technologies in the battery management system of electric vehicles. Frontiers of Mechanical Engineering14: 47–64.
54.
LiuKLiKYangZ, et al. (2017) An advanced lithium-ion battery optimal charging strategy based on a coupled thermoelectric model. Electrochimica Acta225: 330–344.
55.
LiuKShangYOuyangQ, et al. (2021) A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery. IEEE Transactions on Industrial Electronics68: 3170–3180.
56.
LiuKWeiZZhangC, et al. (2022b) Towards long lifetime battery: AI-based manufacturing and management. IEEE/CAA Journal of Automatica Sinica9: 1139–1165.
57.
LiuLWangLYChenZ, et al. (2013) Integrated system identification and state-of-charge estimation of battery systems. IEEE Transactions on Energy Conversion28: 12–23.
58.
LiuYHeYBianH, et al. (2022c) A review of lithium-ion battery state of charge estimation based on deep learning: Directions for improvement and future trends. Journal of Energy Storage52: 104664.
59.
LongBXianWJiangL, et al. (2013) An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries. Microelectronics Reliability53: 821–831.
60.
LowKAungH (2015) Temperature dependent state-of-charge estimation of lithium ion battery using dual spherical unscented Kalman filter. IET Power Electronics8: 2026–2033.
61.
Lvarez AntónJCGarcía NietoPJBlanco ViejoC, et al. (2013) Support vector machines used to estimate the battery state of charge. IEEE Transactions on Power Electronics28: 5919–5926.
62.
MaCT (2020) A novel state of charge estimating scheme based on an air-gap fiber interferometer sensor for the vanadium redox flow battery. Energies13: 291.
63.
MaYWuLGuanY, et al. (2020) The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach. Journal of Power Sources476: 228581.
64.
MengJLuoGGaoF (2016) Lithium polymer battery state-of-charge estimation based on adaptive unscented Kalman filter and support vector machine. IEEE Transactions on Power Electronics31: 2226–2238.
65.
NakamuraHYumotoDOchiY (2003) The application of adaptive digital filter for the internal state estimation of batteries. SICE 2003 Annual Conference2: 2239–2244.
66.
NejadSGladwinDStoneD (2016) A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. Journal of Power Sources316: 183–196.
67.
NgKSMooCSChenYP, et al. (2009) Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy86: 1506–1511.
68.
NovaisSNascimentoMGrandeL, et al. (2016) Internal and external temperature monitoring of a Li-ion battery with fiber Bragg grating sensors. Sensors16: 1394.
69.
PanHLüZLinW, et al. (2017) State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model. Energy138: 764–775.
70.
PanWChenQZhuM, et al. (2020) A data-driven fuzzy information granulation approach for battery state of health forecasting. Journal of Power Sources475: 228716.
71.
Paris Agreement (2015) Paris agreement. In: Report of the conference of the parties to the United Nations Framework Convention on Climate Change (21st Session, 2015: Paris), HeinOnline. Available at: https://unfccc.int/resource/docs/2015/cop21/eng/10.pdf
72.
Pathuri BhuvanaVUnterriederCHuemerM (2013) Battery internal state estimation: A comparative study of non-linear state estimation algorithms. In: Proceedings of the 2013 IEEE vehicle power and propulsion conference (VPPC), Beijing, China, 15–18 October, pp. 1–6. New York: IEEE.
73.
PeiPZhouQWuL, et al. (2020) Capacity estimation for lithium-ion battery using experimental feature interval approach. Energy203: 117778.
74.
PengJZhouXJiaS, et al. (2019) High precision strain monitoring for lithium ion batteries based on fiber Bragg grating sensors. Journal of Power Sources433: 226692.
75.
PetzlMDanzerMA (2013) Advancements in OCV measurement and analysis for lithium-ion batteries. IEEE Transactions on Energy Conversion28: 675–681.
76.
PopVBergveldHDanilovD, et al. (2008) Battery Management Systems: Accurate State-of-Charge Indication for Battery-Powered Applications, vol. 9. Berlin: Springer.
77.
RamadassPHaranBWhiteR, et al. (2003) Mathematical modeling of the capacity fade of Li-ion cells. Journal of Power Sources123: 230–240.
78.
RenteBFabianMVidakovicM, et al. (2020) Lithium-ion battery state-of-charge estimator based on FBG-based strain sensor and employing machine learning. IEEE Sensors Journal21: 1453–1460.
79.
RichardsonRRBirklCROsborneMA, et al. (2019) Gaussian process regression for in situ capacity estimation of lithium-ion batteries. IEEE Transactions on Industrial Informatics15: 127–138.
80.
RichardsonRRIrelandPTHoweyDA (2014) Battery internal temperature estimation by combined impedance and surface temperature measurement. Journal of Power Sources265: 254–261.
81.
RobinsonJBShearingPRBrettDJ (2016) Thermal imaging of electrochemical power systems: A review. Journal of Imaging2: 2.
82.
SchweigerHGObeidiOKomeskerO, et al. (2010) Comparison of several methods for determining the internal resistance of lithium ion cells. Sensors10: 5604–5625.
83.
ShahAShahKShahC, et al. (2022) State of charge, remaining useful life and knee point estimation based on artificial intelligence and machine learning in lithium-ion EV batteries: A comprehensive review. Renewable Energy Focus42: 146–164.
84.
ShenSSadoughiMChenX, et al. (2019) A deep learning method for online capacity estimation of lithium-ion batteries. Journal of Energy Storage25: 1008171–10081713.
85.
ShenSSadoughiMLiM, et al. (2020) Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries. Applied Energy260: 114296.
86.
ShrivastavaPSoonTKIdrisMYIBMekhilefS (2019) Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renewable and Sustainable Energy Reviews113: 109233.
87.
SommerLWKieselPGanguliA, et al. (2015) Fast and slow ion diffusion processes in lithium ion pouch cells during cycling observed with fiber optic strain sensors. Journal of Power Sources296: 46–52.
88.
SpinnerNSFieldCRHammondMH, et al. (2015) Physical and chemical analysis of lithium-ion battery cell-to-cell failure events inside custom fire chamber. Journal of Power Sources279: 713–721.
89.
SrinivasanRSrinivasanL (2015) Graphitic carbon anode temperature excursions reflect crystallographic phase transitions in lithium-ion cells. Journal of Power Sources293: 876–882.
90.
StroeDIKnapVSwierczynskiM, et al. (2017) Operation of a grid-connected lithium-ion battery energy storage system for primary frequency regulation: A battery lifetime perspective. IEEE Transactions on Industry Applications53: 430–438.
91.
SuiXHeSVilsenSB, et al. (2021) A review of non-probabilistic machine learning-based state of health estimation techniques for lithium-ion battery. Applied Energy300: 117346.
92.
TanYZhaoG (2019) Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries. IEEE Transactions on Industrial Electronics67: 8723–8731.
93.
TangXGaoFLiuK, et al. (2022) A balancing current ratio based state-of-health estimation solution for lithium-ion battery pack. IEEE Transactions on Industrial Electronics69: 8055–8065.
94.
TangXLiuKLiuQ, et al. (2021) Comprehensive study and improvement of experimental methods for obtaining referenced battery state-of-power. Journal of Power Sources512: 230462.
95.
TianJXiongRYuQ (2019) Fractional-order model-based incremental capacity analysis for degradation state recognition of lithium-ion batteries. IEEE Transactions on Industrial Electronics66: 1576–1584.
96.
TripathyYMcGordonALowJ, et al. (2017) Internal temperature prediction of lithium-ion cell using differential voltage technique. In: Proceedings of the 2017 IEEE transportation electrification conference and expo (ITEC), Chicago, IL, 22–24 June, pp. 464–467. New York: IEEE.
97.
WangLPanCLiuL, et al. (2016) On-board state of health estimation of LiFePo4 battery pack through differential voltage analysis. Applied Energy168: 465–472.
98.
WangYLiuCPanR, et al. (2017) Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator. Energy121: 739–750.
99.
WeiJDongGChenZ (2018) Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression. IEEE Transactions on Industrial Electronics65: 5634–5643.
100.
WeiZMengSXiongB, et al. (2016) Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer. Applied Energy181: 332–341.
101.
WeiZZouCLengF, et al. (2018) Online model identification and state-of-charge estimate for lithium-ion battery with a recursive total least squares-based observer. IEEE Transactions on Industrial Electronics65: 1336–1346.
102.
WengCFengXSunJ, et al. (2016) State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking. Applied Energy180: 360–368.
103.
WesterhoffUKrokerTKurbachK, et al. (2016) Electrochemical impedance spectroscopy based estimation of the state of charge of lithium-ion batteries. Journal of Energy Storage8: 244–256.
104.
WuJWangYZhangX, et al. (2016) A novel state of health estimation method of Li-ion battery using group method of data handling. Journal of Power Sources327: 457–464.
105.
XingYHeWPechtM, et al. (2014) State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Applied Energy113: 106–115.
106.
XiongRLiLTianJ (2018) Towards a smarter battery management system: A critical review on battery state of health monitoring methods. Journal of Power Sources405: 18–29.
107.
XiongRLiLYuQ (2019) Improved single particle model based state of charge and capacity monitoring of lithium-ion batteries. In: Proceedings of the 2019 IEEE 89th vehicular technology conference (VTC2019-Spring), Kuala Lumpur, Malaysia, 28 April–1 May, pp. 1–5. New York: IEEE.
108.
XuBOudalovAUlbigA, et al. (2018) Modeling of lithium-ion battery degradation for cell life assessment. IEEE Transactions on Smart Grid9: 1131–1140.
109.
YangDZhangXPanR, et al. (2018a) A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve. Journal of Power Sources384: 387–395.
110.
YangFLiWLiC, et al. (2019) State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy175: 66–75.
111.
YangGLeitãoCLiY, et al. (2013) Real-time temperature measurement with fiber Bragg sensors in lithium batteries for safety usage. Measurement46: 3166–3172.
112.
YangJXiaBHuangW, et al. (2018b) Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis. Applied Energy212: 1589–1600.
113.
YangNZhangXLiG (2015) State of charge estimation for pulse discharge of a LiFePo4 battery by a revised Ah counting. Electrochimica Acta151: 63–71.
114.
YangQXuJCaoB, et al. (2017) A simplified fractional order impedance model and parameter identification method for lithium-ion batteries. PLoS ONE12: e0172424.
115.
YangSZhangCJiangJ, et al. (2021) Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications. Journal of Cleaner Production314: 128015.
116.
YouGParkSOhD (2017) Diagnosis of electric vehicle batteries using recurrent neural networks. IEEE Transactions on Industrial Electronics64: 4885–4893.
117.
YuQXiongRYangR, et al. (2019) Online capacity estimation for lithium-ion batteries through joint estimation method. Applied Energy255: 113817.
118.
ZhangCLiKDengJ (2016) Real-time estimation of battery internal temperature based on a simplified thermoelectric model. Journal of Power Sources302: 146–154.
119.
ZhangLLiKDuD, et al. (2020) A sparse learning machine for real-time SOC estimation of Li-ion batteries. IEEE Access8: 156165–156176.
120.
ZhangQZhuTZhangJ, et al. (2013) Micro-fiber-based FBG sensor for simultaneous measurement of vibration and temperature. IEEE Photonics Technology Letters25: 1751–1753.
121.
ZhangYLiuYWangJ, et al. (2022) State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression. Energy239: 121986.
122.
ZhaoFLiYWangX, et al. (2020) Lithium-ion batteries state of charge prediction of electric vehicles using RNNS-CNNS neural networks. IEEE Access8: 98168–98180.
123.
ZhongQZhongFChengJ, et al. (2017) State of charge estimation of lithium-ion batteries using fractional order sliding mode observer. ISA Transactions66: 448–459.
124.
ZhuJGSunZCWeiXZ, et al. (2015) A new lithium-ion battery internal temperature on-line estimate method based on electrochemical impedance spectroscopy measurement. Journal of Power Sources274: 990–1004.
125.
ZhuQLiLHuX, et al. (2017) h∞-based nonlinear observer design for state of charge estimation of lithium-ion battery with polynomial parameters. IEEE Transactions on Vehicular Technology66: 10853–10865.
126.
ZouYHuXMaH, et al. (2015) Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. Journal of Power Sources273: 793–803.
